From 69b8beacb51154ea6c71da5d54a20010beba5256 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Georg=20=C2=B4Brantegger?= Date: Tue, 12 Jul 2022 08:48:00 +0200 Subject: [PATCH 01/12] added outflux velocity to the ausgleichsbecken class --- Ausgleichsbecken/Ausgleichsbecken_class_file.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Ausgleichsbecken/Ausgleichsbecken_class_file.py b/Ausgleichsbecken/Ausgleichsbecken_class_file.py index eb01f6b..8ba924b 100644 --- a/Ausgleichsbecken/Ausgleichsbecken_class_file.py +++ b/Ausgleichsbecken/Ausgleichsbecken_class_file.py @@ -58,11 +58,11 @@ class Ausgleichsbecken_class: self.set_volume() def set_influx(self,influx): - self.influx = influx + self.influx = influx def set_outflux(self,outflux): self.outflux = outflux - self.outflux_vel = outflux/self.area_outflux + self.outflux_vel = outflux/self.area_outflux def set_pressure(self,pressure,pressure_unit,display_pressure_unit): self.pressure = pressure From 04819d2e6834642a0f73af4776b2de17178c65f3 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Georg=20=C2=B4Brantegger?= Date: Fri, 15 Jul 2022 12:01:22 +0200 Subject: [PATCH 02/12] started implementing turbine and controller code --- .../Ausgleichsbecken_class_file.py | 5 +- Ausgleichsbecken/Main_Program.ipynb | 4 +- Main_Programm.ipynb | 17 +- Main_Programm_demo.ipynb | 10351 ---------------- Regler/Regler_class_file.py | 35 + Regler/regler_test.ipynb | 148 + Turbinen/Turbinen_class_file.py | 19 + Turbinen/messy.ipynb | 174 + 8 files changed, 392 insertions(+), 10361 deletions(-) delete mode 100644 Main_Programm_demo.ipynb create mode 100644 Regler/Regler_class_file.py create mode 100644 Regler/regler_test.ipynb create mode 100644 Turbinen/Turbinen_class_file.py create mode 100644 Turbinen/messy.ipynb diff --git a/Ausgleichsbecken/Ausgleichsbecken_class_file.py b/Ausgleichsbecken/Ausgleichsbecken_class_file.py index 8ba924b..85f0c94 100644 --- a/Ausgleichsbecken/Ausgleichsbecken_class_file.py +++ b/Ausgleichsbecken/Ausgleichsbecken_class_file.py @@ -1,5 +1,4 @@ import numpy as np -# from Ausgleichsbecken_functions import FODE_function, get_h_halfstep, get_p_halfstep #importing pressure conversion function import sys @@ -86,7 +85,7 @@ class Ausgleichsbecken_class: f"Volume in reservoir = {self.volume:<10} {self.volume_unit_print} {new_line}" f"Current influx = {self.influx:<10} {self.flux_unit_print} {new_line}" f"Current outflux = {self.outflux:<10} {self.flux_unit_print} {new_line}" - f"Current outflux vel = {self.outflux_vel:<10} {self.velocity_unit_print} {new_line}" + f"Current outflux vel = {round(self.outflux_vel,3):<10} {self.velocity_unit_print} {new_line}" f"Current pipe pressure = {round(p,3):<10} {self.pressure_unit_print} {new_line}" f"Simulation timestep = {self.timestep:<10} {self.time_unit_print} {new_line}" f"----------------------------- {new_line}") @@ -98,7 +97,7 @@ class Ausgleichsbecken_class: f"Volume in reservoir = {self.volume:<10} {self.volume_unit_print} {new_line}" f"Current influx = {self.influx:<10} {self.flux_unit_print} {new_line}" f"Current outflux = {self.outflux:<10} {self.flux_unit_print} {new_line}" - f"Current outflux vel = {self.outflux_vel:<10} {self.velocity_unit_print} {new_line}" + f"Current outflux vel = {round(self.outflux_vel,3):<10} {self.velocity_unit_print} {new_line}" f"Current pipe pressure = {round(p,3):<10} {self.pressure_unit_print} {new_line}" f"----------------------------- {new_line}") diff --git a/Ausgleichsbecken/Main_Program.ipynb b/Ausgleichsbecken/Main_Program.ipynb index 9a76e26..5cf46d7 100644 --- a/Ausgleichsbecken/Main_Program.ipynb +++ b/Ausgleichsbecken/Main_Program.ipynb @@ -133,7 +133,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.13 ('Georg_DT_Slot3')", + "display_name": "Python 3.8.13 ('DT_Slot_3')", "language": "python", "name": "python3" }, @@ -152,7 +152,7 @@ "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" + "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48" } } }, diff --git a/Main_Programm.ipynb b/Main_Programm.ipynb index d64c4a0..a537c58 100644 --- a/Main_Programm.ipynb +++ b/Main_Programm.ipynb @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -181,8 +181,15 @@ "# axs1[2].autoscale()\n", "fig1.tight_layout()\n", "fig1.show()\n", - "plt.pause(1)\n", - "\n", + "plt.pause(1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ "# loop through time steps of the pipeline\n", "for it_pipe in range(1,pipe.nt+1):\n", "\n", @@ -273,7 +280,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.13 ('Georg_DT_Slot3')", + "display_name": "Python 3.8.13 ('DT_Slot_3')", "language": "python", "name": "python3" }, @@ -292,7 +299,7 @@ "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" + "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48" } } }, diff --git a/Main_Programm_demo.ipynb b/Main_Programm_demo.ipynb deleted file mode 100644 index 910c925..0000000 --- a/Main_Programm_demo.ipynb +++ /dev/null @@ -1,10351 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [], - "source": [ - "# imports\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from functions.pressure_conversion import pressure_conversion\n", - "from Ausgleichsbecken.Ausgleichsbecken_class_file import Ausgleichsbecken_class\n", - "from Druckrohrleitung.Druckrohrleitung_class_file import Druckrohrleitung_class" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# for demoing I\n", - "# pipeline\n", - "L = 1000. # length of pipeline [m]\n", - "D = 1. # pipe diameter [m]\n", - "h_pipe = 0 # hydraulic head without reservoir [m] \n", - "Q0 = 2. # initial flow in whole pipe [m³/s]\n", - "f_D = 0.05 # Darcy friction factor\n", - "c = 400. # propagation velocity of the pressure wave [m/s]\n", - "n = 100 # number of pipe segments in discretization\n", - "\n", - "# consider prescribing a total simulation time and deducting the number of timesteps from that\n", - "nt = 1000 # number of time steps after initial conditions\n", - "\n", - "# reservoir\n", - "area_base = 1. # total base are of the cuboid reservoir [m²] \n" - ] - }, - { - "cell_type": "code", - "execution_count": 38, - "metadata": {}, - "outputs": [], - "source": [ - "#define constants\n", - "\n", - "# physics\n", - "g = 9.81 # gravitational acceleration [m/s²]\n", - "rho = 1000. # density of water [kg/m³]\n", - "\n", - "\n", - "A_pipe = D**2/4*np.pi # pipeline area\n", - "alpha = np.arcsin(h_pipe/L) # Höhenwinkel der Druckrohrleitung \n", - "# consider replacing Q0 with a vector be be more flexible in initial conditions\n", - "v0 = Q0/A_pipe # initial flow velocity [m/s]\n", - "\n", - "\n", - "# derivatives of the pipeline constants\n", - "dx = L/n # length of each pipe segment\n", - "dt = dx/c # timestep according to method of characterisitics\n", - "nn = n+1 # number of nodes\n", - "initial_level = 20. # water level in upstream reservoir [m]\n", - "p0 = rho*g*initial_level-v0**2*rho/2\n", - "pl_vec = np.arange(0,nn*dx,dx) # pl = pipe-length. position of the nodes on the pipeline\n", - "t_vec = np.arange(0,nt+1)*dt # time vector\n", - "h_vec = np.arange(0,n+1)*h_pipe/n # hydraulic head of pipeline at each node \n", - "v_init = np.full(nn,Q0/A_pipe) # initial velocity distribution in pipeline\n", - "p_init = (rho*g*(initial_level+h_vec)-v_init**2*rho/2)-(f_D*pl_vec/D*rho/2*v_init**2) # ref Wikipedia: Darcy Weisbach\n", - "\n", - "\n", - "# reservoir\n", - "# replace influx by vector\n", - "initial_influx = 1. # initial influx of volume to the reservoir [m³/s]\n", - "initial_outflux = Q0 # initial outflux of volume from the reservoir to the pipeline [m³/s]\n", - "initial_pipeline_pressure = p0 # Initial condition for the static pipeline pressure at the reservoir (= hydrostatic pressure - dynamic pressure) \n", - "initial_pressure_unit = 'Pa' # DO NOT CHANGE! for pressure conversion in print statements and plot labels \n", - "conversion_pressure_unit = 'mWS' # for pressure conversion in print statements and plot labels\n", - "area_outflux = A_pipe # outlfux area of the reservoir, given by pipeline area [m²]\n", - "critical_level_low = 0. # for yet-to-be-implemented warnings[m]\n", - "critical_level_high = np.inf # for yet-to-be-implemented warnings[m]\n", - "\n", - "# make sure e-RK4 method of reservoir has a small enough timestep to avoid runaway numerical error\n", - "nt_eRK4 = 1000 # number of simulation steps of reservoir in between timesteps of pipeline \n", - "simulation_timestep = dt/nt_eRK4\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "metadata": {}, - "outputs": [], - "source": [ - "# create objects\n", - "\n", - "V = Ausgleichsbecken_class(area_base,area_outflux,critical_level_low,critical_level_high,simulation_timestep)\n", - "V.set_initial_level(initial_level) \n", - "V.set_influx(initial_influx)\n", - "V.set_outflux(initial_outflux)\n", - "V.set_pressure(initial_pipeline_pressure,initial_pressure_unit,conversion_pressure_unit)\n", - "\n", - "pipe = Druckrohrleitung_class(L,D,n,alpha,f_D)\n", - "pipe.set_pressure_propagation_velocity(c)\n", - "pipe.set_number_of_timesteps(nt)\n", - "pipe.set_initial_pressure(p_init,initial_pressure_unit,conversion_pressure_unit)\n", - "pipe.set_initial_flow_velocity(v_init)\n", - "\n", - "# display the attributes of the created reservoir and pipeline object\n", - "# V.get_info(full=True)\n", - "# pipe.get_info()" - ] - }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [], - "source": [ - "# initialization for timeloop\n", - "\n", - "# prepare the vectors in which the pressure and velocity distribution in the pipeline from the previous timestep are stored\n", - "v_old = v_init.copy()\n", - "p_old = p_init.copy()\n", - "\n", - "# prepare the vectors in which the temporal evolution of the boundary conditions are stored\n", - " # keep in mind, that the velocity at the turbine and the pressure at the reservoir are set manually and\n", - " # through the time evolution of the reservoir respectively \n", - " # the pressure at the turbine and the velocity at the reservoir are calculated from the method of characteristics\n", - "v_boundary_res = np.empty_like(t_vec)\n", - "v_boundary_tur = np.empty_like(t_vec)\n", - "p_boundary_res = np.empty_like(t_vec)\n", - "p_boundary_tur = np.empty_like(t_vec)\n", - "\n", - "# prepare the vectors that store the temporal evolution of the level in the reservoir\n", - "level_vec = np.full(nt+1,initial_level) # level at the end of each pipeline timestep\n", - "level_vec_2 = np.empty([nt_eRK4]) # level throughout each reservoir timestep-used for plotting and overwritten afterwards\n", - "\n", - "# set the boudary conditions for the first timestep\n", - "v_boundary_res[0] = v_old[0]\n", - "p_boundary_res[0] = p_old[0]\n", - "p_boundary_tur[0] = p_old[-1]\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "outputs": [], - "source": [ - "# for demoing II\n", - "v_boundary_tur[0] = v_old[-1] \n", - "v_boundary_tur[1:] = 0 # instantaneous closing\n", - "\n", - "const = int(np.min([1000,round(nt/1.25)])) \n", - "# v_boundary_tur[0:const] = np.linspace(v_old[-1],0,const) # linear closing\n", - "v_boundary_tur[0:const] = v_old[1]*np.cos(t_vec[0:const]*2*np.pi)**2 # oscillating" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.974978271865538 m\n", - "Volume in reservoir = 19.974978271865538 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.00169633262999 m³/s \n", - "Current outflux vel = 2.5486389272558534 m/s \n", - "Current pipe pressure = 19.676 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.949968556929008 m\n", - "Volume in reservoir = 19.949968556929008 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.001217208519121 m³/s \n", - "Current outflux vel = 2.5480288874910593 m/s \n", - "Current pipe pressure = 19.651 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.924970849334542 m\n", - "Volume in reservoir = 19.924970849334542 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0007383214035395 m³/s \n", - "Current outflux vel = 2.5474191494780363 m/s \n", - "Current pipe pressure = 19.626 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.89998504768254 m\n", - "Volume in reservoir = 19.89998504768254 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.000262721761632 m³/s \n", - "Current outflux vel = 2.546813597206498 m/s \n", - "Current pipe pressure = 19.601 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.87501114628559 m\n", - "Volume in reservoir = 19.87501114628559 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9997873510246518 m³/s \n", - "Current outflux vel = 2.5462083363857646 m/s \n", - "Current pipe pressure = 19.576 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.85004910216928 m\n", - "Volume in reservoir = 19.85004910216928 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.999313706873469 m³/s \n", - "Current outflux vel = 2.5456052739223463 m/s \n", - "Current pipe pressure = 19.552 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.82509890969469 m\n", - "Volume in reservoir = 19.82509890969469 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9988402896967628 m³/s \n", - "Current outflux vel = 2.545002500451807 m/s \n", - "Current pipe pressure = 19.527 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.80016052574463 m\n", - "Volume in reservoir = 19.80016052574463 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9983685998639578 m³/s \n", - "Current outflux vel = 2.54440192630383 m/s \n", - "Current pipe pressure = 19.502 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.775233944729543 m\n", - "Volume in reservoir = 19.775233944729543 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9978971350454424 m³/s \n", - "Current outflux vel = 2.543801638652945 m/s \n", - "Current pipe pressure = 19.477 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.750319123761017 m\n", - "Volume in reservoir = 19.750319123761017 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9974273884534741 m³/s \n", - "Current outflux vel = 2.5432035387160465 m/s \n", - "Current pipe pressure = 19.452 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.7254160572984 m\n", - "Volume in reservoir = 19.7254160572984 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9969578649336561 m³/s \n", - "Current outflux vel = 2.542605722803431 m/s \n", - "Current pipe pressure = 19.428 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.70052470267841 m\n", - "Volume in reservoir = 19.70052470267841 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9964900506433936 m³/s \n", - "Current outflux vel = 2.5420100831494765 m/s \n", - "Current pipe pressure = 19.403 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.67564505440879 m\n", - "Volume in reservoir = 19.67564505440879 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9960224575006373 m³/s \n", - "Current outflux vel = 2.5414147250692722 m/s \n", - "Current pipe pressure = 19.378 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.65077707004995 m\n", - "Volume in reservoir = 19.65077707004995 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9955565646468187 m³/s \n", - "Current outflux vel = 2.540821531864181 m/s \n", - "Current pipe pressure = 19.353 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.62592074415752 m\n", - "Volume in reservoir = 19.62592074415752 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9950908910332417 m³/s \n", - "Current outflux vel = 2.5402286178044347 m/s \n", - "Current pipe pressure = 19.329 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.60107603451429 m\n", - "Volume in reservoir = 19.60107603451429 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.994626908824356 m³/s \n", - "Current outflux vel = 2.539637857308028 m/s \n", - "Current pipe pressure = 19.304 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.57624293572354 m\n", - "Volume in reservoir = 19.57624293572354 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9941631439656233 m³/s \n", - "Current outflux vel = 2.539047373550431 m/s \n", - "Current pipe pressure = 19.279 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.55142140578876 m\n", - "Volume in reservoir = 19.55142140578876 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.993701061683308 m³/s \n", - "Current outflux vel = 2.5384590321156657 m/s \n", - "Current pipe pressure = 19.255 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.52661143936043 m\n", - "Volume in reservoir = 19.52661143936043 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9932391948780834 m³/s \n", - "Current outflux vel = 2.537870965034853 m/s \n", - "Current pipe pressure = 19.23 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.501812994661673 m\n", - "Volume in reservoir = 19.501812994661673 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9927790018765377 m³/s \n", - "Current outflux vel = 2.5372850291070748 m/s \n", - "Current pipe pressure = 19.205 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.477026066389538 m\n", - "Volume in reservoir = 19.477026066389538 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9923190224959164 m³/s \n", - "Current outflux vel = 2.536699365169905 m/s \n", - "Current pipe pressure = 19.181 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.45225061298532 m\n", - "Volume in reservoir = 19.45225061298532 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9918607082013975 m³/s \n", - "Current outflux vel = 2.5361158212862063 m/s \n", - "Current pipe pressure = 19.156 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.427486629192355 m\n", - "Volume in reservoir = 19.427486629192355 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9914026056883436 m³/s \n", - "Current outflux vel = 2.5355325470510435 m/s \n", - "Current pipe pressure = 19.131 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.402734073668746 m\n", - "Volume in reservoir = 19.402734073668746 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.990946159598546 m³/s \n", - "Current outflux vel = 2.5349513818394733 m/s \n", - "Current pipe pressure = 19.107 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.377992941203676 m\n", - "Volume in reservoir = 19.377992941203676 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9904899234673552 m³/s \n", - "Current outflux vel = 2.534370483955504 m/s \n", - "Current pipe pressure = 19.082 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.353263190670713 m\n", - "Volume in reservoir = 19.353263190670713 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9900353351509226 m³/s \n", - "Current outflux vel = 2.5337916841344477 m/s \n", - "Current pipe pressure = 19.058 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.328544816904422 m\n", - "Volume in reservoir = 19.328544816904422 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9895809549866375 m³/s \n", - "Current outflux vel = 2.5332131493409364 m/s \n", - "Current pipe pressure = 19.033 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.303837778992463 m\n", - "Volume in reservoir = 19.303837778992463 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9891282140825686 m³/s \n", - "Current outflux vel = 2.532636701718357 m/s \n", - "Current pipe pressure = 19.008 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.279142071814626 m\n", - "Volume in reservoir = 19.279142071814626 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9886756795404685 m³/s \n", - "Current outflux vel = 2.5320605168439965 m/s \n", - "Current pipe pressure = 18.984 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.254457654671086 m\n", - "Volume in reservoir = 19.254457654671086 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.988224775757627 m³/s \n", - "Current outflux vel = 2.531486408316812 m/s \n", - "Current pipe pressure = 18.959 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.22978452248642 m\n", - "Volume in reservoir = 19.22978452248642 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.987774076562673 m³/s \n", - "Current outflux vel = 2.5309125602790163 m/s \n", - "Current pipe pressure = 18.935 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.20512263477219 m\n", - "Volume in reservoir = 19.20512263477219 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9873249996791944 m³/s \n", - "Current outflux vel = 2.530340777832345 m/s \n", - "Current pipe pressure = 18.91 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.18047198649725 m\n", - "Volume in reservoir = 19.18047198649725 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.986876125625523 m³/s \n", - "Current outflux vel = 2.529769253636605 m/s \n", - "Current pipe pressure = 18.886 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.15583253738329 m\n", - "Volume in reservoir = 19.15583253738329 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9864288654883362 m³/s \n", - "Current outflux vel = 2.529199784343155 m/s \n", - "Current pipe pressure = 18.861 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.131204282442898 m\n", - "Volume in reservoir = 19.131204282442898 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.985981806438686 m³/s \n", - "Current outflux vel = 2.5286305710823087 m/s \n", - "Current pipe pressure = 18.837 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.106587181606734 m\n", - "Volume in reservoir = 19.106587181606734 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9855363529629513 m³/s \n", - "Current outflux vel = 2.5280634021016635 m/s \n", - "Current pipe pressure = 18.812 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.081981229930697 m\n", - "Volume in reservoir = 19.081981229930697 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9850910988481794 m³/s \n", - "Current outflux vel = 2.52749648695528 m/s \n", - "Current pipe pressure = 18.788 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.057386387553073 m\n", - "Volume in reservoir = 19.057386387553073 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9846474420167948 m³/s \n", - "Current outflux vel = 2.526931605533269 m/s \n", - "Current pipe pressure = 18.763 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.032802649572933 m\n", - "Volume in reservoir = 19.032802649572933 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.984203982835341 m³/s \n", - "Current outflux vel = 2.5263669757669662 m/s \n", - "Current pipe pressure = 18.739 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.008229976334537 m\n", - "Volume in reservoir = 19.008229976334537 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.983762112698412 m³/s \n", - "Current outflux vel = 2.5258043692349905 m/s \n", - "Current pipe pressure = 18.714 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.983668362979802 m\n", - "Volume in reservoir = 18.983668362979802 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9833204385157919 m³/s \n", - "Current outflux vel = 2.52524201219979 m/s \n", - "Current pipe pressure = 18.69 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.959117770057937 m\n", - "Volume in reservoir = 18.959117770057937 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9828803451901382 m³/s \n", - "Current outflux vel = 2.524681667974194 m/s \n", - "Current pipe pressure = 18.665 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.93457819275309 m\n", - "Volume in reservoir = 18.93457819275309 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.98244044613843 m³/s \n", - "Current outflux vel = 2.5241215711058675 m/s \n", - "Current pipe pressure = 18.641 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.910049591818222 m\n", - "Volume in reservoir = 18.910049591818222 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9820021198070468 m³/s \n", - "Current outflux vel = 2.5235634766872517 m/s \n", - "Current pipe pressure = 18.617 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.885531962479405 m\n", - "Volume in reservoir = 18.885531962479405 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.981563986084385 m³/s \n", - "Current outflux vel = 2.5230056275056767 m/s \n", - "Current pipe pressure = 18.592 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.861025265692113 m\n", - "Volume in reservoir = 18.861025265692113 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.98112741699599 m³/s \n", - "Current outflux vel = 2.5224497704783233 m/s \n", - "Current pipe pressure = 18.568 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.83652949672376 m\n", - "Volume in reservoir = 18.83652949672376 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.980691038866066 m³/s \n", - "Current outflux vel = 2.5218941565868462 m/s \n", - "Current pipe pressure = 18.543 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.812044616731146 m\n", - "Volume in reservoir = 18.812044616731146 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9802562173345488 m³/s \n", - "Current outflux vel = 2.5213405246180165 m/s \n", - "Current pipe pressure = 18.519 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.787570621022944 m\n", - "Volume in reservoir = 18.787570621022944 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9798215851261247 m³/s \n", - "Current outflux vel = 2.5207871337028354 m/s \n", - "Current pipe pressure = 18.495 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.763107470955735 m\n", - "Volume in reservoir = 18.763107470955735 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9793885015301123 m³/s \n", - "Current outflux vel = 2.520235714542216 m/s \n", - "Current pipe pressure = 18.47 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.738655161879134 m\n", - "Volume in reservoir = 18.738655161879134 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.978955605636532 m³/s \n", - "Current outflux vel = 2.5196845343717564 m/s \n", - "Current pipe pressure = 18.446 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.714213655348296 m\n", - "Volume in reservoir = 18.714213655348296 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.978524250418847 m³/s \n", - "Current outflux vel = 2.5191353158507717 m/s \n", - "Current pipe pressure = 18.422 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.689782946753418 m\n", - "Volume in reservoir = 18.689782946753418 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9780930812975257 m³/s \n", - "Current outflux vel = 2.518586334275037 m/s \n", - "Current pipe pressure = 18.397 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.665362997847005 m\n", - "Volume in reservoir = 18.665362997847005 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9776634449647492 m³/s \n", - "Current outflux vel = 2.5180393043062907 m/s \n", - "Current pipe pressure = 18.373 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.64095380405949 m\n", - "Volume in reservoir = 18.64095380405949 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9772339931367138 m³/s \n", - "Current outflux vel = 2.5174925092562774 m/s \n", - "Current pipe pressure = 18.349 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.61655532733957 m\n", - "Volume in reservoir = 18.61655532733957 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9768060662586864 m³/s \n", - "Current outflux vel = 2.516947655832918 m/s \n", - "Current pipe pressure = 18.325 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.59216756315753 m\n", - "Volume in reservoir = 18.59216756315753 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9763783223081104 m³/s \n", - "Current outflux vel = 2.5164030353200233 m/s \n", - "Current pipe pressure = 18.3 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.56779047365697 m\n", - "Volume in reservoir = 18.56779047365697 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.975952095517447 m³/s \n", - "Current outflux vel = 2.515860346515125 m/s \n", - "Current pipe pressure = 18.276 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.543424054347522 m\n", - "Volume in reservoir = 18.543424054347522 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9755260500911465 m³/s \n", - "Current outflux vel = 2.5153178886305056 m/s \n", - "Current pipe pressure = 18.252 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.519068267566855 m\n", - "Volume in reservoir = 18.519068267566855 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9751015140827664 m³/s \n", - "Current outflux vel = 2.514777352596472 m/s \n", - "Current pipe pressure = 18.228 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.494723108863557 m\n", - "Volume in reservoir = 18.494723108863557 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9746771578897784 m³/s \n", - "Current outflux vel = 2.5142370455105065 m/s \n", - "Current pipe pressure = 18.203 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.470388540767924 m\n", - "Volume in reservoir = 18.470388540767924 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9742543034204865 m³/s \n", - "Current outflux vel = 2.513698650478536 m/s \n", - "Current pipe pressure = 18.179 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.446064558867214 m\n", - "Volume in reservoir = 18.446064558867214 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.973831627231531 m³/s \n", - "Current outflux vel = 2.51316048244014 m/s \n", - "Current pipe pressure = 18.155 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.421751125883226 m\n", - "Volume in reservoir = 18.421751125883226 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9734104451194927 m³/s \n", - "Current outflux vel = 2.5126242167195576 m/s \n", - "Current pipe pressure = 18.131 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.397448237441726 m\n", - "Volume in reservoir = 18.397448237441726 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9729894397665888 m³/s \n", - "Current outflux vel = 2.5120881760556952 m/s \n", - "Current pipe pressure = 18.107 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.373155856454737 m\n", - "Volume in reservoir = 18.373155856454737 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9725699208909249 m³/s \n", - "Current outflux vel = 2.511554028033437 m/s \n", - "Current pipe pressure = 18.082 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.348873978585956 m\n", - "Volume in reservoir = 18.348873978585956 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.972150577266888 m³/s \n", - "Current outflux vel = 2.5110201031484807 m/s \n", - "Current pipe pressure = 18.058 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.324602566936548 m\n", - "Volume in reservoir = 18.324602566936548 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.971732712567208 m³/s \n", - "Current outflux vel = 2.510488061288499 m/s \n", - "Current pipe pressure = 18.034 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.300341617207977 m\n", - "Volume in reservoir = 18.300341617207977 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9713150216252133 m³/s \n", - "Current outflux vel = 2.509956240663674 m/s \n", - "Current pipe pressure = 18.01 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.27609109268933 m\n", - "Volume in reservoir = 18.27609109268933 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.970898802101129 m³/s \n", - "Current outflux vel = 2.509426293506319 m/s \n", - "Current pipe pressure = 17.986 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.251850989119514 m\n", - "Volume in reservoir = 18.251850989119514 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9704827548542743 m³/s \n", - "Current outflux vel = 2.5088965656991453 m/s \n", - "Current pipe pressure = 17.962 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.227621269974343 m\n", - "Volume in reservoir = 18.227621269974343 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9700681715650026 m³/s \n", - "Current outflux vel = 2.5083687018606584 m/s \n", - "Current pipe pressure = 17.938 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.203401931029838 m\n", - "Volume in reservoir = 18.203401931029838 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.969653759085881 m³/s \n", - "Current outflux vel = 2.507841055504409 m/s \n", - "Current pipe pressure = 17.913 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.179192935947476 m\n", - "Volume in reservoir = 18.179192935947476 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9692408031497846 m³/s \n", - "Current outflux vel = 2.507315263676338 m/s \n", - "Current pipe pressure = 17.889 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.154994280539828 m\n", - "Volume in reservoir = 18.154994280539828 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.968828016569967 m³/s \n", - "Current outflux vel = 2.5067896874793782 m/s \n", - "Current pipe pressure = 17.865 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.13080592865311 m\n", - "Volume in reservoir = 18.13080592865311 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9684166791641413 m³/s \n", - "Current outflux vel = 2.5062659564280523 m/s \n", - "Current pipe pressure = 17.841 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.106627876136287 m\n", - "Volume in reservoir = 18.106627876136287 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9680055096738234 m³/s \n", - "Current outflux vel = 2.5057424391733907 m/s \n", - "Current pipe pressure = 17.817 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.082460087018873 m\n", - "Volume in reservoir = 18.082460087018873 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9675957820336178 m³/s \n", - "Current outflux vel = 2.50522075773931 m/s \n", - "Current pipe pressure = 17.793 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.058302557185964 m\n", - "Volume in reservoir = 18.058302557185964 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9671862208811834 m³/s \n", - "Current outflux vel = 2.5046992882840433 m/s \n", - "Current pipe pressure = 17.769 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.034155250849437 m\n", - "Volume in reservoir = 18.034155250849437 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.966778094299812 m³/s \n", - "Current outflux vel = 2.5041796453813836 m/s \n", - "Current pipe pressure = 17.745 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.010018163930066 m\n", - "Volume in reservoir = 18.010018163930066 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9663701327913394 m³/s \n", - "Current outflux vel = 2.5036602126560665 m/s \n", - "Current pipe pressure = 17.721 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.985891260820875 m\n", - "Volume in reservoir = 17.985891260820875 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9659635986194521 m³/s \n", - "Current outflux vel = 2.5031425972721335 m/s \n", - "Current pipe pressure = 17.697 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.961774537478146 m\n", - "Volume in reservoir = 17.961774537478146 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.965557228118372 m³/s \n", - "Current outflux vel = 2.5026251902803445 m/s \n", - "Current pipe pressure = 17.673 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.937667958474858 m\n", - "Volume in reservoir = 17.937667958474858 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9651522777636092 m³/s \n", - "Current outflux vel = 2.502109591475006 m/s \n", - "Current pipe pressure = 17.649 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.913571519802563 m\n", - "Volume in reservoir = 17.913571519802563 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9647474896902397 m³/s \n", - "Current outflux vel = 2.5015941992927546 m/s \n", - "Current pipe pressure = 17.625 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.889485186213268 m\n", - "Volume in reservoir = 17.889485186213268 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9643441146168428 m³/s \n", - "Current outflux vel = 2.501080606197945 m/s \n", - "Current pipe pressure = 17.601 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.865408953733194 m\n", - "Volume in reservoir = 17.865408953733194 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9639409004479738 m³/s \n", - "Current outflux vel = 2.5005672179731437 m/s \n", - "Current pipe pressure = 17.577 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.841342787292284 m\n", - "Volume in reservoir = 17.841342787292284 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9635390921763638 m³/s \n", - "Current outflux vel = 2.5000556197923283 m/s \n", - "Current pipe pressure = 17.553 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.817286682951334 m\n", - "Volume in reservoir = 17.817286682951334 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.963137443444867 m³/s \n", - "Current outflux vel = 2.4995442247442936 m/s \n", - "Current pipe pressure = 17.529 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.79324060581712 m\n", - "Volume in reservoir = 17.79324060581712 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9627371935512288 m³/s \n", - "Current outflux vel = 2.4990346107519374 m/s \n", - "Current pipe pressure = 17.505 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.76920455198461 m\n", - "Volume in reservoir = 17.76920455198461 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9623371018456348 m³/s \n", - "Current outflux vel = 2.4985251981708547 m/s \n", - "Current pipe pressure = 17.481 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.745178486736222 m\n", - "Volume in reservoir = 17.745178486736222 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9619384019615314 m³/s \n", - "Current outflux vel = 2.498017557711933 m/s \n", - "Current pipe pressure = 17.457 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.721162406200946 m\n", - "Volume in reservoir = 17.721162406200946 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9615398589256154 m³/s \n", - "Current outflux vel = 2.497510116958326 m/s \n", - "Current pipe pressure = 17.434 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.69715627583588 m\n", - "Volume in reservoir = 17.69715627583588 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.961142700737555 m³/s \n", - "Current outflux vel = 2.497004439447772 m/s \n", - "Current pipe pressure = 17.41 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.673160091803602 m\n", - "Volume in reservoir = 17.673160091803602 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9607456980699813 m³/s \n", - "Current outflux vel = 2.496498959952052 m/s \n", - "Current pipe pressure = 17.386 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.649173819734877 m\n", - "Volume in reservoir = 17.649173819734877 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.960350073319041 m³/s \n", - "Current outflux vel = 2.495995234874279 m/s \n", - "Current pipe pressure = 17.362 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.625197455825507 m\n", - "Volume in reservoir = 17.625197455825507 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.959954602772906 m³/s \n", - "Current outflux vel = 2.4954917061361614 m/s \n", - "Current pipe pressure = 17.338 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.601230965878933 m\n", - "Volume in reservoir = 17.601230965878933 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9595605032543464 m³/s \n", - "Current outflux vel = 2.4949899230445705 m/s \n", - "Current pipe pressure = 17.314 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.57727434612395 m\n", - "Volume in reservoir = 17.57727434612395 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9591665566368264 m³/s \n", - "Current outflux vel = 2.494488334632629 m/s \n", - "Current pipe pressure = 17.29 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.553327562535383 m\n", - "Volume in reservoir = 17.553327562535383 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9587739741996677 m³/s \n", - "Current outflux vel = 2.4939884831490704 m/s \n", - "Current pipe pressure = 17.266 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.529390611374897 m\n", - "Volume in reservoir = 17.529390611374897 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9583815433716076 m³/s \n", - "Current outflux vel = 2.493488824700211 m/s \n", - "Current pipe pressure = 17.243 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.507549629752713 m\n", - "Volume in reservoir = 17.507549629752713 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8745398238435809 m³/s \n", - "Current outflux vel = 2.386738231898533 m/s \n", - "Current pipe pressure = 17.232 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.49181578544904 m\n", - "Volume in reservoir = 17.49181578544904 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.630237553945902 m³/s \n", - "Current outflux vel = 2.0756829209962455 m/s \n", - "Current pipe pressure = 17.248 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.485715988192347 m\n", - "Volume in reservoir = 17.485715988192347 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.244769356952353 m³/s \n", - "Current outflux vel = 1.5848895693462952 m/s \n", - "Current pipe pressure = 17.297 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.491984812358545 m\n", - "Volume in reservoir = 17.491984812358545 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.7498007325280815 m³/s \n", - "Current outflux vel = 0.9546759433261461 m/s \n", - "Current pipe pressure = 17.375 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.5122840109439 m\n", - "Volume in reservoir = 17.5122840109439 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.18833075698596502 m³/s \n", - "Current outflux vel = 0.23979016728443867 m/s \n", - "Current pipe pressure = 17.456 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.546969341807635 m\n", - "Volume in reservoir = 17.546969341807635 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.38724264942212255 m³/s \n", - "Current outflux vel = -0.49305265465226156 m/s \n", - "Current pipe pressure = 17.516 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.59494898615186 m\n", - "Volume in reservoir = 17.59494898615186 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9187859523542192 m³/s \n", - "Current outflux vel = -1.169834607684549 m/s \n", - "Current pipe pressure = 17.542 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.653691229382567 m\n", - "Volume in reservoir = 17.653691229382567 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3487829619911234 m³/s \n", - "Current outflux vel = -1.7173238044721222 m/s \n", - "Current pipe pressure = 17.538 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.719439273067405 m\n", - "Volume in reservoir = 17.719439273067405 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6284407295811179 m³/s \n", - "Current outflux vel = -2.0733951331600586 m/s \n", - "Current pipe pressure = 17.525 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.787614590358043 m\n", - "Volume in reservoir = 17.787614590358043 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.725133147910366 m³/s \n", - "Current outflux vel = -2.1965077438529326 m/s \n", - "Current pipe pressure = 17.53 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.853353642606965 m\n", - "Volume in reservoir = 17.853353642606965 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6276307609499943 m³/s \n", - "Current outflux vel = -2.072363849068917 m/s \n", - "Current pipe pressure = 17.574 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.912088092494585 m\n", - "Volume in reservoir = 17.912088092494585 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3477655816579102 m³/s \n", - "Current outflux vel = -1.7160284355998392 m/s \n", - "Current pipe pressure = 17.663 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.96006970174912 m\n", - "Volume in reservoir = 17.96006970174912 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9182093657224913 m³/s \n", - "Current outflux vel = -1.1691004747840674 m/s \n", - "Current pipe pressure = 17.783 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.99475786696207 m\n", - "Volume in reservoir = 17.99475786696207 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.38703881095769765 m³/s \n", - "Current outflux vel = -0.4927931194586177 m/s \n", - "Current pipe pressure = 17.903 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.015026606262627 m\n", - "Volume in reservoir = 18.015026606262627 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.1893753655962835 m³/s \n", - "Current outflux vel = 0.24112020427586703 m/s \n", - "Current pipe pressure = 17.99 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.021195898068683 m\n", - "Volume in reservoir = 18.021195898068683 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.7531574169310881 m³/s \n", - "Current outflux vel = 0.9589498066472497 m/s \n", - "Current pipe pressure = 18.018 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.01492166453556 m\n", - "Volume in reservoir = 18.01492166453556 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2508796072863413 m³/s \n", - "Current outflux vel = 1.5926693816997604 m/s \n", - "Current pipe pressure = 17.984 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.998964148493314 m\n", - "Volume in reservoir = 17.998964148493314 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6383679952822465 m³/s \n", - "Current outflux vel = 2.0860349204218287 m/s \n", - "Current pipe pressure = 17.903 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.976888214144036 m\n", - "Volume in reservoir = 17.976888214144036 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8833739789567103 m³/s \n", - "Current outflux vel = 2.397986227532894 m/s \n", - "Current pipe pressure = 17.802 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.9527385585319 m\n", - "Volume in reservoir = 17.9527385585319 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9666057133385686 m³/s \n", - "Current outflux vel = 2.503960163124769 m/s \n", - "Current pipe pressure = 17.714 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.930714120236146 m\n", - "Volume in reservoir = 17.930714120236146 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8817964663643951 m³/s \n", - "Current outflux vel = 2.3959776761180405 m/s \n", - "Current pipe pressure = 17.666 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.914847359743316 m\n", - "Volume in reservoir = 17.914847359743316 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6355415787196612 m³/s \n", - "Current outflux vel = 2.0824362150844506 m/s \n", - "Current pipe pressure = 17.669 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.90868427338204 m\n", - "Volume in reservoir = 17.90868427338204 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2472881819319541 m³/s \n", - "Current outflux vel = 1.58809663691659 m/s \n", - "Current pipe pressure = 17.719 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.914968955697375 m\n", - "Volume in reservoir = 17.914968955697375 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.7491560305044578 m³/s \n", - "Current outflux vel = 0.9538550832150975 m/s \n", - "Current pipe pressure = 17.798 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.935353749533885 m\n", - "Volume in reservoir = 17.935353749533885 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.18490016211009302 m³/s \n", - "Current outflux vel = 0.23542219822651264 m/s \n", - "Current pipe pressure = 17.879 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 17.970167613263218 m\n", - "Volume in reservoir = 17.970167613263218 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.3923872162984452 m³/s \n", - "Current outflux vel = -0.49960292063973016 m/s \n", - "Current pipe pressure = 17.939 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.018284117149168 m\n", - "Volume in reservoir = 18.018284117149168 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9242658852065694 m³/s \n", - "Current outflux vel = -1.1768118748946546 m/s \n", - "Current pipe pressure = 17.965 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.077144005645298 m\n", - "Volume in reservoir = 18.077144005645298 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3535034376823312 m³/s \n", - "Current outflux vel = -1.723334100792129 m/s \n", - "Current pipe pressure = 17.961 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.14298293898903 m\n", - "Volume in reservoir = 18.14298293898903 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6321032810360379 m³/s \n", - "Current outflux vel = -2.07805843850709 m/s \n", - "Current pipe pressure = 17.948 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.211236815663174 m\n", - "Volume in reservoir = 18.211236815663174 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.7283117176824812 m³/s \n", - "Current outflux vel = -2.2005548245824893 m/s \n", - "Current pipe pressure = 17.952 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.277066314116357 m\n", - "Volume in reservoir = 18.277066314116357 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6312857819941062 m³/s \n", - "Current outflux vel = -2.0770175663991197 m/s \n", - "Current pipe pressure = 17.996 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.33591767985748 m\n", - "Volume in reservoir = 18.33591767985748 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.352471555958164 m³/s \n", - "Current outflux vel = -1.7220202681754297 m/s \n", - "Current pipe pressure = 18.086 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.384035399913373 m\n", - "Volume in reservoir = 18.384035399913373 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9236708111089128 m³/s \n", - "Current outflux vel = -1.1760542030214707 m/s \n", - "Current pipe pressure = 18.206 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.418851546527357 m\n", - "Volume in reservoir = 18.418851546527357 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.3921644309801071 m³/s \n", - "Current outflux vel = -0.4993192615624357 m/s \n", - "Current pipe pressure = 18.327 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.439205431498916 m\n", - "Volume in reservoir = 18.439205431498916 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.18596860651674743 m³/s \n", - "Current outflux vel = 0.23678258389641613 m/s \n", - "Current pipe pressure = 18.414 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.445389855517714 m\n", - "Volume in reservoir = 18.445389855517714 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.7525535347788673 m³/s \n", - "Current outflux vel = 0.9581809200106826 m/s \n", - "Current pipe pressure = 18.443 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.439051204968585 m\n", - "Volume in reservoir = 18.439051204968585 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2534566739853041 m³/s \n", - "Current outflux vel = 1.5959506049302998 m/s \n", - "Current pipe pressure = 18.409 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.422959704557346 m\n", - "Volume in reservoir = 18.422959704557346 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6437245650510253 m³/s \n", - "Current outflux vel = 2.0928551168755707 m/s \n", - "Current pipe pressure = 18.326 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.400704594497494 m\n", - "Volume in reservoir = 18.400704594497494 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8905342627707142 m³/s \n", - "Current outflux vel = 2.4071029840364107 m/s \n", - "Current pipe pressure = 18.225 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.37636085413952 m\n", - "Volume in reservoir = 18.37636085413952 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9743587712376995 m³/s \n", - "Current outflux vel = 2.513831663034564 m/s \n", - "Current pipe pressure = 18.136 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.354158635214578 m\n", - "Volume in reservoir = 18.354158635214578 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8888949021392727 m³/s \n", - "Current outflux vel = 2.405015685252377 m/s \n", - "Current pipe pressure = 18.087 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.338160186145018 m\n", - "Volume in reservoir = 18.338160186145018 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6407955516009698 m³/s \n", - "Current outflux vel = 2.089125781123899 m/s \n", - "Current pipe pressure = 18.091 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.331935170949397 m\n", - "Volume in reservoir = 18.331935170949397 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2497528210628057 m³/s \n", - "Current outflux vel = 1.591234712921492 m/s \n", - "Current pipe pressure = 18.142 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.338237031007786 m\n", - "Volume in reservoir = 18.338237031007786 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.7484589894759166 m³/s \n", - "Current outflux vel = 0.952967583013256 m/s \n", - "Current pipe pressure = 18.221 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.358708194635774 m\n", - "Volume in reservoir = 18.358708194635774 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.1814388684956199 m³/s \n", - "Current outflux vel = 0.23101514232062617 m/s \n", - "Current pipe pressure = 18.303 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.393650151082664 m\n", - "Volume in reservoir = 18.393650151082664 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.39751401984090073 m³/s \n", - "Current outflux vel = -0.5061305696480729 m/s \n", - "Current pipe pressure = 18.362 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.44190138053861 m\n", - "Volume in reservoir = 18.44190138053861 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9296602275943082 m³/s \n", - "Current outflux vel = -1.1836801649405648 m/s \n", - "Current pipe pressure = 18.388 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.500875106094632 m\n", - "Volume in reservoir = 18.500875106094632 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.358071051314673 m³/s \n", - "Current outflux vel = -1.729149767093898 m/s \n", - "Current pipe pressure = 18.384 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.566799948394408 m\n", - "Volume in reservoir = 18.566799948394408 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.635565664831171 m³/s \n", - "Current outflux vel = -2.0824668824741037 m/s \n", - "Current pipe pressure = 18.371 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.63512701198546 m\n", - "Volume in reservoir = 18.63512701198546 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.7312741475924442 m³/s \n", - "Current outflux vel = -2.2043267074923607 m/s \n", - "Current pipe pressure = 18.375 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.701041964915476 m\n", - "Volume in reservoir = 18.701041964915476 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6347398714956516 m³/s \n", - "Current outflux vel = -2.0814154497435418 m/s \n", - "Current pipe pressure = 18.42 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.760006419302417 m\n", - "Volume in reservoir = 18.760006419302417 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3570234451892356 m³/s \n", - "Current outflux vel = -1.7278159135476845 m/s \n", - "Current pipe pressure = 18.509 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.808258094422133 m\n", - "Volume in reservoir = 18.808258094422133 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9290454368623117 m³/s \n", - "Current outflux vel = -1.1828973890688501 m/s \n", - "Current pipe pressure = 18.63 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.843201764762256 m\n", - "Volume in reservoir = 18.843201764762256 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.3972715019976422 m³/s \n", - "Current outflux vel = -0.5058217863397323 m/s \n", - "Current pipe pressure = 18.751 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.863641560128915 m\n", - "Volume in reservoir = 18.863641560128915 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.1825313068364606 m³/s \n", - "Current outflux vel = 0.2324060780163694 m/s \n", - "Current pipe pressure = 18.838 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.869842423876356 m\n", - "Volume in reservoir = 18.869842423876356 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.7518973126484367 m³/s \n", - "Current outflux vel = 0.9573453920440879 m/s \n", - "Current pipe pressure = 18.868 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.863440709334462 m\n", - "Volume in reservoir = 18.863440709334462 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2559796048041003 m³/s \n", - "Current outflux vel = 1.5991629002174224 m/s \n", - "Current pipe pressure = 18.834 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.847216502186825 m\n", - "Volume in reservoir = 18.847216502186825 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6490301314921794 m³/s \n", - "Current outflux vel = 2.099610373875668 m/s \n", - "Current pipe pressure = 18.75 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.82478355076625 m\n", - "Volume in reservoir = 18.82478355076625 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.897641442580045 m³/s \n", - "Current outflux vel = 2.416152126421194 m/s \n", - "Current pipe pressure = 18.648 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.800247093555956 m\n", - "Volume in reservoir = 18.800247093555956 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9820575474970978 m³/s \n", - "Current outflux vel = 2.523634049414098 m/s \n", - "Current pipe pressure = 18.558 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.777868411368377 m\n", - "Volume in reservoir = 18.777868411368377 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.895941188602518 m³/s \n", - "Current outflux vel = 2.413987295820913 m/s \n", - "Current pipe pressure = 18.509 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.761739514944693 m\n", - "Volume in reservoir = 18.761739514944693 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6460004547353813 m³/s \n", - "Current outflux vel = 2.0957528696211476 m/s \n", - "Current pipe pressure = 18.513 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.755453869142816 m\n", - "Volume in reservoir = 18.755453869142816 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.252166060107646 m³/s \n", - "Current outflux vel = 1.5943073443042817 m/s \n", - "Current pipe pressure = 18.564 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.76177414854119 m\n", - "Volume in reservoir = 18.76177414854119 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.7477126964200855 m³/s \n", - "Current outflux vel = 0.9520173731826107 m/s \n", - "Current pipe pressure = 18.644 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.782332379754322 m\n", - "Volume in reservoir = 18.782332379754322 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.17794992810087423 m³/s \n", - "Current outflux vel = 0.22657288544081206 m/s \n", - "Current pipe pressure = 18.726 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.817401908252585 m\n", - "Volume in reservoir = 18.817401908252585 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.4026198499366038 m³/s \n", - "Current outflux vel = -0.512631514434621 m/s \n", - "Current pipe pressure = 18.786 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.865785650531915 m\n", - "Volume in reservoir = 18.865785650531915 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.934965846725148 m³/s \n", - "Current outflux vel = -1.1904354890272535 m/s \n", - "Current pipe pressure = 18.812 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.924869339727962 m\n", - "Volume in reservoir = 18.924869339727962 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3624832343415907 m³/s \n", - "Current outflux vel = -1.7347675330023788 m/s \n", - "Current pipe pressure = 18.808 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.990875060484345 m\n", - "Volume in reservoir = 18.990875060484345 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.638825956271717 m³/s \n", - "Current outflux vel = -2.0866180144635686 m/s \n", - "Current pipe pressure = 18.794 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.0592698859544 m\n", - "Volume in reservoir = 19.0592698859544 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.7340184217510497 m³/s \n", - "Current outflux vel = -2.207820825872692 m/s \n", - "Current pipe pressure = 18.799 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.12526525314951 m\n", - "Volume in reservoir = 19.12526525314951 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.63799117764885 m³/s \n", - "Current outflux vel = -2.0855551413098348 m/s \n", - "Current pipe pressure = 18.843 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.184338905830085 m\n", - "Volume in reservoir = 19.184338905830085 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3614187942780565 m³/s \n", - "Current outflux vel = -1.7334122458204868 m/s \n", - "Current pipe pressure = 18.933 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.23272230364334 m\n", - "Volume in reservoir = 19.23272230364334 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9343302213266865 m³/s \n", - "Current outflux vel = -1.1896261856342942 m/s \n", - "Current pipe pressure = 19.053 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.267792961256824 m\n", - "Volume in reservoir = 19.267792961256824 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.40235689219536774 m³/s \n", - "Current outflux vel = -0.512296706239885 m/s \n", - "Current pipe pressure = 19.174 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.288319356324596 m\n", - "Volume in reservoir = 19.288319356324596 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.1790664783992477 m³/s \n", - "Current outflux vel = 0.22799452143438698 m/s \n", - "Current pipe pressure = 19.262 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.294537890835194 m\n", - "Volume in reservoir = 19.294537890835194 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.7511918117465394 m³/s \n", - "Current outflux vel = 0.9564471203969458 m/s \n", - "Current pipe pressure = 19.292 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.28807438343826 m\n", - "Volume in reservoir = 19.28807438343826 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2584516763979994 m³/s \n", - "Current outflux vel = 1.602310439528191 m/s \n", - "Current pipe pressure = 19.258 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.271718664840417 m\n", - "Volume in reservoir = 19.271718664840417 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6542879828473942 m³/s \n", - "Current outflux vel = 2.106304878141467 m/s \n", - "Current pipe pressure = 19.175 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.249109133198317 m\n", - "Volume in reservoir = 19.249109133198317 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9046984311067174 m³/s \n", - "Current outflux vel = 2.4251373632800957 m/s \n", - "Current pipe pressure = 19.071 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.224381255793617 m\n", - "Volume in reservoir = 19.224381255793617 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9897048657410152 m³/s \n", - "Current outflux vel = 2.5333709174134285 m/s \n", - "Current pipe pressure = 18.981 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.20182735541471 m\n", - "Volume in reservoir = 19.20182735541471 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9029381845217255 m³/s \n", - "Current outflux vel = 2.422896147719599 m/s \n", - "Current pipe pressure = 18.931 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.185569172269023 m\n", - "Volume in reservoir = 19.185569172269023 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6511594765310258 m³/s \n", - "Current outflux vel = 2.102321540183513 m/s \n", - "Current pipe pressure = 18.935 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.179224114533007 m\n", - "Volume in reservoir = 19.179224114533007 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2545310492212405 m³/s \n", - "Current outflux vel = 1.5973185419665783 m/s \n", - "Current pipe pressure = 18.987 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.18556398101568 m\n", - "Volume in reservoir = 19.18556398101568 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.746920080743086 m³/s \n", - "Current outflux vel = 0.9510081835588778 m/s \n", - "Current pipe pressure = 19.068 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.206209904842286 m\n", - "Volume in reservoir = 19.206209904842286 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.17443623582986617 m³/s \n", - "Current outflux vel = 0.22209911349333428 m/s \n", - "Current pipe pressure = 19.15 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.241406408641584 m\n", - "Volume in reservoir = 19.241406408641584 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.4077016729501843 m³/s \n", - "Current outflux vel = -0.5191018924548568 m/s \n", - "Current pipe pressure = 19.21 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.28992037760156 m\n", - "Volume in reservoir = 19.28992037760156 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9401798232688102 m³/s \n", - "Current outflux vel = -1.1970741301479657 m/s \n", - "Current pipe pressure = 19.236 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.34911009844234 m\n", - "Volume in reservoir = 19.34911009844234 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3667376800026083 m³/s \n", - "Current outflux vel = -1.7401844614589135 m/s \n", - "Current pipe pressure = 19.231 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.415191625093748 m\n", - "Volume in reservoir = 19.415191625093748 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6418825336774057 m³/s \n", - "Current outflux vel = -2.0905097696880355 m/s \n", - "Current pipe pressure = 19.218 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.48364874343508 m\n", - "Volume in reservoir = 19.48364874343508 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.7365428601093003 m³/s \n", - "Current outflux vel = -2.211035040618663 m/s \n", - "Current pipe pressure = 19.222 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.54971944385985 m\n", - "Volume in reservoir = 19.54971944385985 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.641038146825055 m³/s \n", - "Current outflux vel = -2.089434662956568 m/s \n", - "Current pipe pressure = 19.267 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.608898347861917 m\n", - "Volume in reservoir = 19.608898347861917 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3656554024573937 m³/s \n", - "Current outflux vel = -1.7388064628899673 m/s \n", - "Current pipe pressure = 19.356 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.657411164633444 m\n", - "Volume in reservoir = 19.657411164633444 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9395223494332184 m³/s \n", - "Current outflux vel = -1.1962370084608616 m/s \n", - "Current pipe pressure = 19.477 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.692608198644354 m\n", - "Volume in reservoir = 19.692608198644354 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.4074176421465767 m³/s \n", - "Current outflux vel = -0.5187402532037808 m/s \n", - "Current pipe pressure = 19.599 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.71322181119646 m\n", - "Volume in reservoir = 19.71322181119646 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.1755769777173326 m³/s \n", - "Current outflux vel = 0.2235515511747924 m/s \n", - "Current pipe pressure = 19.687 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.719459174786365 m\n", - "Volume in reservoir = 19.719459174786365 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.7504399425864621 m³/s \n", - "Current outflux vel = 0.9554898108498686 m/s \n", - "Current pipe pressure = 19.718 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.71293506748853 m\n", - "Volume in reservoir = 19.71293506748853 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.260876017200298 m³/s \n", - "Current outflux vel = 1.6053972061075925 m/s \n", - "Current pipe pressure = 19.684 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.69644895420229 m\n", - "Volume in reservoir = 19.69644895420229 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6595012548911425 m³/s \n", - "Current outflux vel = 2.1129426222650296 m/s \n", - "Current pipe pressure = 19.599 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.673664034284787 m\n", - "Volume in reservoir = 19.673664034284787 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.911707981656641 m³/s \n", - "Current outflux vel = 2.4340622002310783 m/s \n", - "Current pipe pressure = 19.495 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.64874596618851 m\n", - "Volume in reservoir = 19.64874596618851 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9973033884902889 m³/s \n", - "Current outflux vel = 2.5430456570593734 m/s \n", - "Current pipe pressure = 19.404 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.625999096439696 m\n", - "Volume in reservoir = 19.625999096439696 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9106457342187237 m³/s \n", - "Current outflux vel = 2.4327097047868285 m/s \n", - "Current pipe pressure = 19.353 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.60957447049278 m\n", - "Volume in reservoir = 19.60957447049278 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6578053854321366 m³/s \n", - "Current outflux vel = 2.1107833742071147 m/s \n", - "Current pipe pressure = 19.357 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.603113178666412 m\n", - "Volume in reservoir = 19.603113178666412 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2591701681569483 m³/s \n", - "Current outflux vel = 1.6032252516482512 m/s \n", - "Current pipe pressure = 19.409 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.609395326699516 m\n", - "Volume in reservoir = 19.609395326699516 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.7492212540728835 m³/s \n", - "Current outflux vel = 0.9539381284416659 m/s \n", - "Current pipe pressure = 19.491 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.63003004641622 m\n", - "Volume in reservoir = 19.63003004641622 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.17487990397222297 m³/s \n", - "Current outflux vel = 0.22266400931692215 m/s \n", - "Current pipe pressure = 19.574 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.665231943704324 m\n", - "Volume in reservoir = 19.665231943704324 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.4079206398938505 m³/s \n", - "Current outflux vel = -0.5193806898265225 m/s \n", - "Current pipe pressure = 19.634 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.713731424631423 m\n", - "Volume in reservoir = 19.713731424631423 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9396088497242316 m³/s \n", - "Current outflux vel = -1.1963471440520106 m/s \n", - "Current pipe pressure = 19.66 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.772859693699182 m\n", - "Volume in reservoir = 19.772859693699182 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3643000329721373 m³/s \n", - "Current outflux vel = -1.7370807528636116 m/s \n", - "Current pipe pressure = 19.655 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.838827699231935 m\n", - "Volume in reservoir = 19.838827699231935 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6373767582042433 m³/s \n", - "Current outflux vel = -2.0847728381759074 m/s \n", - "Current pipe pressure = 19.642 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.907136966596525 m\n", - "Volume in reservoir = 19.907136966596525 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.730675139495028 m³/s \n", - "Current outflux vel = -2.2035640266951138 m/s \n", - "Current pipe pressure = 19.647 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.97305313745521 m\n", - "Volume in reservoir = 19.97305313745521 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6349059618584552 m³/s \n", - "Current outflux vel = -2.0816269225614628 m/s \n", - "Current pipe pressure = 19.692 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.032088827539624 m\n", - "Volume in reservoir = 20.032088827539624 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3599689299979723 m³/s \n", - "Current outflux vel = -1.7315662212845846 m/s \n", - "Current pipe pressure = 19.781 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.080465295252832 m\n", - "Volume in reservoir = 20.080465295252832 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9340974370526246 m³/s \n", - "Current outflux vel = -1.1893297954911661 m/s \n", - "Current pipe pressure = 19.902 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.11550679773553 m\n", - "Volume in reservoir = 20.11550679773553 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.4012117032578378 m³/s \n", - "Current outflux vel = -0.5108386063984286 m/s \n", - "Current pipe pressure = 20.023 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.135910399324978 m\n", - "Volume in reservoir = 20.135910399324978 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.18397159047074438 m³/s \n", - "Current outflux vel = 0.23423990409517437 m/s \n", - "Current pipe pressure = 20.111 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.141854684365388 m\n", - "Volume in reservoir = 20.141854684365388 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.762161760688726 m³/s \n", - "Current outflux vel = 0.9704144931938635 m/s \n", - "Current pipe pressure = 20.14 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.13494411269316 m\n", - "Volume in reservoir = 20.13494411269316 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2763354060726226 m³/s \n", - "Current outflux vel = 1.6250807113572752 m/s \n", - "Current pipe pressure = 20.105 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.117988095587478 m\n", - "Volume in reservoir = 20.117988095587478 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6782987607995206 m³/s \n", - "Current outflux vel = 2.1368763501299695 m/s \n", - "Current pipe pressure = 20.018 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.09467421838379 m\n", - "Volume in reservoir = 20.09467421838379 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9328674511511414 m³/s \n", - "Current outflux vel = 2.4610032735370937 m/s \n", - "Current pipe pressure = 19.912 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.06919587507006 m\n", - "Volume in reservoir = 20.06919587507006 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.019715234709676 m³/s \n", - "Current outflux vel = 2.57158130593642 m/s \n", - "Current pipe pressure = 19.818 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.045904145058767 m\n", - "Volume in reservoir = 20.045904145058767 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.932440778715025 m³/s \n", - "Current outflux vel = 2.460460017318782 m/s \n", - "Current pipe pressure = 19.767 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.028976653274377 m\n", - "Volume in reservoir = 20.028976653274377 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6779208121265283 m³/s \n", - "Current outflux vel = 2.1363951309336353 m/s \n", - "Current pipe pressure = 19.771 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.022076966006942 m\n", - "Volume in reservoir = 20.022076966006942 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2767068109698494 m³/s \n", - "Current outflux vel = 1.6255535987595324 m/s \n", - "Current pipe pressure = 19.824 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.02799343629163 m\n", - "Volume in reservoir = 20.02799343629163 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.7638494637037254 m³/s \n", - "Current outflux vel = 0.9725633434123295 m/s \n", - "Current pipe pressure = 19.906 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.048323542645356 m\n", - "Volume in reservoir = 20.048323542645356 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.1870655890544535 m³/s \n", - "Current outflux vel = 0.23817930544330743 m/s \n", - "Current pipe pressure = 19.991 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.083252790760664 m\n", - "Volume in reservoir = 20.083252790760664 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.39701850689586476 m³/s \n", - "Current outflux vel = -0.5054996629715249 m/s \n", - "Current pipe pressure = 20.052 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.131476573597812 m\n", - "Volume in reservoir = 20.131476573597812 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.928596457134005 m³/s \n", - "Current outflux vel = -1.1823257303239856 m/s \n", - "Current pipe pressure = 20.079 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.190300751599352 m\n", - "Volume in reservoir = 20.190300751599352 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3521681608847314 m³/s \n", - "Current outflux vel = -1.7216339735702575 m/s \n", - "Current pipe pressure = 20.075 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.25593257779498 m\n", - "Volume in reservoir = 20.25593257779498 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6239781121012862 m³/s \n", - "Current outflux vel = -2.0677131521117107 m/s \n", - "Current pipe pressure = 20.062 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.323892006946224 m\n", - "Volume in reservoir = 20.323892006946224 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.7167414886234986 m³/s \n", - "Current outflux vel = -2.185823151402949 m/s \n", - "Current pipe pressure = 20.068 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.389472794538197 m\n", - "Volume in reservoir = 20.389472794538197 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6215517845217327 m³/s \n", - "Current outflux vel = -2.0646238558889416 m/s \n", - "Current pipe pressure = 20.112 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.44820580917309 m\n", - "Volume in reservoir = 20.44820580917309 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3479137928410776 m³/s \n", - "Current outflux vel = -1.71621714393922 m/s \n", - "Current pipe pressure = 20.201 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.496308316832433 m\n", - "Volume in reservoir = 20.496308316832433 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9231749969499484 m³/s \n", - "Current outflux vel = -1.1754229128274376 m/s \n", - "Current pipe pressure = 20.321 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.531078798321676 m\n", - "Volume in reservoir = 20.531078798321676 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.3903900660412925 m³/s \n", - "Current outflux vel = -0.49706006995554536 m/s \n", - "Current pipe pressure = 20.44 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.55117882598336 m\n", - "Volume in reservoir = 20.55117882598336 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.19610715890135227 m³/s \n", - "Current outflux vel = 0.24969138971886398 m/s \n", - "Current pipe pressure = 20.527 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.5567579460981 m\n", - "Volume in reservoir = 20.5567579460981 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.7767666662434131 m³/s \n", - "Current outflux vel = 0.9890100364932134 m/s \n", - "Current pipe pressure = 20.555 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.549409829735563 m\n", - "Volume in reservoir = 20.549409829735563 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2938386071866699 m³/s \n", - "Current outflux vel = 1.6473664791751326 m/s \n", - "Current pipe pressure = 20.518 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.53195350050116 m\n", - "Volume in reservoir = 20.53195350050116 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6983138167113645 m³/s \n", - "Current outflux vel = 2.1623603108070144 m/s \n", - "Current pipe pressure = 20.43 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.508100507306633 m\n", - "Volume in reservoir = 20.508100507306633 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.954434694461516 m³/s \n", - "Current outflux vel = 2.4884635405907876 m/s \n", - "Current pipe pressure = 20.32 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.482070907658137 m\n", - "Volume in reservoir = 20.482070907658137 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0417675583415797 m³/s \n", - "Current outflux vel = 2.599659196437858 m/s \n", - "Current pipe pressure = 20.226 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.45824283894997 m\n", - "Volume in reservoir = 20.45824283894997 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.953895712468773 m³/s \n", - "Current outflux vel = 2.487777287403727 m/s \n", - "Current pipe pressure = 20.173 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.44081987788327 m\n", - "Volume in reservoir = 20.44081987788327 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.697740574070791 m³/s \n", - "Current outflux vel = 2.161630435608308 m/s \n", - "Current pipe pressure = 20.177 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.43348849073623 m\n", - "Volume in reservoir = 20.43348849073623 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.293975812193798 m³/s \n", - "Current outflux vel = 1.647541174015944 m/s \n", - "Current pipe pressure = 20.231 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.43904564627343 m\n", - "Volume in reservoir = 20.43904564627343 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.7782232804948743 m³/s \n", - "Current outflux vel = 0.9908646553595987 m/s \n", - "Current pipe pressure = 20.314 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.45907728995965 m\n", - "Volume in reservoir = 20.45907728995965 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.19900530760331359 m³/s \n", - "Current outflux vel = 0.253381427252724 m/s \n", - "Current pipe pressure = 20.4 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.493739775168255 m\n", - "Volume in reservoir = 20.493739775168255 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.3863514363239994 m³/s \n", - "Current outflux vel = -0.49191792689294517 m/s \n", - "Current pipe pressure = 20.463 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.541693480243612 m\n", - "Volume in reservoir = 20.541693480243612 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9178078243494836 m³/s \n", - "Current outflux vel = -1.1685892164291067 m/s \n", - "Current pipe pressure = 20.49 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.600219106775334 m\n", - "Volume in reservoir = 20.600219106775334 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3402558824780895 m³/s \n", - "Current outflux vel = -1.7064667896350263 m/s \n", - "Current pipe pressure = 20.487 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.665520383450648 m\n", - "Volume in reservoir = 20.665520383450648 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6108017098575873 m³/s \n", - "Current outflux vel = -2.0509364357176962 m/s \n", - "Current pipe pressure = 20.475 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.733135606020618 m\n", - "Volume in reservoir = 20.733135606020618 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.7030295169274243 m³/s \n", - "Current outflux vel = -2.168364526803218 m/s \n", - "Current pipe pressure = 20.481 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.798386605414688 m\n", - "Volume in reservoir = 20.798386605414688 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6084177592393352 m³/s \n", - "Current outflux vel = -2.0479010955178416 m/s \n", - "Current pipe pressure = 20.525 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.85682242207317 m\n", - "Volume in reservoir = 20.85682242207317 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3360746408561701 m³/s \n", - "Current outflux vel = -1.701143067455906 m/s \n", - "Current pipe pressure = 20.614 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.904656512815805 m\n", - "Volume in reservoir = 20.904656512815805 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9124720775470682 m³/s \n", - "Current outflux vel = -1.1617955325995772 m/s \n", - "Current pipe pressure = 20.732 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.939161779286604 m\n", - "Volume in reservoir = 20.939161779286604 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.3797994233406832 m³/s \n", - "Current outflux vel = -0.4835756448649688 m/s \n", - "Current pipe pressure = 20.85 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.958964314886565 m\n", - "Volume in reservoir = 20.958964314886565 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.20799993371328887 m³/s \n", - "Current outflux vel = 0.26483374090605194 m/s \n", - "Current pipe pressure = 20.936 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.964184580962286 m\n", - "Volume in reservoir = 20.964184580962286 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.7911192788463977 m³/s \n", - "Current outflux vel = 1.0072843504295976 m/s \n", - "Current pipe pressure = 20.963 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.956405580338384 m\n", - "Volume in reservoir = 20.956405580338384 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3110753596936853 m³/s \n", - "Current outflux vel = 1.6693129940898774 m/s \n", - "Current pipe pressure = 20.924 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.93845636526081 m\n", - "Volume in reservoir = 20.93845636526081 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.718031766803616 m³/s \n", - "Current outflux vel = 2.1874659846055833 m/s \n", - "Current pipe pressure = 20.833 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.91407265303952 m\n", - "Volume in reservoir = 20.91407265303952 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9756660741968137 m³/s \n", - "Current outflux vel = 2.5154961728590575 m/s \n", - "Current pipe pressure = 20.722 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.887500515486717 m\n", - "Volume in reservoir = 20.887500515486717 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.063471246882594 m³/s \n", - "Current outflux vel = 2.6272931909548927 m/s \n", - "Current pipe pressure = 20.625 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.863144487742815 m\n", - "Volume in reservoir = 20.863144487742815 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9750156202894056 m³/s \n", - "Current outflux vel = 2.5146679892221178 m/s \n", - "Current pipe pressure = 20.572 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.845233463830745 m\n", - "Volume in reservoir = 20.845233463830745 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7172642627671535 m³/s \n", - "Current outflux vel = 2.1864887681156153 m/s \n", - "Current pipe pressure = 20.575 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.837476984459617 m\n", - "Volume in reservoir = 20.837476984459617 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.310980670742791 m³/s \n", - "Current outflux vel = 1.6691924323731493 m/s \n", - "Current pipe pressure = 20.63 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.842681078990548 m\n", - "Volume in reservoir = 20.842681078990548 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.7923470587598929 m³/s \n", - "Current outflux vel = 1.0088476083676912 m/s \n", - "Current pipe pressure = 20.715 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.86242029501709 m\n", - "Volume in reservoir = 20.86242029501709 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.21070367914130356 m³/s \n", - "Current outflux vel = 0.26827625650389714 m/s \n", - "Current pipe pressure = 20.802 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.89682178571136 m\n", - "Volume in reservoir = 20.89682178571136 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.37591475042466427 m³/s \n", - "Current outflux vel = -0.47862952568993183 m/s \n", - "Current pipe pressure = 20.866 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.944510911655918 m\n", - "Volume in reservoir = 20.944510911655918 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9072381746273734 m³/s \n", - "Current outflux vel = -1.155131520428917 m/s \n", - "Current pipe pressure = 20.894 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.00274339671898 m\n", - "Volume in reservoir = 21.00274339671898 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3285581815342387 m³/s \n", - "Current outflux vel = -1.6915728142108297 m/s \n", - "Current pipe pressure = 20.891 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.06771960967451 m\n", - "Volume in reservoir = 21.06771960967451 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.5978420368824184 m³/s \n", - "Current outflux vel = -2.0344356675988755 m/s \n", - "Current pipe pressure = 20.88 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.134996100730866 m\n", - "Volume in reservoir = 21.134996100730866 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6895332599834036 m³/s \n", - "Current outflux vel = -2.151180558756184 m/s \n", - "Current pipe pressure = 20.886 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.199922764931994 m\n", - "Volume in reservoir = 21.199922764931994 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.5954985057497408 m³/s \n", - "Current outflux vel = -2.0314517910864325 m/s \n", - "Current pipe pressure = 20.931 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.258066734828873 m\n", - "Volume in reservoir = 21.258066734828873 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.324446681279875 m³/s \n", - "Current outflux vel = -1.6863378894987853 m/s \n", - "Current pipe pressure = 21.019 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.305637833997558 m\n", - "Volume in reservoir = 21.305637833997558 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9019841499347644 m³/s \n", - "Current outflux vel = -1.1484418884212722 m/s \n", - "Current pipe pressure = 21.136 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.339883577324663 m\n", - "Volume in reservoir = 21.339883577324663 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.3694353160447066 m³/s \n", - "Current outflux vel = -0.4703796536098532 m/s \n", - "Current pipe pressure = 21.253 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.35939458972083 m\n", - "Volume in reservoir = 21.35939458972083 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.21965439479711188 m³/s \n", - "Current outflux vel = 0.27967266163055243 m/s \n", - "Current pipe pressure = 21.337 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.364262204414054 m\n", - "Volume in reservoir = 21.364262204414054 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.8052239143150717 m³/s \n", - "Current outflux vel = 1.0252429300723875 m/s \n", - "Current pipe pressure = 21.363 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.35605889007448 m\n", - "Volume in reservoir = 21.35605889007448 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.328049259296172 m³/s \n", - "Current outflux vel = 1.690924834292128 m/s \n", - "Current pipe pressure = 21.322 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.337624260954478 m\n", - "Volume in reservoir = 21.337624260954478 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7374507932712295 m³/s \n", - "Current outflux vel = 2.2121910570244077 m/s \n", - "Current pipe pressure = 21.229 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.31271823878863 m\n", - "Volume in reservoir = 21.31271823878863 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.996561101705768 m³/s \n", - "Current outflux vel = 2.542100548171787 m/s \n", - "Current pipe pressure = 21.116 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.285612293302187 m\n", - "Volume in reservoir = 21.285612293302187 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0848258225992686 m³/s \n", - "Current outflux vel = 2.654482681218404 m/s \n", - "Current pipe pressure = 21.018 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.260736696791916 m\n", - "Volume in reservoir = 21.260736696791916 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9958000561134361 m³/s \n", - "Current outflux vel = 2.541131554828284 m/s \n", - "Current pipe pressure = 20.963 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.242345061407 m\n", - "Volume in reservoir = 21.242345061407 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.736490056741466 m³/s \n", - "Current outflux vel = 2.210967809282641 m/s \n", - "Current pipe pressure = 20.967 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.23417000527371 m\n", - "Volume in reservoir = 21.23417000527371 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.327725053643053 m³/s \n", - "Current outflux vel = 1.6905120428339502 m/s \n", - "Current pipe pressure = 21.022 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.239027181461474 m\n", - "Volume in reservoir = 21.239027181461474 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.8062252265185523 m³/s \n", - "Current outflux vel = 1.026517840366485 m/s \n", - "Current pipe pressure = 21.109 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.25847988863979 m\n", - "Volume in reservoir = 21.25847988863979 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.222165344510246 m³/s \n", - "Current outflux vel = 0.2828697021001562 m/s \n", - "Current pipe pressure = 21.197 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.29262603524315 m\n", - "Volume in reservoir = 21.29262603524315 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.365703763544405 m³/s \n", - "Current outflux vel = -0.46562849340321383 m/s \n", - "Current pipe pressure = 21.262 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.340055959539573 m\n", - "Volume in reservoir = 21.340055959539573 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.8968827434666135 m³/s \n", - "Current outflux vel = -1.1419465759722547 m/s \n", - "Current pipe pressure = 21.29 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.398000584378867 m\n", - "Volume in reservoir = 21.398000584378867 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3170700576815328 m³/s \n", - "Current outflux vel = -1.6769456806267495 m/s \n", - "Current pipe pressure = 21.289 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.46265707718434 m\n", - "Volume in reservoir = 21.46265707718434 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.5850936240793223 m³/s \n", - "Current outflux vel = -2.0182038842853656 m/s \n", - "Current pipe pressure = 21.278 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.529600185778943 m\n", - "Volume in reservoir = 21.529600185778943 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.676247942670977 m³/s \n", - "Current outflux vel = -2.134265167389648 m/s \n", - "Current pipe pressure = 21.284 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.59420782734965 m\n", - "Volume in reservoir = 21.59420782734965 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.5827886769764807 m³/s \n", - "Current outflux vel = -2.015269134485505 m/s \n", - "Current pipe pressure = 21.329 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.652065176038175 m\n", - "Volume in reservoir = 21.652065176038175 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3130251176957646 m³/s \n", - "Current outflux vel = -1.6717955030807887 m/s \n", - "Current pipe pressure = 21.416 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.699378591458895 m\n", - "Volume in reservoir = 21.699378591458895 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.8917066772321263 m³/s \n", - "Current outflux vel = -1.1353562037563372 m/s \n", - "Current pipe pressure = 21.532 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.733370389063403 m\n", - "Volume in reservoir = 21.733370389063403 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.3592932599419824 m³/s \n", - "Current outflux vel = -0.45746638671494216 m/s \n", - "Current pipe pressure = 21.649 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.752595733479804 m\n", - "Volume in reservoir = 21.752595733479804 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.23107505129382525 m³/s \n", - "Current outflux vel = 0.2942138931090044 m/s \n", - "Current pipe pressure = 21.732 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.757116789455136 m\n", - "Volume in reservoir = 21.757116789455136 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.819084960361711 m³/s \n", - "Current outflux vel = 1.0428913620303637 m/s \n", - "Current pipe pressure = 21.756 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.74849563915744 m\n", - "Volume in reservoir = 21.74849563915744 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3447640179112823 m³/s \n", - "Current outflux vel = 1.7122067259415894 m/s \n", - "Current pipe pressure = 21.713 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.72958308866652 m\n", - "Volume in reservoir = 21.72958308866652 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7565700637907793 m³/s \n", - "Current outflux vel = 2.2365344683163877 m/s \n", - "Current pipe pressure = 21.618 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.704163170136447 m\n", - "Volume in reservoir = 21.704163170136447 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0171195936512185 m³/s \n", - "Current outflux vel = 2.568276433096854 m/s \n", - "Current pipe pressure = 21.503 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.676532150143498 m\n", - "Volume in reservoir = 21.676532150143498 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.1058311332104873 m³/s \n", - "Current outflux vel = 2.6812274733380526 m/s \n", - "Current pipe pressure = 21.403 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.649394026365048 m\n", - "Volume in reservoir = 21.649394026365048 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.086305108525717 m³/s \n", - "Current outflux vel = 2.6563661665579286 m/s \n", - "Current pipe pressure = 21.336 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.623587736901683 m\n", - "Volume in reservoir = 21.623587736901683 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0330964475453763 m³/s \n", - "Current outflux vel = 2.5886187952753517 m/s \n", - "Current pipe pressure = 21.303 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.599683375514118 m\n", - "Volume in reservoir = 21.599683375514118 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9570007674211034 m³/s \n", - "Current outflux vel = 2.49173076615761 m/s \n", - "Current pipe pressure = 21.294 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.577902730135047 m\n", - "Volume in reservoir = 21.577902730135047 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.87200913922794 m³/s \n", - "Current outflux vel = 2.3835160641706463 m/s \n", - "Current pipe pressure = 21.292 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.5581304069389 m\n", - "Volume in reservoir = 21.5581304069389 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7916191955559637 m³/s \n", - "Current outflux vel = 2.2811604088884536 m/s \n", - "Current pipe pressure = 21.295 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.540023865792122 m\n", - "Volume in reservoir = 21.540023865792122 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7249280403487353 m³/s \n", - "Current outflux vel = 2.19624659279454 m/s \n", - "Current pipe pressure = 21.299 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.523150809361535 m\n", - "Volume in reservoir = 21.523150809361535 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6755351039788018 m³/s \n", - "Current outflux vel = 2.133357552977753 m/s \n", - "Current pipe pressure = 21.299 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.507094746149058 m\n", - "Volume in reservoir = 21.507094746149058 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6428129944209666 m³/s \n", - "Current outflux vel = 2.091694469101561 m/s \n", - "Current pipe pressure = 21.297 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.49149875126283 m\n", - "Volume in reservoir = 21.49149875126283 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6243800386593494 m³/s \n", - "Current outflux vel = 2.068224900899516 m/s \n", - "Current pipe pressure = 21.29 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.476059898903415 m\n", - "Volume in reservoir = 21.476059898903415 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.618074945873549 m³/s \n", - "Current outflux vel = 2.0601970074314107 m/s \n", - "Current pipe pressure = 21.279 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.460495669117712 m\n", - "Volume in reservoir = 21.460495669117712 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.623079523838957 m³/s \n", - "Current outflux vel = 2.0665690340016782 m/s \n", - "Current pipe pressure = 21.266 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.44453000425106 m\n", - "Volume in reservoir = 21.44453000425106 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.639133766004125 m³/s \n", - "Current outflux vel = 2.0870099299871248 m/s \n", - "Current pipe pressure = 21.249 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.427846723548804 m\n", - "Volume in reservoir = 21.427846723548804 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6678420995981669 m³/s \n", - "Current outflux vel = 2.123562515582508 m/s \n", - "Current pipe pressure = 21.228 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.410063727769174 m\n", - "Volume in reservoir = 21.410063727769174 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7118412510820313 m³/s \n", - "Current outflux vel = 2.179583975186557 m/s \n", - "Current pipe pressure = 21.203 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.390758919917605 m\n", - "Volume in reservoir = 21.390758919917605 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7727324302415377 m³/s \n", - "Current outflux vel = 2.257113032417994 m/s \n", - "Current pipe pressure = 21.173 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.369568694661027 m\n", - "Volume in reservoir = 21.369568694661027 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8481782499758375 m³/s \n", - "Current outflux vel = 2.353173633588665 m/s \n", - "Current pipe pressure = 21.136 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.346330964116895 m\n", - "Volume in reservoir = 21.346330964116895 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9301201006957143 m³/s \n", - "Current outflux vel = 2.4575052382939977 m/s \n", - "Current pipe pressure = 21.094 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.32120981566092 m\n", - "Volume in reservoir = 21.32120981566092 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0055106344270097 m³/s \n", - "Current outflux vel = 2.5534954471393734 m/s \n", - "Current pipe pressure = 21.047 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.294738660210623 m\n", - "Volume in reservoir = 21.294738660210623 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0595718817430186 m³/s \n", - "Current outflux vel = 2.6223283650598237 m/s \n", - "Current pipe pressure = 21.0 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.267752480870993 m\n", - "Volume in reservoir = 21.267752480870993 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0802306524801666 m³/s \n", - "Current outflux vel = 2.6486319289079776 m/s \n", - "Current pipe pressure = 20.957 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.241211995175863 m\n", - "Volume in reservoir = 21.241211995175863 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0624444641688613 m³/s \n", - "Current outflux vel = 2.6259858505999176 m/s \n", - "Current pipe pressure = 20.924 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.215976879065682 m\n", - "Volume in reservoir = 21.215976879065682 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0102440367690244 m³/s \n", - "Current outflux vel = 2.5595222021823685 m/s \n", - "Current pipe pressure = 20.903 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.19262474452559 m\n", - "Volume in reservoir = 21.19262474452559 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.934908230436679 m³/s \n", - "Current outflux vel = 2.4636016744255165 m/s \n", - "Current pipe pressure = 20.894 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.171372181526056 m\n", - "Volume in reservoir = 21.171372181526056 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8508831492713491 m³/s \n", - "Current outflux vel = 2.356617618336237 m/s \n", - "Current pipe pressure = 20.892 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.152102389219635 m\n", - "Volume in reservoir = 21.152102389219635 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7715154667855542 m³/s \n", - "Current outflux vel = 2.2555635464213384 m/s \n", - "Current pipe pressure = 20.895 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.134475249291484 m\n", - "Volume in reservoir = 21.134475249291484 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7057497815873854 m³/s \n", - "Current outflux vel = 2.171828075340426 m/s \n", - "Current pipe pressure = 20.898 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.11806354624421 m\n", - "Volume in reservoir = 21.11806354624421 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6570790716826411 m³/s \n", - "Current outflux vel = 2.109858602819372 m/s \n", - "Current pipe pressure = 20.899 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.10245680668179 m\n", - "Volume in reservoir = 21.10245680668179 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6248384369119673 m³/s \n", - "Current outflux vel = 2.0688085516819865 m/s \n", - "Current pipe pressure = 20.896 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.08730342252729 m\n", - "Volume in reservoir = 21.08730342252729 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6066742158431568 m³/s \n", - "Current outflux vel = 2.0456811471178655 m/s \n", - "Current pipe pressure = 20.89 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.072304580795436 m\n", - "Volume in reservoir = 21.072304580795436 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.600473262100657 m³/s \n", - "Current outflux vel = 2.037785847597841 m/s \n", - "Current pipe pressure = 20.88 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.057181730040348 m\n", - "Volume in reservoir = 21.057181730040348 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.605423167151332 m³/s \n", - "Current outflux vel = 2.044088262451045 m/s \n", - "Current pipe pressure = 20.867 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.041662479075658 m\n", - "Volume in reservoir = 21.041662479075658 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.621276027281788 m³/s \n", - "Current outflux vel = 2.064272750866297 m/s \n", - "Current pipe pressure = 20.85 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.025435282622176 m\n", - "Volume in reservoir = 21.025435282622176 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6495975076986094 m³/s \n", - "Current outflux vel = 2.1003327796984363 m/s \n", - "Current pipe pressure = 20.83 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.008123434028583 m\n", - "Volume in reservoir = 21.008123434028583 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6929940532369068 m³/s \n", - "Current outflux vel = 2.155586977582697 m/s \n", - "Current pipe pressure = 20.806 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.98930958103154 m\n", - "Volume in reservoir = 20.98930958103154 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.753092767999739 m³/s \n", - "Current outflux vel = 2.232107037806494 m/s \n", - "Current pipe pressure = 20.776 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.96863250762744 m\n", - "Volume in reservoir = 20.96863250762744 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8276504707977672 m³/s \n", - "Current outflux vel = 2.327036853373555 m/s \n", - "Current pipe pressure = 20.74 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.945928963144485 m\n", - "Volume in reservoir = 20.945928963144485 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9087506614307232 m³/s \n", - "Current outflux vel = 2.430296823172995 m/s \n", - "Current pipe pressure = 20.699 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.921358258301435 m\n", - "Volume in reservoir = 20.921358258301435 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9834906081120895 m³/s \n", - "Current outflux vel = 2.525458678859108 m/s \n", - "Current pipe pressure = 20.653 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.89544663163711 m\n", - "Volume in reservoir = 20.89544663163711 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.037188248333768 m³/s \n", - "Current outflux vel = 2.593828637848311 m/s \n", - "Current pipe pressure = 20.607 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.86902207028595 m\n", - "Volume in reservoir = 20.86902207028595 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.057763388799578 m³/s \n", - "Current outflux vel = 2.62002572032786 m/s \n", - "Current pipe pressure = 20.565 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.84303887841991 m\n", - "Volume in reservoir = 20.84303887841991 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.040150232787476 m³/s \n", - "Current outflux vel = 2.597599953585662 m/s \n", - "Current pipe pressure = 20.533 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.818349866908036 m\n", - "Volume in reservoir = 20.818349866908036 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9883974803375115 m³/s \n", - "Current outflux vel = 2.531706302617478 m/s \n", - "Current pipe pressure = 20.512 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.795525604262256 m\n", - "Volume in reservoir = 20.795525604262256 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9137910706684835 m³/s \n", - "Current outflux vel = 2.4367144715361597 m/s \n", - "Current pipe pressure = 20.503 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.77477796661661 m\n", - "Volume in reservoir = 20.77477796661661 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8306839540672664 m³/s \n", - "Current outflux vel = 2.330899204230574 m/s \n", - "Current pipe pressure = 20.502 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.755989011182063 m\n", - "Volume in reservoir = 20.755989011182063 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7522799853583537 m³/s \n", - "Current outflux vel = 2.231072170806208 m/s \n", - "Current pipe pressure = 20.505 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.738821094821837 m\n", - "Volume in reservoir = 20.738821094821837 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6873790722078523 m³/s \n", - "Current outflux vel = 2.1484377616935673 m/s \n", - "Current pipe pressure = 20.508 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.722852011444417 m\n", - "Volume in reservoir = 20.722852011444417 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6393727834423275 m³/s \n", - "Current outflux vel = 2.0873142564413256 m/s \n", - "Current pipe pressure = 20.509 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.707677069461287 m\n", - "Volume in reservoir = 20.707677069461287 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6075652737662072 m³/s \n", - "Current outflux vel = 2.046815677302143 m/s \n", - "Current pipe pressure = 20.506 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.692949658006786 m\n", - "Volume in reservoir = 20.692949658006786 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5896342275504878 m³/s \n", - "Current outflux vel = 2.023985160181815 m/s \n", - "Current pipe pressure = 20.5 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.678374510022028 m\n", - "Volume in reservoir = 20.678374510022028 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.583524536674757 m³/s \n", - "Current outflux vel = 2.016206060152727 m/s \n", - "Current pipe pressure = 20.49 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.663676509059837 m\n", - "Volume in reservoir = 20.663676509059837 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5884282419236528 m³/s \n", - "Current outflux vel = 2.0224496515913466 m/s \n", - "Current pipe pressure = 20.477 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.648586365985665 m\n", - "Volume in reservoir = 20.648586365985665 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6041107842450115 m³/s \n", - "Current outflux vel = 2.0424172846368833 m/s \n", - "Current pipe pressure = 20.461 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.632796857255965 m\n", - "Volume in reservoir = 20.632796857255965 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.632089038750875 m³/s \n", - "Current outflux vel = 2.0780403046664135 m/s \n", - "Current pipe pressure = 20.441 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.615936449267004 m\n", - "Volume in reservoir = 20.615936449267004 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6749354021073792 m³/s \n", - "Current outflux vel = 2.1325939888400063 m/s \n", - "Current pipe pressure = 20.418 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.597592483119087 m\n", - "Volume in reservoir = 20.597592483119087 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7342961402310786 m³/s \n", - "Current outflux vel = 2.2081744280237685 m/s \n", - "Current pipe pressure = 20.389 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.577406210343355 m\n", - "Volume in reservoir = 20.577406210343355 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8080170936698632 m³/s \n", - "Current outflux vel = 2.302038861217609 m/s \n", - "Current pipe pressure = 20.354 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.555213396350045 m\n", - "Volume in reservoir = 20.555213396350045 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8883198419259293 m³/s \n", - "Current outflux vel = 2.4042834958481447 m/s \n", - "Current pipe pressure = 20.313 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.531168834747287 m\n", - "Volume in reservoir = 20.531168834747287 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9624430395555308 m³/s \n", - "Current outflux vel = 2.498660082252373 m/s \n", - "Current pipe pressure = 20.269 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.50579191409325 m\n", - "Volume in reservoir = 20.50579191409325 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0157980132035456 m³/s \n", - "Current outflux vel = 2.5665937446093277 m/s \n", - "Current pipe pressure = 20.223 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.47990408028607 m\n", - "Volume in reservoir = 20.47990408028607 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0362922679644213 m³/s \n", - "Current outflux vel = 2.592687840210752 m/s \n", - "Current pipe pressure = 20.183 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.45445351059286 m\n", - "Volume in reservoir = 20.45445351059286 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.018843418163843 m³/s \n", - "Current outflux vel = 2.5704712746345115 m/s \n", - "Current pipe pressure = 20.151 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.430286604928877 m\n", - "Volume in reservoir = 20.430286604928877 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.96751142885713 m³/s \n", - "Current outflux vel = 2.5051133559392817 m/s \n", - "Current pipe pressure = 20.131 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.407967203732607 m\n", - "Volume in reservoir = 20.407967203732607 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8935948709584314 m³/s \n", - "Current outflux vel = 2.4109998714119523 m/s \n", - "Current pipe pressure = 20.121 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.38770271489028 m\n", - "Volume in reservoir = 20.38770271489028 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8113563310815846 m³/s \n", - "Current outflux vel = 2.3062905103394713 m/s \n", - "Current pipe pressure = 20.12 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.36937421855157 m\n", - "Volume in reservoir = 20.36937421855157 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.733860082888223 m³/s \n", - "Current outflux vel = 2.2076192225710725 m/s \n", - "Current pipe pressure = 20.123 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.352646613478353 m\n", - "Volume in reservoir = 20.352646613478353 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6697652755792216 m³/s \n", - "Current outflux vel = 2.1260111792930716 m/s \n", - "Current pipe pressure = 20.126 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.33710264570002 m\n", - "Volume in reservoir = 20.33710264570002 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6223670335230902 m³/s \n", - "Current outflux vel = 2.0656618631562758 m/s \n", - "Current pipe pressure = 20.127 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.322343195239984 m\n", - "Volume in reservoir = 20.322343195239984 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5909446855619036 m³/s \n", - "Current outflux vel = 2.025653687143665 m/s \n", - "Current pipe pressure = 20.125 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.308026316767354 m\n", - "Volume in reservoir = 20.308026316767354 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5732121229959137 m³/s \n", - "Current outflux vel = 2.003075887255156 m/s \n", - "Current pipe pressure = 20.119 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.293859757464226 m\n", - "Volume in reservoir = 20.293859757464226 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.56718027858151 m³/s \n", - "Current outflux vel = 1.9953959044190475 m/s \n", - "Current pipe pressure = 20.109 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.279571267802552 m\n", - "Volume in reservoir = 20.279571267802552 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5720471000674865 m³/s \n", - "Current outflux vel = 2.0015925339921594 m/s \n", - "Current pipe pressure = 20.097 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.264894131350736 m\n", - "Volume in reservoir = 20.264894131350736 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5875898283034096 m³/s \n", - "Current outflux vel = 2.0213821502152083 m/s \n", - "Current pipe pressure = 20.081 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.24952511840175 m\n", - "Volume in reservoir = 20.24952511840175 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6152684910428867 m³/s \n", - "Current outflux vel = 2.0566237181604983 m/s \n", - "Current pipe pressure = 20.062 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.23309767420035 m\n", - "Volume in reservoir = 20.23309767420035 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6576160884429463 m³/s \n", - "Current outflux vel = 2.110542353794778 m/s \n", - "Current pipe pressure = 20.039 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.215203795343324 m\n", - "Volume in reservoir = 20.215203795343324 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7162917905174775 m³/s \n", - "Current outflux vel = 2.1852505779911704 m/s \n", - "Current pipe pressure = 20.011 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.195487289365687 m\n", - "Volume in reservoir = 20.195487289365687 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7892254037716286 m³/s \n", - "Current outflux vel = 2.278112538526776 m/s \n", - "Current pipe pressure = 19.977 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.173783122234493 m\n", - "Volume in reservoir = 20.173783122234493 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8687727443572342 m³/s \n", - "Current outflux vel = 2.3793953582388854 m/s \n", - "Current pipe pressure = 19.937 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.15024182773056 m\n", - "Volume in reservoir = 20.15024182773056 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9423109380013712 m³/s \n", - "Current outflux vel = 2.473027094434993 m/s \n", - "Current pipe pressure = 19.893 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.125376256340907 m\n", - "Volume in reservoir = 20.125376256340907 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9953425119506762 m³/s \n", - "Current outflux vel = 2.540548991506795 m/s \n", - "Current pipe pressure = 19.849 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.100001728253872 m\n", - "Volume in reservoir = 20.100001728253872 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0157585203098307 m³/s \n", - "Current outflux vel = 2.566543460695314 m/s \n", - "Current pipe pressure = 19.809 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.075060563112388 m\n", - "Volume in reservoir = 20.075060563112388 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9984658328090124 m³/s \n", - "Current outflux vel = 2.5445257271345247 m/s \n", - "Current pipe pressure = 19.778 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.05139316547286 m\n", - "Volume in reservoir = 20.05139316547286 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9475298192134278 m³/s \n", - "Current outflux vel = 2.4796719803734586 m/s \n", - "Current pipe pressure = 19.758 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.029556953429964 m\n", - "Volume in reservoir = 20.029556953429964 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8742660800283146 m³/s \n", - "Current outflux vel = 2.3863896904478095 m/s \n", - "Current pipe pressure = 19.749 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.009755110971867 m\n", - "Volume in reservoir = 20.009755110971867 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7928492915141794 m³/s \n", - "Current outflux vel = 2.282726615706273 m/s \n", - "Current pipe pressure = 19.748 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.991867912459455 m\n", - "Volume in reservoir = 19.991867912459455 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7162070775771903 m³/s \n", - "Current outflux vel = 2.185142718125646 m/s \n", - "Current pipe pressure = 19.751 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.975562876330706 m\n", - "Volume in reservoir = 19.975562876330706 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6528615745039246 m³/s \n", - "Current outflux vel = 2.1044887186316212 m/s \n", - "Current pipe pressure = 19.754 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.960427657323084 m\n", - "Volume in reservoir = 19.960427657323084 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6060163140605273 m³/s \n", - "Current outflux vel = 2.0448434805516698 m/s \n", - "Current pipe pressure = 19.755 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.946068518104866 m\n", - "Volume in reservoir = 19.946068518104866 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5749316241127593 m³/s \n", - "Current outflux vel = 2.00526522407434 m/s \n", - "Current pipe pressure = 19.752 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.932147842362646 m\n", - "Volume in reservoir = 19.932147842362646 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5573635067999514 m³/s \n", - "Current outflux vel = 1.9828968023851266 m/s \n", - "Current pipe pressure = 19.746 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.918375895318782 m\n", - "Volume in reservoir = 19.918375895318782 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5513953252235857 m³/s \n", - "Current outflux vel = 1.9752978775919379 m/s \n", - "Current pipe pressure = 19.737 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.90448268103034 m\n", - "Volume in reservoir = 19.90448268103034 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5562356220884779 m³/s \n", - "Current outflux vel = 1.9814607349685762 m/s \n", - "Current pipe pressure = 19.725 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.89020355983586 m\n", - "Volume in reservoir = 19.89020355983586 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5716687456923897 m³/s \n", - "Current outflux vel = 2.0011107982398624 m/s \n", - "Current pipe pressure = 19.71 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.87523896512633 m\n", - "Volume in reservoir = 19.87523896512633 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5990912714466436 m³/s \n", - "Current outflux vel = 2.036026242446697 m/s \n", - "Current pipe pressure = 19.691 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.859227145404756 m\n", - "Volume in reservoir = 19.859227145404756 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.640990594766461 m³/s \n", - "Current outflux vel = 2.0893741177951326 m/s \n", - "Current pipe pressure = 19.669 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.84176472775811 m\n", - "Volume in reservoir = 19.84176472775811 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6990327627438782 m³/s \n", - "Current outflux vel = 2.163275701326141 m/s \n", - "Current pipe pressure = 19.641 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.82249817397843 m\n", - "Volume in reservoir = 19.82249817397843 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7712266145028497 m³/s \n", - "Current outflux vel = 2.255195768272412 m/s \n", - "Current pipe pressure = 19.608 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.801261840451488 m\n", - "Volume in reservoir = 19.801261840451488 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8500585356789099 m³/s \n", - "Current outflux vel = 2.355567687701217 m/s \n", - "Current pipe pressure = 19.569 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.778202256272877 m\n", - "Volume in reservoir = 19.778202256272877 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9230415092013982 m³/s \n", - "Current outflux vel = 2.4484924956824083 m/s \n", - "Current pipe pressure = 19.526 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.753826035997463 m\n", - "Volume in reservoir = 19.753826035997463 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.975767379674303 m³/s \n", - "Current outflux vel = 2.515625158999095 m/s \n", - "Current pipe pressure = 19.483 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.728942752891133 m\n", - "Volume in reservoir = 19.728942752891133 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.996107679885666 m³/s \n", - "Current outflux vel = 2.5415232335799876 m/s \n", - "Current pipe pressure = 19.443 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.704503996545817 m\n", - "Volume in reservoir = 19.704503996545817 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9783685574052516 m³/s \n", - "Current outflux vel = 2.5189370813490233 m/s \n", - "Current pipe pressure = 19.413 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.681345187960968 m\n", - "Volume in reservoir = 19.681345187960968 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.927185416124052 m³/s \n", - "Current outflux vel = 2.453768681846033 m/s \n", - "Current pipe pressure = 19.394 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.660018414778108 m\n", - "Volume in reservoir = 19.660018414778108 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.853887233174381 m³/s \n", - "Current outflux vel = 2.3604425367572794 m/s \n", - "Current pipe pressure = 19.385 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.640724096284263 m\n", - "Volume in reservoir = 19.640724096284263 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7725465625487744 m³/s \n", - "Current outflux vel = 2.256876378321479 m/s \n", - "Current pipe pressure = 19.384 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.623342942247174 m\n", - "Volume in reservoir = 19.623342942247174 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6959631218950264 m³/s \n", - "Current outflux vel = 2.159367313209249 m/s \n", - "Current pipe pressure = 19.388 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.607545905569502 m\n", - "Volume in reservoir = 19.607545905569502 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.632539086935131 m³/s \n", - "Current outflux vel = 2.0786133238116444 m/s \n", - "Current pipe pressure = 19.391 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.592925709361932 m\n", - "Volume in reservoir = 19.592925709361932 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5854126001918827 m³/s \n", - "Current outflux vel = 2.0186100172857033 m/s \n", - "Current pipe pressure = 19.392 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.579093571873848 m\n", - "Volume in reservoir = 19.579093571873848 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5538484500044354 m³/s \n", - "Current outflux vel = 1.9784212930710856 m/s \n", - "Current pipe pressure = 19.39 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.565715314641277 m\n", - "Volume in reservoir = 19.565715314641277 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5356633091950171 m³/s \n", - "Current outflux vel = 1.9552672526659571 m/s \n", - "Current pipe pressure = 19.385 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.552502667530614 m\n", - "Volume in reservoir = 19.552502667530614 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5290194584109573 m³/s \n", - "Current outflux vel = 1.9468080391183724 m/s \n", - "Current pipe pressure = 19.376 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.539186318324756 m\n", - "Volume in reservoir = 19.539186318324756 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5331566944763544 m³/s \n", - "Current outflux vel = 1.9520757316827404 m/s \n", - "Current pipe pressure = 19.365 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.525502078227085 m\n", - "Volume in reservoir = 19.525502078227085 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5478687104396618 m³/s \n", - "Current outflux vel = 1.9708076521899984 m/s \n", - "Current pipe pressure = 19.35 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.511151825721498 m\n", - "Volume in reservoir = 19.511151825721498 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.574512316742436 m³/s \n", - "Current outflux vel = 2.0047313453490454 m/s \n", - "Current pipe pressure = 19.333 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.495776020009483 m\n", - "Volume in reservoir = 19.495776020009483 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6155442776741793 m³/s \n", - "Current outflux vel = 2.0569748606053695 m/s \n", - "Current pipe pressure = 19.311 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.478973310070387 m\n", - "Volume in reservoir = 19.478973310070387 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.672638159674272 m³/s \n", - "Current outflux vel = 2.1296690489303307 m/s \n", - "Current pipe pressure = 19.285 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.460390768551527 m\n", - "Volume in reservoir = 19.460390768551527 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.743859221874311 m³/s \n", - "Current outflux vel = 2.2203505217414627 m/s \n", - "Current pipe pressure = 19.253 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.43986108547105 m\n", - "Volume in reservoir = 19.43986108547105 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8217849503429804 m³/s \n", - "Current outflux vel = 2.3195686407800675 m/s \n", - "Current pipe pressure = 19.215 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.41752713425312 m\n", - "Volume in reservoir = 19.41752713425312 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8940079536419885 m³/s \n", - "Current outflux vel = 2.4115258246199027 m/s \n", - "Current pipe pressure = 19.173 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.39389115795531 m\n", - "Volume in reservoir = 19.39389115795531 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.946148789083319 m³/s \n", - "Current outflux vel = 2.477913598199333 m/s \n", - "Current pipe pressure = 19.131 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.369761142474196 m\n", - "Volume in reservoir = 19.369761142474196 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9659676784428597 m³/s \n", - "Current outflux vel = 2.5031477918646314 m/s \n", - "Current pipe pressure = 19.093 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.346073167779426 m\n", - "Volume in reservoir = 19.346073167779426 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9483276676011143 m³/s \n", - "Current outflux vel = 2.4806878324913644 m/s \n", - "Current pipe pressure = 19.063 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.323661964205197 m\n", - "Volume in reservoir = 19.323661964205197 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8972712669552025 m³/s \n", - "Current outflux vel = 2.4156808041771476 m/s \n", - "Current pipe pressure = 19.045 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.303076818826845 m\n", - "Volume in reservoir = 19.303076818826845 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8242122733861919 m³/s \n", - "Current outflux vel = 2.322659204466531 m/s \n", - "Current pipe pressure = 19.037 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.28451590340102 m\n", - "Volume in reservoir = 19.28451590340102 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7432006653127887 m³/s \n", - "Current outflux vel = 2.219512021484888 m/s \n", - "Current pipe pressure = 19.036 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.26785957020209 m\n", - "Volume in reservoir = 19.26785957020209 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6669606531833832 m³/s \n", - "Current outflux vel = 2.1224402231506403 m/s \n", - "Current pipe pressure = 19.04 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.25278036843247 m\n", - "Volume in reservoir = 19.25278036843247 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6038162639105749 m³/s \n", - "Current outflux vel = 2.04204228970035 m/s \n", - "Current pipe pressure = 19.043 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.238873739437025 m\n", - "Volume in reservoir = 19.238873739437025 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.556860711650497 m³/s \n", - "Current outflux vel = 1.9822566237179404 m/s \n", - "Current pipe pressure = 19.045 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.225753407877118 m\n", - "Volume in reservoir = 19.225753407877118 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5253672004615844 m³/s \n", - "Current outflux vel = 1.9421578398696573 m/s \n", - "Current pipe pressure = 19.043 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.213086554401542 m\n", - "Volume in reservoir = 19.213086554401542 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5071982602299878 m³/s \n", - "Current outflux vel = 1.919024426680859 m/s \n", - "Current pipe pressure = 19.039 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.20058474561098 m\n", - "Volume in reservoir = 19.20058474561098 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.500577066906518 m³/s \n", - "Current outflux vel = 1.9105940615080808 m/s \n", - "Current pipe pressure = 19.031 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.18797793849185 m\n", - "Volume in reservoir = 19.18797793849185 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5047661419806784 m³/s \n", - "Current outflux vel = 1.9159277575483662 m/s \n", - "Current pipe pressure = 19.02 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.17500128342913 m\n", - "Volume in reservoir = 19.17500128342913 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5195564134220372 m³/s \n", - "Current outflux vel = 1.9347593160248713 m/s \n", - "Current pipe pressure = 19.006 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.161357232558927 m\n", - "Volume in reservoir = 19.161357232558927 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.546255323472871 m³/s \n", - "Current outflux vel = 1.96875342410292 m/s \n", - "Current pipe pressure = 18.989 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.146688004896294 m\n", - "Volume in reservoir = 19.146688004896294 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5872721865190385 m³/s \n", - "Current outflux vel = 2.020977716134287 m/s \n", - "Current pipe pressure = 18.969 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.130594501909993 m\n", - "Volume in reservoir = 19.130594501909993 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6442608332191693 m³/s \n", - "Current outflux vel = 2.0935379147138344 m/s \n", - "Current pipe pressure = 18.943 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.112725248436096 m\n", - "Volume in reservoir = 19.112725248436096 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7153185155889716 m³/s \n", - "Current outflux vel = 2.1840113658642974 m/s \n", - "Current pipe pressure = 18.912 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.092912624803123 m\n", - "Volume in reservoir = 19.092912624803123 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7930932061315523 m³/s \n", - "Current outflux vel = 2.283037177442651 m/s \n", - "Current pipe pressure = 18.875 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.071297360616658 m\n", - "Volume in reservoir = 19.071297360616658 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8652509348037563 m³/s \n", - "Current outflux vel = 2.3749112510463712 m/s \n", - "Current pipe pressure = 18.835 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.048378800181574 m\n", - "Volume in reservoir = 19.048378800181574 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9174425349917954 m³/s \n", - "Current outflux vel = 2.44136366030879 m/s \n", - "Current pipe pressure = 18.793 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.024964490500547 m\n", - "Volume in reservoir = 19.024964490500547 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9373298849522067 m³/s \n", - "Current outflux vel = 2.466685020718373 m/s \n", - "Current pipe pressure = 18.756 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.001990158577254 m\n", - "Volume in reservoir = 19.001990158577254 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9197725681460414 m³/s \n", - "Current outflux vel = 2.44433035066132 m/s \n", - "Current pipe pressure = 18.727 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.98029022761606 m\n", - "Volume in reservoir = 18.98029022761606 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8688111163195242 m³/s \n", - "Current outflux vel = 2.3794442149386823 m/s \n", - "Current pipe pressure = 18.71 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.960411219451736 m\n", - "Volume in reservoir = 18.960411219451736 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7959576201927425 m³/s \n", - "Current outflux vel = 2.286684262697854 m/s \n", - "Current pipe pressure = 18.702 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.942549211922284 m\n", - "Volume in reservoir = 18.942549211922284 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7152352233806218 m³/s \n", - "Current outflux vel = 2.183905314930858 m/s \n", - "Current pipe pressure = 18.702 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.926584327056265 m\n", - "Volume in reservoir = 18.926584327056265 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.639293699088349 m³/s \n", - "Current outflux vel = 2.0872135631144704 m/s \n", - "Current pipe pressure = 18.706 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.912190771343727 m\n", - "Volume in reservoir = 18.912190771343727 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5763815727023678 m³/s \n", - "Current outflux vel = 2.0071113559564626 m/s \n", - "Current pipe pressure = 18.71 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.898966681659132 m\n", - "Volume in reservoir = 18.898966681659132 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.529550479979664 m³/s \n", - "Current outflux vel = 1.947484156778757 m/s \n", - "Current pipe pressure = 18.712 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.88652814035601 m\n", - "Volume in reservoir = 18.88652814035601 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4980870836174782 m³/s \n", - "Current outflux vel = 1.9074237163187457 m/s \n", - "Current pipe pressure = 18.71 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.87454340062085 m\n", - "Volume in reservoir = 18.87454340062085 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4799052864442357 m³/s \n", - "Current outflux vel = 1.8842739331634193 m/s \n", - "Current pipe pressure = 18.706 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.8627234267376 m\n", - "Volume in reservoir = 18.8627234267376 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4732952608547023 m³/s \n", - "Current outflux vel = 1.875857787191114 m/s \n", - "Current pipe pressure = 18.699 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.85079701648704 m\n", - "Volume in reservoir = 18.85079701648704 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4775418296355125 m³/s \n", - "Current outflux vel = 1.8812646864922793 m/s \n", - "Current pipe pressure = 18.689 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.83849823491068 m\n", - "Volume in reservoir = 18.83849823491068 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4924330033779087 m³/s \n", - "Current outflux vel = 1.9002247177686202 m/s \n", - "Current pipe pressure = 18.676 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.82552984782194 m\n", - "Volume in reservoir = 18.82552984782194 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.519220273879799 m³/s \n", - "Current outflux vel = 1.9343313298671447 m/s \n", - "Current pipe pressure = 18.66 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.811535720129474 m\n", - "Volume in reservoir = 18.811535720129474 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5602596639677793 m³/s \n", - "Current outflux vel = 1.9865843042189733 m/s \n", - "Current pipe pressure = 18.64 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.79611901591523 m\n", - "Volume in reservoir = 18.79611901591523 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6171803035928747 m³/s \n", - "Current outflux vel = 2.059057913501264 m/s \n", - "Current pipe pressure = 18.615 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.77892979873861 m\n", - "Volume in reservoir = 18.77892979873861 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6881083783660482 m³/s \n", - "Current outflux vel = 2.149366343134401 m/s \n", - "Current pipe pressure = 18.585 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.759800280429264 m\n", - "Volume in reservoir = 18.759800280429264 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7657601580495343 m³/s \n", - "Current outflux vel = 2.248235659746478 m/s \n", - "Current pipe pressure = 18.549 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.738869179089658 m\n", - "Volume in reservoir = 18.738869179089658 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8378754464589453 m³/s \n", - "Current outflux vel = 2.3400556967293213 m/s \n", - "Current pipe pressure = 18.509 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.716633243942024 m\n", - "Volume in reservoir = 18.716633243942024 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8901284961942326 m³/s \n", - "Current outflux vel = 2.4065863459853025 m/s \n", - "Current pipe pressure = 18.469 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.693899878630667 m\n", - "Volume in reservoir = 18.693899878630667 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9100831662214055 m³/s \n", - "Current outflux vel = 2.4319934209660405 m/s \n", - "Current pipe pressure = 18.433 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.671604620210054 m\n", - "Volume in reservoir = 18.671604620210054 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.892600870794649 m³/s \n", - "Current outflux vel = 2.4097342710959513 m/s \n", - "Current pipe pressure = 18.405 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.650581871668454 m\n", - "Volume in reservoir = 18.650581871668454 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8417152049050178 m³/s \n", - "Current outflux vel = 2.344944629025092 m/s \n", - "Current pipe pressure = 18.387 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.631375713957812 m\n", - "Volume in reservoir = 18.631375713957812 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7690350590030604 m³/s \n", - "Current outflux vel = 2.2524053931455983 m/s \n", - "Current pipe pressure = 18.38 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.61418023747036 m\n", - "Volume in reservoir = 18.61418023747036 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6885654553034914 m³/s \n", - "Current outflux vel = 2.14994831156614 m/s \n", - "Current pipe pressure = 18.381 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.598875457806333 m\n", - "Volume in reservoir = 18.598875457806333 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6128810379134426 m³/s \n", - "Current outflux vel = 2.0535839184248883 m/s \n", - "Current pipe pressure = 18.385 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.585137305737703 m\n", - "Volume in reservoir = 18.585137305737703 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5501571124139346 m³/s \n", - "Current outflux vel = 1.9737213360778927 m/s \n", - "Current pipe pressure = 18.389 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.57256660079006 m\n", - "Volume in reservoir = 18.57256660079006 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.503406931944526 m³/s \n", - "Current outflux vel = 1.9141971575807362 m/s \n", - "Current pipe pressure = 18.391 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.560781648492895 m\n", - "Volume in reservoir = 18.560781648492895 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4719354853406468 m³/s \n", - "Current outflux vel = 1.8741264672346558 m/s \n", - "Current pipe pressure = 18.391 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.54945150476131 m\n", - "Volume in reservoir = 18.54945150476131 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4537134616543401 m³/s \n", - "Current outflux vel = 1.8509254660921495 m/s \n", - "Current pipe pressure = 18.387 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.538286116773374 m\n", - "Volume in reservoir = 18.538286116773374 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4471038309552702 m³/s \n", - "Current outflux vel = 1.842509822909998 m/s \n", - "Current pipe pressure = 18.38 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.527012718418693 m\n", - "Volume in reservoir = 18.527012718418693 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4514133154474935 m³/s \n", - "Current outflux vel = 1.8479968289829198 m/s \n", - "Current pipe pressure = 18.37 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.51536388813897 m\n", - "Volume in reservoir = 18.51536388813897 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4664268741069815 m³/s \n", - "Current outflux vel = 1.867112685575381 m/s \n", - "Current pipe pressure = 18.358 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.50304246028231 m\n", - "Volume in reservoir = 18.50304246028231 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4933338035131936 m³/s \n", - "Current outflux vel = 1.9013716521227675 m/s \n", - "Current pipe pressure = 18.343 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.489693843578983 m\n", - "Volume in reservoir = 18.489693843578983 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5344311126928116 m³/s \n", - "Current outflux vel = 1.9536983713524647 m/s \n", - "Current pipe pressure = 18.323 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.47492348227643 m\n", - "Volume in reservoir = 18.47492348227643 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5913184430046086 m³/s \n", - "Current outflux vel = 2.026129569899856 m/s \n", - "Current pipe pressure = 18.3 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.458383068760927 m\n", - "Volume in reservoir = 18.458383068760927 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6621480087475382 m³/s \n", - "Current outflux vel = 2.116312573940173 m/s \n", - "Current pipe pressure = 18.27 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.439904785199257 m\n", - "Volume in reservoir = 18.439904785199257 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7397024247839628 m³/s \n", - "Current outflux vel = 2.2150579233065915 m/s \n", - "Current pipe pressure = 18.236 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.419625381535447 m\n", - "Volume in reservoir = 18.419625381535447 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8117990859512587 m³/s \n", - "Current outflux vel = 2.3068542433481647 m/s \n", - "Current pipe pressure = 18.197 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.398039465246498 m\n", - "Volume in reservoir = 18.398039465246498 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8641192619607039 m³/s \n", - "Current outflux vel = 2.373470360430894 m/s \n", - "Current pipe pressure = 18.158 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.375954465077864 m\n", - "Volume in reservoir = 18.375954465077864 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8841401893104353 m³/s \n", - "Current outflux vel = 2.398961796854842 m/s \n", - "Current pipe pressure = 18.122 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.35430587721814 m\n", - "Volume in reservoir = 18.35430587721814 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8667258731306313 m³/s \n", - "Current outflux vel = 2.3767892008501943 m/s \n", - "Current pipe pressure = 18.095 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.33392824448415 m\n", - "Volume in reservoir = 18.33392824448415 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8159025402251674 m³/s \n", - "Current outflux vel = 2.3120789235997177 m/s \n", - "Current pipe pressure = 18.078 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.315363682073926 m\n", - "Volume in reservoir = 18.315363682073926 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7433632770945613 m³/s \n", - "Current outflux vel = 2.2197190652358807 m/s \n", - "Current pipe pressure = 18.071 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.298804311745858 m\n", - "Volume in reservoir = 18.298804311745858 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6631132388228167 m³/s \n", - "Current outflux vel = 2.117541543041785 m/s \n", - "Current pipe pressure = 18.072 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.28413016389982 m\n", - "Volume in reservoir = 18.28413016389982 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5876478369876 m³/s \n", - "Current outflux vel = 2.0214560091658575 m/s \n", - "Current pipe pressure = 18.076 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.27101896671529 m\n", - "Volume in reservoir = 18.27101896671529 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5250710972404007 m³/s \n", - "Current outflux vel = 1.9417808295391228 m/s \n", - "Current pipe pressure = 18.081 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.259074217370234 m\n", - "Volume in reservoir = 18.259074217370234 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4783610133041614 m³/s \n", - "Current outflux vel = 1.8823077035336042 m/s \n", - "Current pipe pressure = 18.083 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.24791632415687 m\n", - "Volume in reservoir = 18.24791632415687 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4468455183872373 m³/s \n", - "Current outflux vel = 1.8421809291334765 m/s \n", - "Current pipe pressure = 18.083 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.237214890335498 m\n", - "Volume in reservoir = 18.237214890335498 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4285574881784449 m³/s \n", - "Current outflux vel = 1.8188958858763307 m/s \n", - "Current pipe pressure = 18.08 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.226678460156098 m\n", - "Volume in reservoir = 18.226678460156098 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.421937909820354 m³/s \n", - "Current outflux vel = 1.8104675769413363 m/s \n", - "Current pipe pressure = 18.074 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.216032309561026 m\n", - "Volume in reservoir = 18.216032309561026 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4263157366527885 m³/s \n", - "Current outflux vel = 1.8160415991843948 m/s \n", - "Current pipe pressure = 18.065 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.20500715704167 m\n", - "Volume in reservoir = 18.20500715704167 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4414720503031941 m³/s \n", - "Current outflux vel = 1.8353392170765004 m/s \n", - "Current pipe pressure = 18.053 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.19330567326831 m\n", - "Volume in reservoir = 18.19330567326831 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4685283069296624 m³/s \n", - "Current outflux vel = 1.8697883129458226 m/s \n", - "Current pipe pressure = 18.038 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.18057472052256 m\n", - "Volume in reservoir = 18.18057472052256 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5097168242547705 m³/s \n", - "Current outflux vel = 1.9222311619931598 m/s \n", - "Current pipe pressure = 18.02 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.166422046499157 m\n", - "Volume in reservoir = 18.166422046499157 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5666032104166239 m³/s \n", - "Current outflux vel = 1.9946611584115066 m/s \n", - "Current pipe pressure = 17.997 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.15050106615896 m\n", - "Volume in reservoir = 18.15050106615896 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.63736288693171 m³/s \n", - "Current outflux vel = 2.0847551767231822 m/s \n", - "Current pipe pressure = 17.968 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.13264406772654 m\n", - "Volume in reservoir = 18.13264406772654 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7148431320594133 m³/s \n", - "Current outflux vel = 2.183406088755548 m/s \n", - "Current pipe pressure = 17.934 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.1129858601847 m\n", - "Volume in reservoir = 18.1129858601847 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7869432700825996 m³/s \n", - "Current outflux vel = 2.275206835667532 m/s \n", - "Current pipe pressure = 17.897 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.092019368473316 m\n", - "Volume in reservoir = 18.092019368473316 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8393343053405915 m³/s \n", - "Current outflux vel = 2.3419131735476215 m/s \n", - "Current pipe pressure = 17.858 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.070552164589937 m\n", - "Volume in reservoir = 18.070552164589937 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.859420496390445 m³/s \n", - "Current outflux vel = 2.3674877062954005 m/s \n", - "Current pipe pressure = 17.824 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.05081861342014 m\n", - "Volume in reservoir = 18.05081861342014 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7901150298109418 m³/s \n", - "Current outflux vel = 2.279245245580056 m/s \n", - "Current pipe pressure = 17.805 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.036262256203226 m\n", - "Volume in reservoir = 18.036262256203226 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5830319016186933 m³/s \n", - "Current outflux vel = 2.015578817718224 m/s \n", - "Current pipe pressure = 17.813 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.02999892177117 m\n", - "Volume in reservoir = 18.02999892177117 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2512314883828066 m³/s \n", - "Current outflux vel = 1.5931174106268247 m/s \n", - "Current pipe pressure = 17.852 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.03456397811833 m\n", - "Volume in reservoir = 18.03456397811833 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.8179199399526033 m³/s \n", - "Current outflux vel = 1.0414080119750642 m/s \n", - "Current pipe pressure = 17.918 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.051646450466666 m\n", - "Volume in reservoir = 18.051646450466666 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.31701183806814254 m³/s \n", - "Current outflux vel = 0.40363200837753893 m/s \n", - "Current pipe pressure = 17.99 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.081802706178994 m\n", - "Volume in reservoir = 18.081802706178994 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.2060945887163009 m³/s \n", - "Current outflux vel = -0.26240778030952355 m/s \n", - "Current pipe pressure = 18.05 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.124235567246025 m\n", - "Volume in reservoir = 18.124235567246025 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.6970564864101311 m³/s \n", - "Current outflux vel = -0.8875198834115274 m/s \n", - "Current pipe pressure = 18.085 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.176727775995772 m\n", - "Volume in reservoir = 18.176727775995772 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.0990886509357425 m³/s \n", - "Current outflux vel = -1.399403133541009 m/s \n", - "Current pipe pressure = 18.095 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.235814027824816 m\n", - "Volume in reservoir = 18.235814027824816 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3624298822265066 m³/s \n", - "Current outflux vel = -1.7346996029796586 m/s \n", - "Current pipe pressure = 18.095 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.297178384773527 m\n", - "Volume in reservoir = 18.297178384773527 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.453253450076634 m³/s \n", - "Current outflux vel = -1.8503397611603782 m/s \n", - "Current pipe pressure = 18.107 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.356208194078697 m\n", - "Volume in reservoir = 18.356208194078697 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3598348096292643 m³/s \n", - "Current outflux vel = -1.731395453927391 m/s \n", - "Current pipe pressure = 18.15 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.408574291937338 m\n", - "Volume in reservoir = 18.408574291937338 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.093534565648534 m³/s \n", - "Current outflux vel = -1.3923314525185033 m/s \n", - "Current pipe pressure = 18.228 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.45078679147777 m\n", - "Volume in reservoir = 18.45078679147777 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.6878105115076991 m³/s \n", - "Current outflux vel = -0.8757475425361223 m/s \n", - "Current pipe pressure = 18.328 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.48059678496199 m\n", - "Volume in reservoir = 18.48059678496199 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.19210994035446577 m³/s \n", - "Current outflux vel = -0.24460197299601927 m/s \n", - "Current pipe pressure = 18.423 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.49717869427269 m\n", - "Volume in reservoir = 18.49717869427269 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.336773769981826 m³/s \n", - "Current outflux vel = 0.4287936815704046 m/s \n", - "Current pipe pressure = 18.484 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.50108909350546 m\n", - "Volume in reservoir = 18.50108909350546 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.8435116962947237 m³/s \n", - "Current outflux vel = 1.0739924481690788 m/s \n", - "Current pipe pressure = 18.495 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.49405821338431 m\n", - "Volume in reservoir = 18.49405821338431 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2811896492084378 m³/s \n", - "Current outflux vel = 1.631261325677554 m/s \n", - "Current pipe pressure = 18.453 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.47868568850953 m\n", - "Volume in reservoir = 18.47868568850953 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.615017619317196 m³/s \n", - "Current outflux vel = 2.0563042983586928 m/s \n", - "Current pipe pressure = 18.375 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.458130925648224 m\n", - "Volume in reservoir = 18.458130925648224 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.822544444031355 m³/s \n", - "Current outflux vel = 2.3205356581780827 m/s \n", - "Current pipe pressure = 18.286 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.43584469762318 m\n", - "Volume in reservoir = 18.43584469762318 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8920365710253189 m³/s \n", - "Current outflux vel = 2.4090157823145555 m/s \n", - "Current pipe pressure = 18.21 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.415357556517197 m\n", - "Volume in reservoir = 18.415357556517197 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8202325721428836 m³/s \n", - "Current outflux vel = 2.3175920914673194 m/s \n", - "Current pipe pressure = 18.168 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.40009593702437 m\n", - "Volume in reservoir = 18.40009593702437 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.611254072524319 m³/s \n", - "Current outflux vel = 2.051512401753541 m/s \n", - "Current pipe pressure = 18.168 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.393175153344075 m\n", - "Volume in reservoir = 18.393175153344075 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.277538502969119 m³/s \n", - "Current outflux vel = 1.6266125419020425 m/s \n", - "Current pipe pressure = 18.209 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.397142187023857 m\n", - "Volume in reservoir = 18.397142187023857 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.8418483330690767 m³/s \n", - "Current outflux vel = 1.0718745883329268 m/s \n", - "Current pipe pressure = 18.276 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.413683517232798 m\n", - "Volume in reservoir = 18.413683517232798 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.3386632899446476 m³/s \n", - "Current outflux vel = 0.43119949310763556 m/s \n", - "Current pipe pressure = 18.35 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.44333779935795 m\n", - "Volume in reservoir = 18.44333779935795 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.18601594276896724 m³/s \n", - "Current outflux vel = -0.23684285428464194 m/s \n", - "Current pipe pressure = 18.411 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.4852814093454 m\n", - "Volume in reservoir = 18.4852814093454 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.6775013652834435 m³/s \n", - "Current outflux vel = -0.8626215298909428 m/s \n", - "Current pipe pressure = 18.447 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.537273998827136 m\n", - "Volume in reservoir = 18.537273998827136 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.0791363810954313 m³/s \n", - "Current outflux vel = -1.373999114573098 m/s \n", - "Current pipe pressure = 18.458 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.595842179101755 m\n", - "Volume in reservoir = 18.595842179101755 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3417565045590167 m³/s \n", - "Current outflux vel = -1.7083774410101658 m/s \n", - "Current pipe pressure = 18.459 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.65668002952095 m\n", - "Volume in reservoir = 18.65668002952095 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.4322538543883647 m³/s \n", - "Current outflux vel = -1.8236022455066234 m/s \n", - "Current pipe pressure = 18.471 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.715192999145728 m\n", - "Volume in reservoir = 18.715192999145728 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3392231403676198 m³/s \n", - "Current outflux vel = -1.705151861540463 m/s \n", - "Current pipe pressure = 18.514 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.767061873563428 m\n", - "Volume in reservoir = 18.767061873563428 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.0736984444416389 m³/s \n", - "Current outflux vel = -1.3670753185837248 m/s \n", - "Current pipe pressure = 18.591 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.808788346564487 m\n", - "Volume in reservoir = 18.808788346564487 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.6684063461855375 m³/s \n", - "Current outflux vel = -0.8510413919153673 m/s \n", - "Current pipe pressure = 18.69 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.838099671431443 m\n", - "Volume in reservoir = 18.838099671431443 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.17218244067907934 m³/s \n", - "Current outflux vel = -0.21922949238162015 m/s \n", - "Current pipe pressure = 18.783 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.854143357842407 m\n", - "Volume in reservoir = 18.854143357842407 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.35829429880850167 m³/s \n", - "Current outflux vel = 0.45619446989614104 m/s \n", - "Current pipe pressure = 18.842 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.857458958395924 m\n", - "Volume in reservoir = 18.857458958395924 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.8673018305381948 m³/s \n", - "Current outflux vel = 1.1042829878624243 m/s \n", - "Current pipe pressure = 18.851 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.849775565464082 m\n", - "Volume in reservoir = 18.849775565464082 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.307293112037996 m³/s \n", - "Current outflux vel = 1.664497286806672 m/s \n", - "Current pipe pressure = 18.806 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.833706205645232 m\n", - "Volume in reservoir = 18.833706205645232 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6428971718296088 m³/s \n", - "Current outflux vel = 2.0918016471070175 m/s \n", - "Current pipe pressure = 18.726 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.812429601761128 m\n", - "Volume in reservoir = 18.812429601761128 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8514261874939983 m³/s \n", - "Current outflux vel = 2.3573090360756166 m/s \n", - "Current pipe pressure = 18.634 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.789415060501657 m\n", - "Volume in reservoir = 18.789415060501657 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9211780978486772 m³/s \n", - "Current outflux vel = 2.4461199266600158 m/s \n", - "Current pipe pressure = 18.556 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.768210663885412 m\n", - "Volume in reservoir = 18.768210663885412 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8489319432884488 m³/s \n", - "Current outflux vel = 2.3541332657188843 m/s \n", - "Current pipe pressure = 18.513 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.752260592785017 m\n", - "Volume in reservoir = 18.752260592785017 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.638801058238996 m³/s \n", - "Current outflux vel = 2.086586313303722 m/s \n", - "Current pipe pressure = 18.513 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.744698101752228 m\n", - "Volume in reservoir = 18.744698101752228 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.303215217292789 m³/s \n", - "Current outflux vel = 1.6593051499578069 m/s \n", - "Current pipe pressure = 18.554 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.748082038984155 m\n", - "Volume in reservoir = 18.748082038984155 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.8651796608212385 m³/s \n", - "Current outflux vel = 1.1015809574581563 m/s \n", - "Current pipe pressure = 18.622 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.764096430243136 m\n", - "Volume in reservoir = 18.764096430243136 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.35974660947305465 m³/s \n", - "Current outflux vel = 0.4580436092654905 m/s \n", - "Current pipe pressure = 18.698 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.793262363996806 m\n", - "Volume in reservoir = 18.793262363996806 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.1664817319827179 m³/s \n", - "Current outflux vel = -0.2119711246363971 m/s \n", - "Current pipe pressure = 18.761 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.834729861127933 m\n", - "Volume in reservoir = 18.834729861127933 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.65847032330755 m³/s \n", - "Current outflux vel = -0.8383904546697203 m/s \n", - "Current pipe pressure = 18.798 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.886235679964138 m\n", - "Volume in reservoir = 18.886235679964138 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.0596954878910287 m³/s \n", - "Current outflux vel = -1.3492462005602797 m/s \n", - "Current pipe pressure = 18.81 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.94429845536237 m\n", - "Volume in reservoir = 18.94429845536237 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3215861352260736 m³/s \n", - "Current outflux vel = -1.6826957291435491 m/s \n", - "Current pipe pressure = 18.811 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.00462258403588 m\n", - "Volume in reservoir = 19.00462258403588 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.4117612710573393 m³/s \n", - "Current outflux vel = -1.7975102780357815 m/s \n", - "Current pipe pressure = 18.824 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.062631301954283 m\n", - "Volume in reservoir = 19.062631301954283 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3191105520548732 m³/s \n", - "Current outflux vel = -1.679543718753696 m/s \n", - "Current pipe pressure = 18.866 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.114015663528782 m\n", - "Volume in reservoir = 19.114015663528782 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.0543669019122317 m³/s \n", - "Current outflux vel = -1.3424616341745539 m/s \n", - "Current pipe pressure = 18.943 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.15526908490371 m\n", - "Volume in reservoir = 19.15526908490371 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.6495183692313966 m³/s \n", - "Current outflux vel = -0.8269924727373088 m/s \n", - "Current pipe pressure = 19.04 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.184095206880766 m\n", - "Volume in reservoir = 19.184095206880766 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.15279188989047032 m³/s \n", - "Current outflux vel = -0.19454067632336755 m/s \n", - "Current pipe pressure = 19.131 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.19961475477844 m\n", - "Volume in reservoir = 19.19961475477844 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.37925215342735236 m³/s \n", - "Current outflux vel = 0.4828788391696722 m/s \n", - "Current pipe pressure = 19.189 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.20235041254316 m\n", - "Volume in reservoir = 19.20235041254316 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.8904980124186597 m³/s \n", - "Current outflux vel = 1.1338172839195015 m/s \n", - "Current pipe pressure = 19.195 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.1940303946784 m\n", - "Volume in reservoir = 19.1940303946784 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.332761084302397 m³/s \n", - "Current outflux vel = 1.6969241162179256 m/s \n", - "Current pipe pressure = 19.148 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.177281851030763 m\n", - "Volume in reservoir = 19.177281851030763 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6700705943009293 m³/s \n", - "Current outflux vel = 2.126399923163298 m/s \n", - "Current pipe pressure = 19.065 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.155302187937018 m\n", - "Volume in reservoir = 19.155302187937018 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8795565508937635 m³/s \n", - "Current outflux vel = 2.393125727163968 m/s \n", - "Current pipe pressure = 18.97 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.131578428476914 m\n", - "Volume in reservoir = 19.131578428476914 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9495557135844919 m³/s \n", - "Current outflux vel = 2.482251429200154 m/s \n", - "Current pipe pressure = 18.891 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.1096754771615 m\n", - "Volume in reservoir = 19.1096754771615 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.876883187824323 m³/s \n", - "Current outflux vel = 2.389721895586522 m/s \n", - "Current pipe pressure = 18.847 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.093054459371405 m\n", - "Volume in reservoir = 19.093054459371405 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.665647770413698 m³/s \n", - "Current outflux vel = 2.1207686088906756 m/s \n", - "Current pipe pressure = 18.847 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.08486595355734 m\n", - "Volume in reservoir = 19.08486595355734 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3282641621695606 m³/s \n", - "Current outflux vel = 1.6911984571288037 m/s \n", - "Current pipe pressure = 18.888 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.087681431372324 m\n", - "Volume in reservoir = 19.087681431372324 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.8879254521374415 m³/s \n", - "Current outflux vel = 1.1305417984382395 m/s \n", - "Current pipe pressure = 18.957 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.103182755135677 m\n", - "Volume in reservoir = 19.103182755135677 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.38027505137091916 m³/s \n", - "Current outflux vel = 0.48418123328164975 m/s \n", - "Current pipe pressure = 19.034 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.13187358242454 m\n", - "Volume in reservoir = 19.13187358242454 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.14747669719853262 m³/s \n", - "Current outflux vel = -0.18777316280010514 m/s \n", - "Current pipe pressure = 19.099 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.172877663295864 m\n", - "Volume in reservoir = 19.172877663295864 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.6399458478103827 m³/s \n", - "Current outflux vel = -0.8148043599212493 m/s \n", - "Current pipe pressure = 19.137 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.22390904203881 m\n", - "Volume in reservoir = 19.22390904203881 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.040745505274048 m³/s \n", - "Current outflux vel = -1.3251183333202956 m/s \n", - "Current pipe pressure = 19.15 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.281478514763187 m\n", - "Volume in reservoir = 19.281478514763187 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.301896554682224 m³/s \n", - "Current outflux vel = -1.657626176575872 m/s \n", - "Current pipe pressure = 19.152 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.34130114726194 m\n", - "Volume in reservoir = 19.34130114726194 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3917537582313633 m³/s \n", - "Current outflux vel = -1.7720359215139527 m/s \n", - "Current pipe pressure = 19.165 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.398817640784916 m\n", - "Volume in reservoir = 19.398817640784916 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.2994750503405734 m³/s \n", - "Current outflux vel = -1.6545430214903345 m/s \n", - "Current pipe pressure = 19.208 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.449729689138614 m\n", - "Volume in reservoir = 19.449729689138614 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.0355198732796616 m³/s \n", - "Current outflux vel = -1.3184648520188096 m/s \n", - "Current pipe pressure = 19.283 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.490522601560237 m\n", - "Volume in reservoir = 19.490522601560237 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.631129566577593 m³/s \n", - "Current outflux vel = -0.8035791220181551 m/s \n", - "Current pipe pressure = 19.379 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.518876612992937 m\n", - "Volume in reservoir = 19.518876612992937 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.13392352333979649 m³/s \n", - "Current outflux vel = -0.17051672588649142 m/s \n", - "Current pipe pressure = 19.469 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.533885783526316 m\n", - "Volume in reservoir = 19.533885783526316 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.39966019362765637 m³/s \n", - "Current outflux vel = 0.5088631629832442 m/s \n", - "Current pipe pressure = 19.524 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.536056073094514 m\n", - "Volume in reservoir = 19.536056073094514 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.9131114617993822 m³/s \n", - "Current outflux vel = 1.1626096219139044 m/s \n", - "Current pipe pressure = 19.528 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.527115181285946 m\n", - "Volume in reservoir = 19.527115181285946 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3575990324102931 m³/s \n", - "Current outflux vel = 1.728548773958979 m/s \n", - "Current pipe pressure = 19.479 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.509705017631433 m\n", - "Volume in reservoir = 19.509705017631433 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6965413770834266 m³/s \n", - "Current outflux vel = 2.160103570582068 m/s \n", - "Current pipe pressure = 19.393 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.487040963020533 m\n", - "Volume in reservoir = 19.487040963020533 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9069400929567297 m³/s \n", - "Current outflux vel = 2.4279915357934554 m/s \n", - "Current pipe pressure = 19.296 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.46262695882498 m\n", - "Volume in reservoir = 19.46262695882498 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.977174268313604 m³/s \n", - "Current outflux vel = 2.5174164652496915 m/s \n", - "Current pipe pressure = 19.214 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.44004403652403 m\n", - "Volume in reservoir = 19.44004403652403 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.90409097298703 m³/s \n", - "Current outflux vel = 2.424363923580339 m/s \n", - "Current pipe pressure = 19.169 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.422769483672894 m\n", - "Volume in reservoir = 19.422769483672894 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6917979220876354 m³/s \n", - "Current outflux vel = 2.154064016102755 m/s \n", - "Current pipe pressure = 19.169 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.413970529913847 m\n", - "Volume in reservoir = 19.413970529913847 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3526903497236773 m³/s \n", - "Current outflux vel = 1.722298845049823 m/s \n", - "Current pipe pressure = 19.211 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.416231897502904 m\n", - "Volume in reservoir = 19.416231897502904 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.9100972067300397 m³/s \n", - "Current outflux vel = 1.158771753161699 m/s \n", - "Current pipe pressure = 19.281 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.431233695153008 m\n", - "Volume in reservoir = 19.431233695153008 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.4002618072076471 m³/s \n", - "Current outflux vel = 0.5096291611839381 m/s \n", - "Current pipe pressure = 19.36 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.45946227993457 m\n", - "Volume in reservoir = 19.45946227993457 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.12898578433648575 m³/s \n", - "Current outflux vel = -0.16422980132589501 m/s \n", - "Current pipe pressure = 19.425 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.50001520961341 m\n", - "Volume in reservoir = 19.50001520961341 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.6219108113152736 m³/s \n", - "Current outflux vel = -0.7918414382649347 m/s \n", - "Current pipe pressure = 19.465 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.550583979320194 m\n", - "Volume in reservoir = 19.550583979320194 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.0222666790665658 m³/s \n", - "Current outflux vel = -1.301590361052641 m/s \n", - "Current pipe pressure = 19.479 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.60767176389382 m\n", - "Volume in reservoir = 19.60767176389382 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.282668569961559 m³/s \n", - "Current outflux vel = -1.6331443460639576 m/s \n", - "Current pipe pressure = 19.482 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.667004578964736 m\n", - "Volume in reservoir = 19.667004578964736 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3722098188684477 m³/s \n", - "Current outflux vel = -1.7471518050571824 m/s \n", - "Current pipe pressure = 19.496 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.724040391039285 m\n", - "Volume in reservoir = 19.724040391039285 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.2802976376999995 m³/s \n", - "Current outflux vel = -1.6301255813506517 m/s \n", - "Current pipe pressure = 19.537 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.774491834608128 m\n", - "Volume in reservoir = 19.774491834608128 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.0171380407359516 m³/s \n", - "Current outflux vel = -1.2950603759194586 m/s \n", - "Current pipe pressure = 19.612 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.81483635795861 m\n", - "Volume in reservoir = 19.81483635795861 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.6132232684119483 m³/s \n", - "Current outflux vel = -0.7807801150938375 m/s \n", - "Current pipe pressure = 19.707 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.842730982625792 m\n", - "Volume in reservoir = 19.842730982625792 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.11556274840280699 m³/s \n", - "Current outflux vel = -0.14713906116473413 m/s \n", - "Current pipe pressure = 19.795 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.857243213870117 m\n", - "Volume in reservoir = 19.857243213870117 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.419531280981672 m³/s \n", - "Current outflux vel = 0.5341638171992636 m/s \n", - "Current pipe pressure = 19.849 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.858862424044183 m\n", - "Volume in reservoir = 19.858862424044183 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.9351535821716467 m³/s \n", - "Current outflux vel = 1.190674521221684 m/s \n", - "Current pipe pressure = 19.85 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.849316385238758 m\n", - "Volume in reservoir = 19.849316385238758 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3818079112076893 m³/s \n", - "Current outflux vel = 1.7593724757775246 m/s \n", - "Current pipe pressure = 19.798 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.83126205500761 m\n", - "Volume in reservoir = 19.83126205500761 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7223139348448715 m³/s \n", - "Current outflux vel = 2.192918210292911 m/s \n", - "Current pipe pressure = 19.71 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.8079321378974 m\n", - "Volume in reservoir = 19.8079321378974 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9335823519755162 m³/s \n", - "Current outflux vel = 2.4619135135372514 m/s \n", - "Current pipe pressure = 19.61 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.78284671679747 m\n", - "Volume in reservoir = 19.78284671679747 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.00403957641688 m³/s \n", - "Current outflux vel = 2.551622437908277 m/s \n", - "Current pipe pressure = 19.527 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.759590554781887 m\n", - "Volume in reservoir = 19.759590554781887 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9310293979184778 m³/s \n", - "Current outflux vel = 2.458662991475938 m/s \n", - "Current pipe pressure = 19.481 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.741655589564484 m\n", - "Volume in reservoir = 19.741655589564484 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7182231305759708 m³/s \n", - "Current outflux vel = 2.187709636527975 m/s \n", - "Current pipe pressure = 19.48 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.73222400117161 m\n", - "Volume in reservoir = 19.73222400117161 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3780043857287037 m³/s \n", - "Current outflux vel = 1.7545296767282723 m/s \n", - "Current pipe pressure = 19.523 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.73389247141869 m\n", - "Volume in reservoir = 19.73389247141869 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.9338211579195913 m³/s \n", - "Current outflux vel = 1.188978025973603 m/s \n", - "Current pipe pressure = 19.594 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.748338529359025 m\n", - "Volume in reservoir = 19.748338529359025 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.42249798506857755 m³/s \n", - "Current outflux vel = 0.5379411421602392 m/s \n", - "Current pipe pressure = 19.674 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.776030287807817 m\n", - "Volume in reservoir = 19.776030287807817 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.10751062895744823 m³/s \n", - "Current outflux vel = -0.13688678426797238 m/s \n", - "Current pipe pressure = 19.741 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.816038802565522 m\n", - "Volume in reservoir = 19.816038802565522 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.6001456473226694 m³/s \n", - "Current outflux vel = -0.7641291707719051 m/s \n", - "Current pipe pressure = 19.782 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.86603352090399 m\n", - "Volume in reservoir = 19.86603352090399 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9993322217519535 m³/s \n", - "Current outflux vel = -1.2723893030626359 m/s \n", - "Current pipe pressure = 19.797 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.92251105880942 m\n", - "Volume in reservoir = 19.92251105880942 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.2583020286608746 m³/s \n", - "Current outflux vel = -1.6021199021115038 m/s \n", - "Current pipe pressure = 19.801 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.98120958399303 m\n", - "Volume in reservoir = 19.98120958399303 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3468922960307095 m³/s \n", - "Current outflux vel = -1.7149165338054386 m/s \n", - "Current pipe pressure = 19.815 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.037605214722163 m\n", - "Volume in reservoir = 20.037605214722163 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.254746582718209 m³/s \n", - "Current outflux vel = -1.5975929677381335 m/s \n", - "Current pipe pressure = 19.857 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.087423445030744 m\n", - "Volume in reservoir = 20.087423445030744 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9918583790529591 m³/s \n", - "Current outflux vel = -1.2628733109871462 m/s \n", - "Current pipe pressure = 19.931 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.12713658715859 m\n", - "Volume in reservoir = 20.12713658715859 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.5880027254224272 m³/s \n", - "Current outflux vel = -0.748668322419886 m/s \n", - "Current pipe pressure = 20.024 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.15438164250314 m\n", - "Volume in reservoir = 20.15438164250314 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.08959767324627856 m³/s \n", - "Current outflux vel = -0.11407930069342158 m/s \n", - "Current pipe pressure = 20.11 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.16820280469656 m\n", - "Volume in reservoir = 20.16820280469656 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.4471656905131347 m³/s \n", - "Current outflux vel = 0.5693490402101283 m/s \n", - "Current pipe pressure = 20.161 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.169075714647956 m\n", - "Volume in reservoir = 20.169075714647956 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.9650041590667633 m³/s \n", - "Current outflux vel = 1.2286814561577042 m/s \n", - "Current pipe pressure = 20.159 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.158730745740623 m\n", - "Volume in reservoir = 20.158730745740623 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.413769558002925 m³/s \n", - "Current outflux vel = 1.8000673083920766 m/s \n", - "Current pipe pressure = 20.104 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.139839666710113 m\n", - "Volume in reservoir = 20.139839666710113 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7557930615136996 m³/s \n", - "Current outflux vel = 2.2355451582908605 m/s \n", - "Current pipe pressure = 20.012 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.115649310605942 m\n", - "Volume in reservoir = 20.115649310605942 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9680125263719068 m³/s \n", - "Current outflux vel = 2.505751373110864 m/s \n", - "Current pipe pressure = 19.91 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.089690515498116 m\n", - "Volume in reservoir = 20.089690515498116 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.038989226812935 m³/s \n", - "Current outflux vel = 2.596121714867203 m/s \n", - "Current pipe pressure = 19.825 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.06556890399616 m\n", - "Volume in reservoir = 20.06556890399616 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.965663074429606 m³/s \n", - "Current outflux vel = 2.502759957989472 m/s \n", - "Current pipe pressure = 19.777 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.046785524762324 m\n", - "Volume in reservoir = 20.046785524762324 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7521756286737717 m³/s \n", - "Current outflux vel = 2.230939299748641 m/s \n", - "Current pipe pressure = 19.776 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.03653356356101 m\n", - "Volume in reservoir = 20.03653356356101 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.410834445470898 m³/s \n", - "Current outflux vel = 1.796330207048052 m/s \n", - "Current pipe pressure = 19.819 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.03742106333072 m\n", - "Volume in reservoir = 20.03742106333072 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.9650734732891412 m³/s \n", - "Current outflux vel = 1.2287697097666483 m/s \n", - "Current pipe pressure = 19.891 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.051125206433756 m\n", - "Volume in reservoir = 20.051125206433756 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.4521852265586152 m³/s \n", - "Current outflux vel = 0.5757401119994576 m/s \n", - "Current pipe pressure = 19.973 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.078098427412318 m\n", - "Volume in reservoir = 20.078098427412318 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.07876464758528043 m³/s \n", - "Current outflux vel = -0.10028626403270799 m/s \n", - "Current pipe pressure = 20.042 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.117388531064684 m\n", - "Volume in reservoir = 20.117388531064684 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.5714218824708304 m³/s \n", - "Current outflux vel = -0.7275569374888697 m/s \n", - "Current pipe pressure = 20.085 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.16664631645887 m\n", - "Volume in reservoir = 20.16664631645887 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9698862949866293 m³/s \n", - "Current outflux vel = -1.2348975846736496 m/s \n", - "Current pipe pressure = 20.101 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.222364790513073 m\n", - "Volume in reservoir = 20.222364790513073 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.2279878099850285 m³/s \n", - "Current outflux vel = -1.5635226401256672 m/s \n", - "Current pipe pressure = 20.106 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.280295871683045 m\n", - "Volume in reservoir = 20.280295871683045 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3162536369440658 m³/s \n", - "Current outflux vel = -1.6759061814586647 m/s \n", - "Current pipe pressure = 20.121 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.335934327753492 m\n", - "Volume in reservoir = 20.335934327753492 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.2245200090515451 m³/s \n", - "Current outflux vel = -1.5591072988438868 m/s \n", - "Current pipe pressure = 20.162 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.385019248245065 m\n", - "Volume in reservoir = 20.385019248245065 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.962577799984379 m³/s \n", - "Current outflux vel = -1.2255921198242852 m/s \n", - "Current pipe pressure = 20.235 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.42401890161463 m\n", - "Volume in reservoir = 20.42401890161463 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.5594993923809106 m³/s \n", - "Current outflux vel = -0.7123767516346708 m/s \n", - "Current pipe pressure = 20.326 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.45055079704061 m\n", - "Volume in reservoir = 20.45055079704061 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.061089122343714976 m³/s \n", - "Current outflux vel = -0.07778108632118232 m/s \n", - "Current pipe pressure = 20.409 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.463635830074928 m\n", - "Volume in reservoir = 20.463635830074928 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.4766025374271511 m³/s \n", - "Current outflux vel = 0.6068291977733692 m/s \n", - "Current pipe pressure = 20.457 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.463735474437197 m\n", - "Volume in reservoir = 20.463735474437197 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.9959338597617111 m³/s \n", - "Current outflux vel = 1.2680623741893344 m/s \n", - "Current pipe pressure = 20.453 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.45258287037658 m\n", - "Volume in reservoir = 20.45258287037658 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4460802168144178 m³/s \n", - "Current outflux vel = 1.8412065169073148 m/s \n", - "Current pipe pressure = 20.394 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.432862546089734 m\n", - "Volume in reservoir = 20.432862546089734 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7889729232896678 m³/s \n", - "Current outflux vel = 2.27779107039287 m/s \n", - "Current pipe pressure = 20.299 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.407833100498447 m\n", - "Volume in reservoir = 20.407833100498447 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0015894112992574 m³/s \n", - "Current outflux vel = 2.5485027907893887 m/s \n", - "Current pipe pressure = 20.194 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.381034200014835 m\n", - "Volume in reservoir = 20.381034200014835 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0726084832016296 m³/s \n", - "Current outflux vel = 2.638927081565879 m/s \n", - "Current pipe pressure = 20.106 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.35607965744544 m\n", - "Volume in reservoir = 20.35607965744544 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9989959754876059 m³/s \n", - "Current outflux vel = 2.5452007257572618 m/s \n", - "Current pipe pressure = 20.057 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.33647862792642 m\n", - "Volume in reservoir = 20.33647862792642 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7848971907647722 m³/s \n", - "Current outflux vel = 2.2726016865684096 m/s \n", - "Current pipe pressure = 20.056 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.325434750490153 m\n", - "Volume in reservoir = 20.325434750490153 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4425259009018065 m³/s \n", - "Current outflux vel = 1.8366810213328966 m/s \n", - "Current pipe pressure = 20.099 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.325567107309336 m\n", - "Volume in reservoir = 20.325567107309336 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.9952924291584263 m³/s \n", - "Current outflux vel = 1.2672456793800289 m/s \n", - "Current pipe pressure = 20.173 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.338554253889836 m\n", - "Volume in reservoir = 20.338554253889836 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.4808755964992434 m³/s \n", - "Current outflux vel = 0.6122698255609464 m/s \n", - "Current pipe pressure = 20.257 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.364833529076822 m\n", - "Volume in reservoir = 20.364833529076822 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.051001638328260226 m³/s \n", - "Current outflux vel = -0.06493730276582148 m/s \n", - "Current pipe pressure = 20.327 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.40342984555661 m\n", - "Volume in reservoir = 20.40342984555661 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.5436813514314893 m³/s \n", - "Current outflux vel = -0.6922365963776275 m/s \n", - "Current pipe pressure = 20.372 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.45197562535787 m\n", - "Volume in reservoir = 20.45197562535787 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9414345199909748 m³/s \n", - "Current outflux vel = -1.1986716596312752 m/s \n", - "Current pipe pressure = 20.39 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.506960656225438 m\n", - "Volume in reservoir = 20.506960656225438 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.198694323228427 m³/s \n", - "Current outflux vel = -1.5262250143839866 m/s \n", - "Current pipe pressure = 20.396 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.56415012350462 m\n", - "Volume in reservoir = 20.56415012350462 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.2866433552344052 m³/s \n", - "Current outflux vel = -1.6382051998551763 m/s \n", - "Current pipe pressure = 20.411 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.61905691712713 m\n", - "Volume in reservoir = 20.61905691712713 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.1953090168393654 m³/s \n", - "Current outflux vel = -1.5219147084183886 m/s \n", - "Current pipe pressure = 20.452 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.667433257801612 m\n", - "Volume in reservoir = 20.667433257801612 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9342821296075087 m³/s \n", - "Current outflux vel = -1.1895649533556627 m/s \n", - "Current pipe pressure = 20.524 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.705743779208387 m\n", - "Volume in reservoir = 20.705743779208387 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.5319671118817462 m³/s \n", - "Current outflux vel = -0.6773215633463938 m/s \n", - "Current pipe pressure = 20.612 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.73158680828424 m\n", - "Volume in reservoir = 20.73158680828424 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.03355031459273356 m³/s \n", - "Current outflux vel = -0.04271758727777356 m/s \n", - "Current pipe pressure = 20.693 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.74396036260806 m\n", - "Volume in reservoir = 20.74396036260806 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.505054213407485 m³/s \n", - "Current outflux vel = 0.6430549967455219 m/s \n", - "Current pipe pressure = 20.739 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.74331253680738 m\n", - "Volume in reservoir = 20.74331253680738 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.0258321229387928 m³/s \n", - "Current outflux vel = 1.306130025185294 m/s \n", - "Current pipe pressure = 20.731 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.731380797775472 m\n", - "Volume in reservoir = 20.731380797775472 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4772509282201358 m³/s \n", - "Current outflux vel = 1.8808942993066022 m/s \n", - "Current pipe pressure = 20.669 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.710861896569124 m\n", - "Volume in reservoir = 20.710861896569124 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8209259122234516 m³/s \n", - "Current outflux vel = 2.3184748794758483 m/s \n", - "Current pipe pressure = 20.571 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.6850253142105 m\n", - "Volume in reservoir = 20.6850253142105 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0338879043762317 m³/s \n", - "Current outflux vel = 2.5896265094103477 m/s \n", - "Current pipe pressure = 20.463 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.65741859767929 m\n", - "Volume in reservoir = 20.65741859767929 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.104935804208887 m³/s \n", - "Current outflux vel = 2.6800875050476667 m/s \n", - "Current pipe pressure = 20.373 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.63166291535004 m\n", - "Volume in reservoir = 20.63166291535004 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0310568335543175 m³/s \n", - "Current outflux vel = 2.5860218780859405 m/s \n", - "Current pipe pressure = 20.323 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.611274617449812 m\n", - "Volume in reservoir = 20.611274617449812 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.816403093433523 m³/s \n", - "Current outflux vel = 2.31271624773884 m/s \n", - "Current pipe pressure = 20.321 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.599466947967148 m\n", - "Volume in reservoir = 20.599466947967148 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4730920408683201 m³/s \n", - "Current outflux vel = 1.8755990394681716 m/s \n", - "Current pipe pressure = 20.365 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.598869576762358 m\n", - "Volume in reservoir = 20.598869576762358 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.0244945136704753 m³/s \n", - "Current outflux vel = 1.304426928169468 m/s \n", - "Current pipe pressure = 20.44 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.61116395363133 m\n", - "Volume in reservoir = 20.61116395363133 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.5085967375632623 m³/s \n", - "Current outflux vel = 0.6475654785888371 m/s \n", - "Current pipe pressure = 20.525 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.636773120578837 m\n", - "Volume in reservoir = 20.636773120578837 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.02419154619738606 m³/s \n", - "Current outflux vel = -0.030801633266799484 m/s \n", - "Current pipe pressure = 20.598 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.674699438939307 m\n", - "Volume in reservoir = 20.674699438939307 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.5168908592086948 m³/s \n", - "Current outflux vel = -0.6581258822566457 m/s \n", - "Current pipe pressure = 20.644 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.72255738068879 m\n", - "Volume in reservoir = 20.72255738068879 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9139468134349823 m³/s \n", - "Current outflux vel = -1.1636732246501096 m/s \n", - "Current pipe pressure = 20.663 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.77683370897238 m\n", - "Volume in reservoir = 20.77683370897238 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.17038678351896 m³/s \n", - "Current outflux vel = -1.4901827354117323 m/s \n", - "Current pipe pressure = 20.671 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.833306423367073 m\n", - "Volume in reservoir = 20.833306423367073 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.2580231718148631 m³/s \n", - "Current outflux vel = -1.6017648505478417 m/s \n", - "Current pipe pressure = 20.686 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.887506193165233 m\n", - "Volume in reservoir = 20.887506193165233 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.1670791529510784 m³/s \n", - "Current outflux vel = -1.4859713293733305 m/s \n", - "Current pipe pressure = 20.727 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.935197937766464 m\n", - "Volume in reservoir = 20.935197937766464 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.9069419481417854 m³/s \n", - "Current outflux vel = -1.1547543531532685 m/s \n", - "Current pipe pressure = 20.797 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.972842864769923 m\n", - "Volume in reservoir = 20.972842864769923 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.5053734476177437 m³/s \n", - "Current outflux vel = -0.6434614583660556 m/s \n", - "Current pipe pressure = 20.884 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.99802058468103 m\n", - "Volume in reservoir = 20.99802058468103 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.006952014854047346 m³/s \n", - "Current outflux vel = -0.008851580227759332 m/s \n", - "Current pipe pressure = 20.963 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.009706639186888 m\n", - "Volume in reservoir = 21.009706639186888 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.5325474891182498 m³/s \n", - "Current outflux vel = 0.6780605225947745 m/s \n", - "Current pipe pressure = 21.005 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.008336822425512 m\n", - "Volume in reservoir = 21.008336822425512 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.0547115507454514 m³/s \n", - "Current outflux vel = 1.342900454698056 m/s \n", - "Current pipe pressure = 20.994 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.99565407775612 m\n", - "Volume in reservoir = 20.99565407775612 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5072965056041971 m³/s \n", - "Current outflux vel = 1.9191495165763897 m/s \n", - "Current pipe pressure = 20.929 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.974366873137082 m\n", - "Volume in reservoir = 20.974366873137082 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8516678073124146 m³/s \n", - "Current outflux vel = 2.357616675983216 m/s \n", - "Current pipe pressure = 20.828 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.94775457268255 m\n", - "Volume in reservoir = 20.94775457268255 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0649293569835026 m³/s \n", - "Current outflux vel = 2.6291497143959472 m/s \n", - "Current pipe pressure = 20.717 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.919371778352303 m\n", - "Volume in reservoir = 20.919371778352303 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.1359932217585085 m³/s \n", - "Current outflux vel = 2.7196310372291967 m/s \n", - "Current pipe pressure = 20.626 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.892846209136078 m\n", - "Volume in reservoir = 20.892846209136078 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0618671766938688 m³/s \n", - "Current outflux vel = 2.625250825358077 m/s \n", - "Current pipe pressure = 20.575 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.871700620719135 m\n", - "Volume in reservoir = 20.871700620719135 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.846709487701483 m³/s \n", - "Current outflux vel = 2.3513035473791417 m/s \n", - "Current pipe pressure = 20.573 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.859156897107365 m\n", - "Volume in reservoir = 20.859156897107365 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5025483051384 m³/s \n", - "Current outflux vel = 1.913103919977007 m/s \n", - "Current pipe pressure = 20.617 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.857854883074065 m\n", - "Volume in reservoir = 20.857854883074065 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.0526929016703332 m³/s \n", - "Current outflux vel = 1.3403302308686726 m/s \n", - "Current pipe pressure = 20.693 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.869480041403893 m\n", - "Volume in reservoir = 20.869480041403893 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.535375660400662 m³/s \n", - "Current outflux vel = 0.6816614621108259 m/s \n", - "Current pipe pressure = 20.78 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.89444219329463 m\n", - "Volume in reservoir = 20.89444219329463 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.0016953061126087554 m³/s \n", - "Current outflux vel = 0.00215853078300471 m/s \n", - "Current pipe pressure = 20.854 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.931721520861856 m\n", - "Volume in reservoir = 20.931721520861856 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.4910193113567918 m³/s \n", - "Current outflux vel = -0.6251852044480947 m/s \n", - "Current pipe pressure = 20.902 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.978915044475496 m\n", - "Volume in reservoir = 20.978915044475496 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.8873935418746703 m³/s \n", - "Current outflux vel = -1.1298645492574289 m/s \n", - "Current pipe pressure = 20.923 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.032506549603404 m\n", - "Volume in reservoir = 21.032506549603404 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.143031110659661 m³/s \n", - "Current outflux vel = -1.455352410954434 m/s \n", - "Current pipe pressure = 20.931 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.088286427219174 m\n", - "Volume in reservoir = 21.088286427219174 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.230355718043521 m³/s \n", - "Current outflux vel = -1.5665375543040367 m/s \n", - "Current pipe pressure = 20.947 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.141802956454374 m\n", - "Volume in reservoir = 21.141802956454374 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.1397966347330517 m³/s \n", - "Current outflux vel = -1.4512341482981812 m/s \n", - "Current pipe pressure = 20.987 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.188833349856203 m\n", - "Volume in reservoir = 21.188833349856203 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.8805280800563076 m³/s \n", - "Current outflux vel = -1.12112317177742 m/s \n", - "Current pipe pressure = 21.057 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.225835462942914 m\n", - "Volume in reservoir = 21.225835462942914 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -0.4796884086584643 m³/s \n", - "Current outflux vel = -0.6107582510550378 m/s \n", - "Current pipe pressure = 21.142 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.250370708111753 m\n", - "Volume in reservoir = 21.250370708111753 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.018734382528116204 m³/s \n", - "Current outflux vel = 0.023853356680993063 m/s \n", - "Current pipe pressure = 21.218 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.26139268484347 m\n", - "Volume in reservoir = 21.26139268484347 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 0.5591045592212921 m³/s \n", - "Current outflux vel = 0.7118740344422718 m/s \n", - "Current pipe pressure = 21.258 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.259326015896846 m\n", - "Volume in reservoir = 21.259326015896846 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.0825857192682375 m³/s \n", - "Current outflux vel = 1.3783909483378796 m/s \n", - "Current pipe pressure = 21.244 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.24591997621128 m\n", - "Volume in reservoir = 21.24591997621128 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5362336779157155 m³/s \n", - "Current outflux vel = 1.9559934686762301 m/s \n", - "Current pipe pressure = 21.176 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.223894241580293 m\n", - "Volume in reservoir = 21.223894241580293 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.881218599916618 m³/s \n", - "Current outflux vel = 2.395241913705155 m/s \n", - "Current pipe pressure = 21.071 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.19653706277868 m\n", - "Volume in reservoir = 21.19653706277868 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0947369174793375 m³/s \n", - "Current outflux vel = 2.66710187915133 m/s \n", - "Current pipe pressure = 20.958 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.167409333321327 m\n", - "Volume in reservoir = 21.167409333321327 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.165804551656128 m³/s \n", - "Current outflux vel = 2.7575880013359915 m/s \n", - "Current pipe pressure = 20.865 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.1390719381078 m\n", - "Volume in reservoir = 21.1390719381078 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.1343563087884974 m³/s \n", - "Current outflux vel = 2.7175468549044886 m/s \n", - "Current pipe pressure = 20.805 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.11285149542176 m\n", - "Volume in reservoir = 21.11285149542176 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0497304537425083 m³/s \n", - "Current outflux vel = 2.60979786975291 m/s \n", - "Current pipe pressure = 20.779 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.089675309601088 m\n", - "Volume in reservoir = 21.089675309601088 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9279281539009736 m³/s \n", - "Current outflux vel = 2.4547143649549783 m/s \n", - "Current pipe pressure = 20.78 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.069940550649637 m\n", - "Volume in reservoir = 21.069940550649637 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7901991741130814 m³/s \n", - "Current outflux vel = 2.279352381433004 m/s \n", - "Current pipe pressure = 20.793 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.05350701213175 m\n", - "Volume in reservoir = 21.05350701213175 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.658057023908889 m³/s \n", - "Current outflux vel = 2.1111037702666926 m/s \n", - "Current pipe pressure = 20.812 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.039841811205573 m\n", - "Volume in reservoir = 21.039841811205573 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5472278702474924 m³/s \n", - "Current outflux vel = 1.9699917091154726 m/s \n", - "Current pipe pressure = 20.831 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.028235617151555 m\n", - "Volume in reservoir = 21.028235617151555 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.464783962005051 m³/s \n", - "Current outflux vel = 1.865020864918679 m/s \n", - "Current pipe pressure = 20.846 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.01798994758457 m\n", - "Volume in reservoir = 21.01798994758457 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4102982177930938 m³/s \n", - "Current outflux vel = 1.79564746076369 m/s \n", - "Current pipe pressure = 20.855 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.00850816420341 m\n", - "Volume in reservoir = 21.00850816420341 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3796974875862715 m³/s \n", - "Current outflux vel = 1.7566854009665922 m/s \n", - "Current pipe pressure = 20.858 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.999287521719452 m\n", - "Volume in reservoir = 20.999287521719452 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3692232014871903 m³/s \n", - "Current outflux vel = 1.743349125702372 m/s \n", - "Current pipe pressure = 20.856 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.989878219356342 m\n", - "Volume in reservoir = 20.989878219356342 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3767539340767798 m³/s \n", - "Current outflux vel = 1.7529375522362634 m/s \n", - "Current pipe pressure = 20.85 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.9798191317057 m\n", - "Volume in reservoir = 20.9798191317057 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4027400612654937 m³/s \n", - "Current outflux vel = 1.7860241169874513 m/s \n", - "Current pipe pressure = 20.838 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.968573131214928 m\n", - "Volume in reservoir = 20.968573131214928 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4502204183377505 m³/s \n", - "Current outflux vel = 1.8464779852099946 m/s \n", - "Current pipe pressure = 20.822 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.955484717091668 m\n", - "Volume in reservoir = 20.955484717091668 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5239303950389476 m³/s \n", - "Current outflux vel = 1.9403284423874663 m/s \n", - "Current pipe pressure = 20.799 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.939844988313947 m\n", - "Volume in reservoir = 20.939844988313947 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6260084276398443 m³/s \n", - "Current outflux vel = 2.070298230143693 m/s \n", - "Current pipe pressure = 20.767 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.92107573435854 m\n", - "Volume in reservoir = 20.92107573435854 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.751231448633075 m³/s \n", - "Current outflux vel = 2.229737132383476 m/s \n", - "Current pipe pressure = 20.725 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.898962938641883 m\n", - "Volume in reservoir = 20.898962938641883 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.88503630269939 m³/s \n", - "Current outflux vel = 2.4001027638582255 m/s \n", - "Current pipe pressure = 20.673 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.87382573874446 m\n", - "Volume in reservoir = 20.87382573874446 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0060973102212594 m³/s \n", - "Current outflux vel = 2.5542424259605507 m/s \n", - "Current pipe pressure = 20.614 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.846557686106596 m\n", - "Volume in reservoir = 20.846557686106596 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0914298402940297 m³/s \n", - "Current outflux vel = 2.6628911777015043 m/s \n", - "Current pipe pressure = 20.554 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.818499217900996 m\n", - "Volume in reservoir = 20.818499217900996 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.12314080925576 m³/s \n", - "Current outflux vel = 2.7032668373854487 m/s \n", - "Current pipe pressure = 20.501 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.79116359894335 m\n", - "Volume in reservoir = 20.79116359894335 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0942942254135937 m³/s \n", - "Current outflux vel = 2.6665382261070842 m/s \n", - "Current pipe pressure = 20.464 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.76591859010663 m\n", - "Volume in reservoir = 20.76591859010663 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0106915767178863 m³/s \n", - "Current outflux vel = 2.5600920277431083 m/s \n", - "Current pipe pressure = 20.446 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.74371107157441 m\n", - "Volume in reservoir = 20.74371107157441 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8891626942484603 m³/s \n", - "Current outflux vel = 2.405356648755563 m/s \n", - "Current pipe pressure = 20.446 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.724932001040198 m\n", - "Volume in reservoir = 20.724932001040198 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.751954151126913 m³/s \n", - "Current outflux vel = 2.2306573057777093 m/s \n", - "Current pipe pressure = 20.459 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.709438570566338 m\n", - "Volume in reservoir = 20.709438570566338 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6204359291202683 m³/s \n", - "Current outflux vel = 2.063203104665591 m/s \n", - "Current pipe pressure = 20.478 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.69669845613095 m\n", - "Volume in reservoir = 20.69669845613095 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5102086239594688 m³/s \n", - "Current outflux vel = 1.9228573408252707 m/s \n", - "Current pipe pressure = 20.497 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.686005288260525 m\n", - "Volume in reservoir = 20.686005288260525 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4282481722690528 m³/s \n", - "Current outflux vel = 1.818502052628677 m/s \n", - "Current pipe pressure = 20.512 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.676664624494723 m\n", - "Volume in reservoir = 20.676664624494723 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.374084166897561 m³/s \n", - "Current outflux vel = 1.7495382990884458 m/s \n", - "Current pipe pressure = 20.521 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.66808302425907 m\n", - "Volume in reservoir = 20.66808302425907 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3436770408213303 m³/s \n", - "Current outflux vel = 1.7108227437264412 m/s \n", - "Current pipe pressure = 20.525 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.659760022399773 m\n", - "Volume in reservoir = 20.659760022399773 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3333048979687774 m³/s \n", - "Current outflux vel = 1.6976165212829288 m/s \n", - "Current pipe pressure = 20.524 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.65124835040214 m\n", - "Volume in reservoir = 20.65124835040214 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3408362613281317 m³/s \n", - "Current outflux vel = 1.7072057509378282 m/s \n", - "Current pipe pressure = 20.518 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.64208844305751 m\n", - "Volume in reservoir = 20.64208844305751 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3667604218970415 m³/s \n", - "Current outflux vel = 1.740213417338228 m/s \n", - "Current pipe pressure = 20.508 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.631745218989536 m\n", - "Volume in reservoir = 20.631745218989536 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4140967730925007 m³/s \n", - "Current outflux vel = 1.8004839315837584 m/s \n", - "Current pipe pressure = 20.492 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.619566096650303 m\n", - "Volume in reservoir = 20.619566096650303 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4875457907846854 m³/s \n", - "Current outflux vel = 1.8940021254314006 m/s \n", - "Current pipe pressure = 20.471 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.604844372805 m\n", - "Volume in reservoir = 20.604844372805 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5892747441298507 m³/s \n", - "Current outflux vel = 2.0235274516749833 m/s \n", - "Current pipe pressure = 20.44 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.587001970728824 m\n", - "Volume in reservoir = 20.587001970728824 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7141431053255414 m³/s \n", - "Current outflux vel = 2.1825147870356103 m/s \n", - "Current pipe pressure = 20.4 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.56582230698083 m\n", - "Volume in reservoir = 20.56582230698083 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.84769580934938 m³/s \n", - "Current outflux vel = 2.3525593711050727 m/s \n", - "Current pipe pressure = 20.35 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.541618550379866 m\n", - "Volume in reservoir = 20.541618550379866 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9687434127981756 m³/s \n", - "Current outflux vel = 2.5066819666114997 m/s \n", - "Current pipe pressure = 20.293 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.51528229958378 m\n", - "Volume in reservoir = 20.51528229958378 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0541408592874277 m³/s \n", - "Current outflux vel = 2.6154133725010205 m/s \n", - "Current pipe pressure = 20.234 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.48815289601536 m\n", - "Volume in reservoir = 20.48815289601536 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.085960975137784 m³/s \n", - "Current outflux vel = 2.655928002319748 m/s \n", - "Current pipe pressure = 20.183 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.461742920169364 m\n", - "Volume in reservoir = 20.461742920169364 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0572511866708254 m³/s \n", - "Current outflux vel = 2.6193735643226352 m/s \n", - "Current pipe pressure = 20.146 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.43741925153126 m\n", - "Volume in reservoir = 20.43741925153126 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.973820784192952 m³/s \n", - "Current outflux vel = 2.513146676654636 m/s \n", - "Current pipe pressure = 20.129 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.416127114550726 m\n", - "Volume in reservoir = 20.416127114550726 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.852530532837561 m³/s \n", - "Current outflux vel = 2.3587151322380846 m/s \n", - "Current pipe pressure = 20.129 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.3982518827904 m\n", - "Volume in reservoir = 20.3982518827904 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7157841726937526 m³/s \n", - "Current outflux vel = 2.1846042589043915 m/s \n", - "Current pipe pressure = 20.142 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.38364860833497 m\n", - "Volume in reservoir = 20.38364860833497 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5848139397195686 m³/s \n", - "Current outflux vel = 2.0178477790984832 m/s \n", - "Current pipe pressure = 20.162 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.371785635506935 m\n", - "Volume in reservoir = 20.371785635506935 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4751081386341636 m³/s \n", - "Current outflux vel = 1.8781660148696957 m/s \n", - "Current pipe pressure = 20.181 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.361959518704865 m\n", - "Volume in reservoir = 20.361959518704865 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3935522845723491 m³/s \n", - "Current outflux vel = 1.7743258763735437 m/s \n", - "Current pipe pressure = 20.196 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.35347959879785 m\n", - "Volume in reservoir = 20.35347959879785 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3396414286534655 m³/s \n", - "Current outflux vel = 1.7056844427271014 m/s \n", - "Current pipe pressure = 20.206 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.345755153189234 m\n", - "Volume in reservoir = 20.345755153189234 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3093785166562157 m³/s \n", - "Current outflux vel = 1.6671525064333628 m/s \n", - "Current pipe pressure = 20.21 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.33828733621855 m\n", - "Volume in reservoir = 20.33828733621855 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.299085564964712 m³/s \n", - "Current outflux vel = 1.6540471133076915 m/s \n", - "Current pipe pressure = 20.209 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.330630483690342 m\n", - "Volume in reservoir = 20.330630483690342 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3066317389368058 m³/s \n", - "Current outflux vel = 1.6636552004204126 m/s \n", - "Current pipe pressure = 20.204 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.322325946990162 m\n", - "Volume in reservoir = 20.322325946990162 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.332533872750253 m³/s \n", - "Current outflux vel = 1.6966348214847153 m/s \n", - "Current pipe pressure = 20.195 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.312840253666497 m\n", - "Volume in reservoir = 20.312840253666497 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3797836693788559 m³/s \n", - "Current outflux vel = 1.7567951310329468 m/s \n", - "Current pipe pressure = 20.18 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.301523539446332 m\n", - "Volume in reservoir = 20.301523539446332 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4530372747918407 m³/s \n", - "Current outflux vel = 1.8500645182391848 m/s \n", - "Current pipe pressure = 20.16 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.28767132670028 m\n", - "Volume in reservoir = 20.28767132670028 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5544815975616315 m³/s \n", - "Current outflux vel = 1.9792274415785602 m/s \n", - "Current pipe pressure = 20.131 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.27070584491537 m\n", - "Volume in reservoir = 20.27070584491537 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6790528628478947 m³/s \n", - "Current outflux vel = 2.137836502678725 m/s \n", - "Current pipe pressure = 20.092 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.250407973853292 m\n", - "Volume in reservoir = 20.250407973853292 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8124097843012807 m³/s \n", - "Current outflux vel = 2.3076318086373173 m/s \n", - "Current pipe pressure = 20.043 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.22708539981095 m\n", - "Volume in reservoir = 20.22708539981095 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.933480901231897 m³/s \n", - "Current outflux vel = 2.4617843424386328 m/s \n", - "Current pipe pressure = 19.988 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.201628384213443 m\n", - "Volume in reservoir = 20.201628384213443 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.018955560068908 m³/s \n", - "Current outflux vel = 2.570614058142662 m/s \n", - "Current pipe pressure = 19.931 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.17537554353836 m\n", - "Volume in reservoir = 20.17537554353836 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0508822686316286 m³/s \n", - "Current outflux vel = 2.6112644060179524 m/s \n", - "Current pipe pressure = 19.881 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.14983904978432 m\n", - "Volume in reservoir = 20.14983904978432 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.0222956629401128 m³/s \n", - "Current outflux vel = 2.5748668092017635 m/s \n", - "Current pipe pressure = 19.845 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.12638525972685 m\n", - "Volume in reservoir = 20.12638525972685 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9390095275046992 m³/s \n", - "Current outflux vel = 2.4688236080372263 m/s \n", - "Current pipe pressure = 19.828 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.105958388792565 m\n", - "Volume in reservoir = 20.105958388792565 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8179040426572033 m³/s \n", - "Current outflux vel = 2.3146273156450694 m/s \n", - "Current pipe pressure = 19.829 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.088938602263777 m\n", - "Volume in reservoir = 20.088938602263777 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6815509493853849 m³/s \n", - "Current outflux vel = 2.141017165244428 m/s \n", - "Current pipe pressure = 19.843 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.075178829559118 m\n", - "Volume in reservoir = 20.075178829559118 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5510590765840868 m³/s \n", - "Current outflux vel = 1.9748697525272647 m/s \n", - "Current pipe pressure = 19.862 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.064148211203815 m\n", - "Volume in reservoir = 20.064148211203815 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4418000276399812 m³/s \n", - "Current outflux vel = 1.8357568107914748 m/s \n", - "Current pipe pressure = 19.881 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.055146211624056 m\n", - "Volume in reservoir = 20.055146211624056 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3605745778879257 m³/s \n", - "Current outflux vel = 1.7323373561282587 m/s \n", - "Current pipe pressure = 19.896 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.0474857296904 m\n", - "Volume in reservoir = 20.0474857296904 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.306851695946489 m³/s \n", - "Current outflux vel = 1.6639352583832832 m/s \n", - "Current pipe pressure = 19.906 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.04057830173567 m\n", - "Volume in reservoir = 20.04057830173567 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2766861928103876 m³/s \n", - "Current outflux vel = 1.6255273469035663 m/s \n", - "Current pipe pressure = 19.911 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.03392610676753 m\n", - "Volume in reservoir = 20.03392610676753 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2664494311393943 m³/s \n", - "Current outflux vel = 1.6124934971340283 m/s \n", - "Current pipe pressure = 19.911 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.027084136987117 m\n", - "Volume in reservoir = 20.027084136987117 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.274025344609446 m³/s \n", - "Current outflux vel = 1.62213944975159 m/s \n", - "Current pipe pressure = 19.906 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.019594078022447 m\n", - "Volume in reservoir = 20.019594078022447 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2999436884370135 m³/s \n", - "Current outflux vel = 1.6551397100468912 m/s \n", - "Current pipe pressure = 19.898 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.010923644456078 m\n", - "Volume in reservoir = 20.010923644456078 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3471620638714141 m³/s \n", - "Current outflux vel = 1.7152600128881215 m/s \n", - "Current pipe pressure = 19.885 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.000425515596454 m\n", - "Volume in reservoir = 20.000425515596454 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4202823529771018 m³/s \n", - "Current outflux vel = 1.808359656499951 m/s \n", - "Current pipe pressure = 19.865 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.987397483484628 m\n", - "Volume in reservoir = 19.987397483484628 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5215023924930164 m³/s \n", - "Current outflux vel = 1.937237013531269 m/s \n", - "Current pipe pressure = 19.837 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.971262263876326 m\n", - "Volume in reservoir = 19.971262263876326 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6458297177188448 m³/s \n", - "Current outflux vel = 2.0955354804999433 m/s \n", - "Current pipe pressure = 19.8 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.95179802402427 m\n", - "Volume in reservoir = 19.95179802402427 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7790510459414566 m³/s \n", - "Current outflux vel = 2.2651581437951154 m/s \n", - "Current pipe pressure = 19.753 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.929307896013082 m\n", - "Volume in reservoir = 19.929307896013082 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9001687330364632 m³/s \n", - "Current outflux vel = 2.4193699725713373 m/s \n", - "Current pipe pressure = 19.699 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.904681105455577 m\n", - "Volume in reservoir = 19.904681105455577 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.985731610112875 m³/s \n", - "Current outflux vel = 2.5283120112263386 m/s \n", - "Current pipe pressure = 19.643 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.879255876086066 m\n", - "Volume in reservoir = 19.879255876086066 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 2.017762598116073 m³/s \n", - "Current outflux vel = 2.5690951318089477 m/s \n", - "Current pipe pressure = 19.594 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.85454422453897 m\n", - "Volume in reservoir = 19.85454422453897 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9892867213016505 m³/s \n", - "Current outflux vel = 2.532838519377818 m/s \n", - "Current pipe pressure = 19.56 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.831912307414935 m\n", - "Volume in reservoir = 19.831912307414935 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9061194803513493 m³/s \n", - "Current outflux vel = 2.426946699373377 m/s \n", - "Current pipe pressure = 19.544 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.812303629060338 m\n", - "Volume in reservoir = 19.812303629060338 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7851614573013508 m³/s \n", - "Current outflux vel = 2.2729381611731316 m/s \n", - "Current pipe pressure = 19.545 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.79609404211116 m\n", - "Volume in reservoir = 19.79609404211116 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6491284838709095 m³/s \n", - "Current outflux vel = 2.099735600013586 m/s \n", - "Current pipe pressure = 19.558 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.783134131228877 m\n", - "Volume in reservoir = 19.783134131228877 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.519050697829526 m³/s \n", - "Current outflux vel = 1.9341154189340968 m/s \n", - "Current pipe pressure = 19.578 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.772893968824942 m\n", - "Volume in reservoir = 19.772893968824942 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4101686776351483 m³/s \n", - "Current outflux vel = 1.7954825251119628 m/s \n", - "Current pipe pressure = 19.597 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.764675937356465 m\n", - "Volume in reservoir = 19.764675937356465 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3292035993757354 m³/s \n", - "Current outflux vel = 1.6923945857295009 m/s \n", - "Current pipe pressure = 19.612 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.75779629219355 m\n", - "Volume in reservoir = 19.75779629219355 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2756067228382342 m³/s \n", - "Current outflux vel = 1.6241529230476661 m/s \n", - "Current pipe pressure = 19.623 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.751668414360395 m\n", - "Volume in reservoir = 19.751668414360395 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2454932369041096 m³/s \n", - "Current outflux vel = 1.5858112419265127 m/s \n", - "Current pipe pressure = 19.628 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.745794933355292 m\n", - "Volume in reservoir = 19.745794933355292 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.235290250806127 m³/s \n", - "Current outflux vel = 1.572820396552178 m/s \n", - "Current pipe pressure = 19.628 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.739730564009193 m\n", - "Volume in reservoir = 19.739730564009193 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2429108515862244 m³/s \n", - "Current outflux vel = 1.5825232468200376 m/s \n", - "Current pipe pressure = 19.625 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.733016755537484 m\n", - "Volume in reservoir = 19.733016755537484 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2688831922016357 m³/s \n", - "Current outflux vel = 1.6155922579609108 m/s \n", - "Current pipe pressure = 19.617 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.72512203591746 m\n", - "Volume in reservoir = 19.72512203591746 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3161228987384828 m³/s \n", - "Current outflux vel = 1.6757397204053086 m/s \n", - "Current pipe pressure = 19.605 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.71540147189184 m\n", - "Volume in reservoir = 19.71540147189184 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3891688838560048 m³/s \n", - "Current outflux vel = 1.7687447572410737 m/s \n", - "Current pipe pressure = 19.586 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.703155185791182 m\n", - "Volume in reservoir = 19.703155185791182 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4902212402740782 m³/s \n", - "Current outflux vel = 1.897408613521237 m/s \n", - "Current pipe pressure = 19.559 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.68780656496862 m\n", - "Volume in reservoir = 19.68780656496862 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.614353822611981 m³/s \n", - "Current outflux vel = 2.055459126143948 m/s \n", - "Current pipe pressure = 19.523 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.66913089308027 m\n", - "Volume in reservoir = 19.66913089308027 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7474956025673727 m³/s \n", - "Current outflux vel = 2.2249805054395804 m/s \n", - "Current pipe pressure = 19.478 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.647427694296464 m\n", - "Volume in reservoir = 19.647427694296464 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8686780524119133 m³/s \n", - "Current outflux vel = 2.379274792709535 m/s \n", - "Current pipe pressure = 19.425 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.623585368753698 m\n", - "Volume in reservoir = 19.623585368753698 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9543389323605893 m³/s \n", - "Current outflux vel = 2.488341612497001 m/s \n", - "Current pipe pressure = 19.371 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.59894204333274 m\n", - "Volume in reservoir = 19.59894204333274 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9864721174190427 m³/s \n", - "Current outflux vel = 2.529254854411717 m/s \n", - "Current pipe pressure = 19.323 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.575018979773223 m\n", - "Volume in reservoir = 19.575018979773223 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9577288436961469 m³/s \n", - "Current outflux vel = 2.4926577816625786 m/s \n", - "Current pipe pressure = 19.29 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.553183224555358 m\n", - "Volume in reservoir = 19.553183224555358 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8742587383164506 m³/s \n", - "Current outflux vel = 2.386380342689938 m/s \n", - "Current pipe pressure = 19.274 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.534379009930618 m\n", - "Volume in reservoir = 19.534379009930618 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7529685599617535 m³/s \n", - "Current outflux vel = 2.231948891220757 m/s \n", - "Current pipe pressure = 19.276 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.51898066061835 m\n", - "Volume in reservoir = 19.51898066061835 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6166646939559215 m³/s \n", - "Current outflux vel = 2.0584014189218487 m/s \n", - "Current pipe pressure = 19.29 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.506837884747217 m\n", - "Volume in reservoir = 19.506837884747217 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.486351269647888 m³/s \n", - "Current outflux vel = 1.892481213883008 m/s \n", - "Current pipe pressure = 19.309 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.49742315168644 m\n", - "Volume in reservoir = 19.49742315168644 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3771379470572163 m³/s \n", - "Current outflux vel = 1.7534264927486467 m/s \n", - "Current pipe pressure = 19.329 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.49004236288368 m\n", - "Volume in reservoir = 19.49004236288368 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2957009034352525 m³/s \n", - "Current outflux vel = 1.64973762840284 m/s \n", - "Current pipe pressure = 19.345 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.484014724665904 m\n", - "Volume in reservoir = 19.484014724665904 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2415139323325501 m³/s \n", - "Current outflux vel = 1.5807446339854578 m/s \n", - "Current pipe pressure = 19.355 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.478754189712824 m\n", - "Volume in reservoir = 19.478754189712824 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.210787292259958 m³/s \n", - "Current outflux vel = 1.5416222607681895 m/s \n", - "Current pipe pressure = 19.361 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.47376207305219 m\n", - "Volume in reservoir = 19.47376207305219 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2000235111735407 m³/s \n", - "Current outflux vel = 1.5279173890380904 m/s \n", - "Current pipe pressure = 19.362 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.468590518047524 m\n", - "Volume in reservoir = 19.468590518047524 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.207185992589082 m³/s \n", - "Current outflux vel = 1.5370369436147884 m/s \n", - "Current pipe pressure = 19.36 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.46277793814618 m\n", - "Volume in reservoir = 19.46277793814618 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2328214922736542 m³/s \n", - "Current outflux vel = 1.5696770755622316 m/s \n", - "Current pipe pressure = 19.353 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.455790093530812 m\n", - "Volume in reservoir = 19.455790093530812 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2798349321109896 m³/s \n", - "Current outflux vel = 1.6295364462971544 m/s \n", - "Current pipe pressure = 19.342 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.44698053916859 m\n", - "Volume in reservoir = 19.44698053916859 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3527149720494775 m³/s \n", - "Current outflux vel = 1.722330195168715 m/s \n", - "Current pipe pressure = 19.324 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.43564819685495 m\n", - "Volume in reservoir = 19.43564819685495 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.453649228113024 m³/s \n", - "Current outflux vel = 1.8508436814072473 m/s \n", - "Current pipe pressure = 19.299 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.421214672594576 m\n", - "Volume in reservoir = 19.421214672594576 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.577734760280011 m³/s \n", - "Current outflux vel = 2.008834287891762 m/s \n", - "Current pipe pressure = 19.265 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.403450867202647 m\n", - "Volume in reservoir = 19.403450867202647 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.711004651238993 m³/s \n", - "Current outflux vel = 2.1785187831832813 m/s \n", - "Current pipe pressure = 19.221 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.38265450341603 m\n", - "Volume in reservoir = 19.38265450341603 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.832387276769123 m³/s \n", - "Current outflux vel = 2.333067942052023 m/s \n", - "Current pipe pressure = 19.17 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.35971648715791 m\n", - "Volume in reservoir = 19.35971648715791 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9181483680970155 m³/s \n", - "Current outflux vel = 2.4422623549303393 m/s \n", - "Current pipe pressure = 19.117 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.335979969864457 m\n", - "Volume in reservoir = 19.335979969864457 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9501811066485977 m³/s \n", - "Current outflux vel = 2.4830477043803763 m/s \n", - "Current pipe pressure = 19.071 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.312963093789623 m\n", - "Volume in reservoir = 19.312963093789623 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9214623999108076 m³/s \n", - "Current outflux vel = 2.4464819112881697 m/s \n", - "Current pipe pressure = 19.038 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.2920389456848 m\n", - "Volume in reservoir = 19.2920389456848 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8377753853706444 m³/s \n", - "Current outflux vel = 2.3399282949948077 m/s \n", - "Current pipe pressure = 19.023 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.274156333028454 m\n", - "Volume in reservoir = 19.274156333028454 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7160856707492262 m³/s \n", - "Current outflux vel = 2.1849881381512812 m/s \n", - "Current pipe pressure = 19.026 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.25968999999105 m\n", - "Volume in reservoir = 19.25968999999105 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5793656166935743 m³/s \n", - "Current outflux vel = 2.0109107587692963 m/s \n", - "Current pipe pressure = 19.04 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.248487390412304 m\n", - "Volume in reservoir = 19.248487390412304 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.448726761359173 m³/s \n", - "Current outflux vel = 1.8445762020786 m/s \n", - "Current pipe pressure = 19.06 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.240021163672836 m\n", - "Volume in reservoir = 19.240021163672836 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3391806193245217 m³/s \n", - "Current outflux vel = 1.7050977220669072 m/s \n", - "Current pipe pressure = 19.079 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.233597530683575 m\n", - "Volume in reservoir = 19.233597530683575 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2573984958017839 m³/s \n", - "Current outflux vel = 1.6009694883453416 m/s \n", - "Current pipe pressure = 19.096 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.228534975256625 m\n", - "Volume in reservoir = 19.228534975256625 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.202895240651445 m³/s \n", - "Current outflux vel = 1.53157378857114 m/s \n", - "Current pipe pressure = 19.107 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.224244680550903 m\n", - "Volume in reservoir = 19.224244680550903 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.1719628820268433 m³/s \n", - "Current outflux vel = 1.4921894863583671 m/s \n", - "Current pipe pressure = 19.113 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.22022391302264 m\n", - "Volume in reservoir = 19.22022391302264 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.1611550890424773 m³/s \n", - "Current outflux vel = 1.4784285769393612 m/s \n", - "Current pipe pressure = 19.115 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.216020030927528 m\n", - "Volume in reservoir = 19.216020030927528 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.1684647676091124 m³/s \n", - "Current outflux vel = 1.4877355487497042 m/s \n", - "Current pipe pressure = 19.114 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.211166607550958 m\n", - "Volume in reservoir = 19.211166607550958 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.1944409100340254 m³/s \n", - "Current outflux vel = 1.520809400504776 m/s \n", - "Current pipe pressure = 19.108 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.20512547440005 m\n", - "Volume in reservoir = 19.20512547440005 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2419519993718238 m³/s \n", - "Current outflux vel = 1.581302398263106 m/s \n", - "Current pipe pressure = 19.098 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.197248267085673 m\n", - "Volume in reservoir = 19.197248267085673 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.315406318393052 m³/s \n", - "Current outflux vel = 1.6748273419725261 m/s \n", - "Current pipe pressure = 19.082 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.186833428940623 m\n", - "Volume in reservoir = 19.186833428940623 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4169338264710756 m³/s \n", - "Current outflux vel = 1.8040961801358844 m/s \n", - "Current pipe pressure = 19.058 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.17330220626718 m\n", - "Volume in reservoir = 19.17330220626718 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5416268143744432 m³/s \n", - "Current outflux vel = 1.9628602232856354 m/s \n", - "Current pipe pressure = 19.025 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.156423023060384 m\n", - "Volume in reservoir = 19.156423023060384 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6756031012696635 m³/s \n", - "Current outflux vel = 2.133444129817413 m/s \n", - "Current pipe pressure = 18.982 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.136494599254714 m\n", - "Volume in reservoir = 19.136494599254714 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7976522667668158 m³/s \n", - "Current outflux vel = 2.288841953730314 m/s \n", - "Current pipe pressure = 18.933 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.114412092732486 m\n", - "Volume in reservoir = 19.114412092732486 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8839101028634513 m³/s \n", - "Current outflux vel = 2.3986688416918343 m/s \n", - "Current pipe pressure = 18.881 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.09152478068799 m\n", - "Volume in reservoir = 19.09152478068799 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.9161948588394404 m³/s \n", - "Current outflux vel = 2.4397750696925886 m/s \n", - "Current pipe pressure = 18.836 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.06935697449019 m\n", - "Volume in reservoir = 19.06935697449019 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8874816150853329 m³/s \n", - "Current outflux vel = 2.403216232287239 m/s \n", - "Current pipe pressure = 18.804 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.049287939091958 m\n", - "Volume in reservoir = 19.049287939091958 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8035530385106093 m³/s \n", - "Current outflux vel = 2.2963550496589678 m/s \n", - "Current pipe pressure = 18.79 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.032270834719313 m\n", - "Volume in reservoir = 19.032270834719313 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6814477359237112 m³/s \n", - "Current outflux vel = 2.140885749783476 m/s \n", - "Current pipe pressure = 18.793 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.01868158744582 m\n", - "Volume in reservoir = 19.01868158744582 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5442649353049513 m³/s \n", - "Current outflux vel = 1.966219183178152 m/s \n", - "Current pipe pressure = 18.808 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.00836532705584 m\n", - "Volume in reservoir = 19.00836532705584 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4132560864592596 m³/s \n", - "Current outflux vel = 1.7994135361175856 m/s \n", - "Current pipe pressure = 18.828 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.00079485333592 m\n", - "Volume in reservoir = 19.00079485333592 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3033345274321688 m³/s \n", - "Current outflux vel = 1.6594570603453531 m/s \n", - "Current pipe pressure = 18.848 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.995276766652832 m\n", - "Volume in reservoir = 18.995276766652832 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.221161531040287 m³/s \n", - "Current outflux vel = 1.5548311518298294 m/s \n", - "Current pipe pressure = 18.864 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.9911286951479 m\n", - "Volume in reservoir = 18.9911286951479 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.1663015331586293 m³/s \n", - "Current outflux vel = 1.4849812331028154 m/s \n", - "Current pipe pressure = 18.876 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.987758940616438 m\n", - "Volume in reservoir = 18.987758940616438 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.1351274768170154 m³/s \n", - "Current outflux vel = 1.4452891917988708 m/s \n", - "Current pipe pressure = 18.883 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.98466022974306 m\n", - "Volume in reservoir = 18.98466022974306 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.1242593489200285 m³/s \n", - "Current outflux vel = 1.4314514615831877 m/s \n", - "Current pipe pressure = 18.886 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.9813745263104 m\n", - "Volume in reservoir = 18.9813745263104 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.1317242801526335 m³/s \n", - "Current outflux vel = 1.440956107227269 m/s \n", - "Current pipe pressure = 18.885 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.977430100006536 m\n", - "Volume in reservoir = 18.977430100006536 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.1580676669015526 m³/s \n", - "Current outflux vel = 1.474497548978245 m/s \n", - "Current pipe pressure = 18.88 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.97228477976629 m\n", - "Volume in reservoir = 18.97228477976629 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2061059738683715 m³/s \n", - "Current outflux vel = 1.5356618210705253 m/s \n", - "Current pipe pressure = 18.871 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.96528823708354 m\n", - "Volume in reservoir = 18.96528823708354 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2801659404035626 m³/s \n", - "Current outflux vel = 1.6299578991448935 m/s \n", - "Current pipe pressure = 18.856 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.955738572757873 m\n", - "Volume in reservoir = 18.955738572757873 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3823126372356513 m³/s \n", - "Current outflux vel = 1.7600151129155828 m/s \n", - "Current pipe pressure = 18.833 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.943056636137683 m\n", - "Volume in reservoir = 18.943056636137683 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5076405150309853 m³/s \n", - "Current outflux vel = 1.919587522982338 m/s \n", - "Current pipe pressure = 18.802 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.92700855130784 m\n", - "Volume in reservoir = 18.92700855130784 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.642343558549616 m³/s \n", - "Current outflux vel = 2.0910967647864402 m/s \n", - "Current pipe pressure = 18.76 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.907894579926587 m\n", - "Volume in reservoir = 18.907894579926587 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.765057848526834 m³/s \n", - "Current outflux vel = 2.247341451489532 m/s \n", - "Current pipe pressure = 18.712 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.886614202270287 m\n", - "Volume in reservoir = 18.886614202270287 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8518081896467395 m³/s \n", - "Current outflux vel = 2.3577954163226607 m/s \n", - "Current pipe pressure = 18.662 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.864522908871454 m\n", - "Volume in reservoir = 18.864522908871454 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8843371991899738 m³/s \n", - "Current outflux vel = 2.399212637624174 m/s \n", - "Current pipe pressure = 18.618 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.843151201388014 m\n", - "Volume in reservoir = 18.843151201388014 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.855620799719369 m³/s \n", - "Current outflux vel = 2.362649782235788 m/s \n", - "Current pipe pressure = 18.587 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.823884601312827 m\n", - "Volume in reservoir = 18.823884601312827 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.771438901761951 m³/s \n", - "Current outflux vel = 2.255466060805543 m/s \n", - "Current pipe pressure = 18.574 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.80768069583434 m\n", - "Volume in reservoir = 18.80768069583434 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6489032789159166 m³/s \n", - "Current outflux vel = 2.0994488601592187 m/s \n", - "Current pipe pressure = 18.577 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.79491710212544 m\n", - "Volume in reservoir = 18.79491710212544 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5112226248210727 m³/s \n", - "Current outflux vel = 1.9241484068206602 m/s \n", - "Current pipe pressure = 18.592 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.785436816088975 m\n", - "Volume in reservoir = 18.785436816088975 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.379801463365988 m³/s \n", - "Current outflux vel = 1.756817787041022 m/s \n", - "Current pipe pressure = 18.612 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.778712651789686 m\n", - "Volume in reservoir = 18.778712651789686 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.26946720059864 m³/s \n", - "Current outflux vel = 1.6163358405464339 m/s \n", - "Current pipe pressure = 18.632 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.774051671559565 m\n", - "Volume in reservoir = 18.774051671559565 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.1868631424300427 m³/s \n", - "Current outflux vel = 1.511161087130572 m/s \n", - "Current pipe pressure = 18.649 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.77077058193601 m\n", - "Volume in reservoir = 18.77077058193601 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.131608859528039 m³/s \n", - "Current outflux vel = 1.4408091491237571 m/s \n", - "Current pipe pressure = 18.662 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.768274691636687 m\n", - "Volume in reservoir = 18.768274691636687 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.1001600376101257 m³/s \n", - "Current outflux vel = 1.400767265422536 m/s \n", - "Current pipe pressure = 18.669 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.766051708379397 m\n", - "Volume in reservoir = 18.766051708379397 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.0892176813854468 m³/s \n", - "Current outflux vel = 1.386835024764696 m/s \n", - "Current pipe pressure = 18.673 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.763637694688704 m\n", - "Volume in reservoir = 18.763637694688704 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.0968442471642554 m³/s \n", - "Current outflux vel = 1.3965454699047988 m/s \n", - "Current pipe pressure = 18.673 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.760555169209482 m\n", - "Volume in reservoir = 18.760555169209482 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.1235791659794145 m³/s \n", - "Current outflux vel = 1.4305854257655435 m/s \n", - "Current pipe pressure = 18.669 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.756257861834666 m\n", - "Volume in reservoir = 18.756257861834666 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.1721728453861628 m³/s \n", - "Current outflux vel = 1.4924568200103983 m/s \n", - "Current pipe pressure = 18.661 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.75009351661821 m\n", - "Volume in reservoir = 18.75009351661821 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2468651565423334 m³/s \n", - "Current outflux vel = 1.587558024262098 m/s \n", - "Current pipe pressure = 18.647 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.741359975510264 m\n", - "Volume in reservoir = 18.741359975510264 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.349654397912856 m³/s \n", - "Current outflux vel = 1.7184333511483747 m/s \n", - "Current pipe pressure = 18.625 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.729477712204403 m\n", - "Volume in reservoir = 18.729477712204403 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.47563967505857 m³/s \n", - "Current outflux vel = 1.8788427880647172 m/s \n", - "Current pipe pressure = 18.595 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.714210765814926 m\n", - "Volume in reservoir = 18.714210765814926 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6110833912173692 m³/s \n", - "Current outflux vel = 2.051295083563985 m/s \n", - "Current pipe pressure = 18.555 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.695861296021715 m\n", - "Volume in reservoir = 18.695861296021715 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7344624725714024 m³/s \n", - "Current outflux vel = 2.208386208937037 m/s \n", - "Current pipe pressure = 18.508 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.675333272032162 m\n", - "Volume in reservoir = 18.675333272032162 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.821698315789492 m³/s \n", - "Current outflux vel = 2.3194583342406254 m/s \n", - "Current pipe pressure = 18.459 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.653988405226468 m\n", - "Volume in reservoir = 18.653988405226468 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.8544642600510475 m³/s \n", - "Current outflux vel = 2.3611772301950262 m/s \n", - "Current pipe pressure = 18.416 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.63336339713496 m\n", - "Volume in reservoir = 18.63336339713496 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.825736991979113 m³/s \n", - "Current outflux vel = 2.3246005364736315 m/s \n", - "Current pipe pressure = 18.386 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.614850024916542 m\n", - "Volume in reservoir = 18.614850024916542 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7412940905672203 m³/s \n", - "Current outflux vel = 2.217084495123837 m/s \n", - "Current pipe pressure = 18.373 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.599410475056978 m\n", - "Volume in reservoir = 18.599410475056978 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.6183135682414969 m³/s \n", - "Current outflux vel = 2.0605008308665402 m/s \n", - "Current pipe pressure = 18.377 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.58742437590025 m\n", - "Volume in reservoir = 18.58742437590025 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.4801076747347406 m³/s \n", - "Current outflux vel = 1.8845316219382815 m/s \n", - "Current pipe pressure = 18.392 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.57873281300215 m\n", - "Volume in reservoir = 18.57873281300215 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.3482378652224156 m³/s \n", - "Current outflux vel = 1.716629765710496 m/s \n", - "Current pipe pressure = 18.412 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.57280855848013 m\n", - "Volume in reservoir = 18.57280855848013 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.237456804025213 m³/s \n", - "Current outflux vel = 1.5755789377864917 m/s \n", - "Current pipe pressure = 18.433 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.56895915897305 m\n", - "Volume in reservoir = 18.56895915897305 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.1543866925014956 m³/s \n", - "Current outflux vel = 1.4698107868089345 m/s \n", - "Current pipe pressure = 18.45 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.566500415984738 m\n", - "Volume in reservoir = 18.566500415984738 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.0987024774259952 m³/s \n", - "Current outflux vel = 1.3989114421572695 m/s \n", - "Current pipe pressure = 18.463 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.564834500778506 m\n", - "Volume in reservoir = 18.564834500778506 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.066949024680037 m³/s \n", - "Current outflux vel = 1.3584816904392363 m/s \n", - "Current pipe pressure = 18.471 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.563443654588244 m\n", - "Volume in reservoir = 18.563443654588244 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.0559204805424103 m³/s \n", - "Current outflux vel = 1.3444397119223528 m/s \n", - "Current pipe pressure = 18.475 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.56185761205368 m\n", - "Volume in reservoir = 18.56185761205368 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.0637137908713892 m³/s \n", - "Current outflux vel = 1.3543624628176016 m/s \n", - "Current pipe pressure = 18.476 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.559592724113305 m\n", - "Volume in reservoir = 18.559592724113305 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.0908620253589847 m³/s \n", - "Current outflux vel = 1.3889286685369513 m/s \n", - "Current pipe pressure = 18.473 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.556098512594467 m\n", - "Volume in reservoir = 18.556098512594467 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.140037228268957 m³/s \n", - "Current outflux vel = 1.4515404815023036 m/s \n", - "Current pipe pressure = 18.466 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.550720846833133 m\n", - "Volume in reservoir = 18.550720846833133 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.2153859347887472 m³/s \n", - "Current outflux vel = 1.5474774342879447 m/s \n", - "Current pipe pressure = 18.453 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.54275741439271 m\n", - "Volume in reservoir = 18.54275741439271 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.318837599337019 m³/s \n", - "Current outflux vel = 1.679196184559481 m/s \n", - "Current pipe pressure = 18.432 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.53162832251812 m\n", - "Volume in reservoir = 18.53162832251812 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.44549979155596 m³/s \n", - "Current outflux vel = 1.840467496515483 m/s \n", - "Current pipe pressure = 18.403 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.517095820507148 m\n", - "Volume in reservoir = 18.517095820507148 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.5816918936822966 m³/s \n", - "Current outflux vel = 2.0138726666233446 m/s \n", - "Current pipe pressure = 18.364 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.499464182980322 m\n", - "Volume in reservoir = 18.499464182980322 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.7057348055189903 m³/s \n", - "Current outflux vel = 2.1718090072179206 m/s \n", - "Current pipe pressure = 18.318 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.479642040111894 m\n", - "Volume in reservoir = 18.479642040111894 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = 1.793448297595253 m³/s \n", - "Current outflux vel = 2.2834892939362326 m/s \n", - "Current pipe pressure = 18.27 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.552909736297963 m\n", - "Volume in reservoir = 18.552909736297963 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.9297622374205485 m³/s \n", - "Current outflux vel = -2.4570495926204483 m/s \n", - "Current pipe pressure = 18.485 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.62544869149267 m\n", - "Volume in reservoir = 18.62544869149267 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.9006435636267554 m³/s \n", - "Current outflux vel = -2.4199745456559474 m/s \n", - "Current pipe pressure = 18.559 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.69625159603343 m\n", - "Volume in reservoir = 18.69625159603343 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.829753034235658 m³/s \n", - "Current outflux vel = -2.329713920287992 m/s \n", - "Current pipe pressure = 18.339 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.764410509181403 m\n", - "Volume in reservoir = 18.764410509181403 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.7241853107589202 m³/s \n", - "Current outflux vel = -2.1953009201097426 m/s \n", - "Current pipe pressure = 18.431 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.829572774129115 m\n", - "Volume in reservoir = 18.829572774129115 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.604545741057279 m³/s \n", - "Current outflux vel = -2.042971088850514 m/s \n", - "Current pipe pressure = 18.525 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.8918384232376 m\n", - "Volume in reservoir = 18.8918384232376 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.4889181988735198 m³/s \n", - "Current outflux vel = -1.895749529681619 m/s \n", - "Current pipe pressure = 18.621 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 18.951663662343275 m\n", - "Volume in reservoir = 18.951663662343275 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3915087591294568 m³/s \n", - "Current outflux vel = -1.7717239789689807 m/s \n", - "Current pipe pressure = 18.712 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.0096576459752 m\n", - "Volume in reservoir = 19.0096576459752 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3184206119435584 m³/s \n", - "Current outflux vel = -1.678665259720471 m/s \n", - "Current pipe pressure = 18.794 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.066434121575515 m\n", - "Volume in reservoir = 19.066434121575515 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.26983505669192 m³/s \n", - "Current outflux vel = -1.6168042094711699 m/s \n", - "Current pipe pressure = 18.869 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.12251142725041 m\n", - "Volume in reservoir = 19.12251142725041 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.2419421616578004 m³/s \n", - "Current outflux vel = -1.5812898724965816 m/s \n", - "Current pipe pressure = 18.937 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.178335430154217 m\n", - "Volume in reservoir = 19.178335430154217 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.231849879720882 m³/s \n", - "Current outflux vel = -1.5684399800378808 m/s \n", - "Current pipe pressure = 18.999 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.234296845674997 m\n", - "Volume in reservoir = 19.234296845674997 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.237358473385065 m³/s \n", - "Current outflux vel = -1.5754537393269963 m/s \n", - "Current pipe pressure = 19.057 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.290804866064118 m\n", - "Volume in reservoir = 19.290804866064118 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.2592084327138677 m³/s \n", - "Current outflux vel = -1.6032739715952828 m/s \n", - "Current pipe pressure = 19.112 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.348325477938122 m\n", - "Volume in reservoir = 19.348325477938122 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.2996699110671446 m³/s \n", - "Current outflux vel = -1.6547911258731207 m/s \n", - "Current pipe pressure = 19.164 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.407434594672747 m\n", - "Volume in reservoir = 19.407434594672747 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3631332677350856 m³/s \n", - "Current outflux vel = -1.735595181224375 m/s \n", - "Current pipe pressure = 19.212 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.468740686475215 m\n", - "Volume in reservoir = 19.468740686475215 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.4508947592885293 m³/s \n", - "Current outflux vel = -1.8473365827751607 m/s \n", - "Current pipe pressure = 19.257 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.532737913075014 m\n", - "Volume in reservoir = 19.532737913075014 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.5583797752166255 m³/s \n", - "Current outflux vel = -1.9841907555213014 m/s \n", - "Current pipe pressure = 19.298 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.599587225735338 m\n", - "Volume in reservoir = 19.599587225735338 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6722703302326978 m³/s \n", - "Current outflux vel = -2.1292007139396003 m/s \n", - "Current pipe pressure = 19.337 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.668995725740444 m\n", - "Volume in reservoir = 19.668995725740444 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.7744364122873317 m³/s \n", - "Current outflux vel = -2.259282609742218 m/s \n", - "Current pipe pressure = 19.376 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.740178231267095 m\n", - "Volume in reservoir = 19.740178231267095 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.8452248461600278 m³/s \n", - "Current outflux vel = -2.3494132430588044 m/s \n", - "Current pipe pressure = 19.419 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.811991551065002 m\n", - "Volume in reservoir = 19.811991551065002 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.8703543568157308 m³/s \n", - "Current outflux vel = -2.381409129765489 m/s \n", - "Current pipe pressure = 19.471 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.88314650827768 m\n", - "Volume in reservoir = 19.88314650827768 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.8440134449659553 m³/s \n", - "Current outflux vel = -2.347870839153972 m/s \n", - "Current pipe pressure = 19.536 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 19.952480606874957 m\n", - "Volume in reservoir = 19.952480606874957 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.771272355376768 m³/s \n", - "Current outflux vel = -2.2552540073618954 m/s \n", - "Current pipe pressure = 19.614 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.019169056848003 m\n", - "Volume in reservoir = 20.019169056848003 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6656181232759562 m³/s \n", - "Current outflux vel = -2.1207308609825146 m/s \n", - "Current pipe pressure = 19.703 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.082862880802534 m\n", - "Volume in reservoir = 20.082862880802534 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.5460450090846662 m³/s \n", - "Current outflux vel = -1.968485643507031 m/s \n", - "Current pipe pressure = 19.797 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.143665899205274 m\n", - "Volume in reservoir = 20.143665899205274 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.4306265211857643 m³/s \n", - "Current outflux vel = -1.8215302605206123 m/s \n", - "Current pipe pressure = 19.89 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.202035453987467 m\n", - "Volume in reservoir = 20.202035453987467 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.333474026883796 m³/s \n", - "Current outflux vel = -1.6978318629056885 m/s \n", - "Current pipe pressure = 19.978 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.25858041677155 m\n", - "Volume in reservoir = 20.25858041677155 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.2606355326832912 m³/s \n", - "Current outflux vel = -1.6050910117106432 m/s \n", - "Current pipe pressure = 20.058 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.31391283692246 m\n", - "Volume in reservoir = 20.31391283692246 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.212236295646925 m³/s \n", - "Current outflux vel = -1.543467189180931 m/s \n", - "Current pipe pressure = 20.131 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.36854998400715 m\n", - "Volume in reservoir = 20.36854998400715 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.184491077513108 m³/s \n", - "Current outflux vel = -1.508140880275652 m/s \n", - "Current pipe pressure = 20.197 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.422936772229544 m\n", - "Volume in reservoir = 20.422936772229544 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.174511880961967 m³/s \n", - "Current outflux vel = -1.4954349726020546 m/s \n", - "Current pipe pressure = 20.257 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.47746296858388 m\n", - "Volume in reservoir = 20.47746296858388 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.1800985233334242 m³/s \n", - "Current outflux vel = -1.502548106591687 m/s \n", - "Current pipe pressure = 20.314 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.53253734832926 m\n", - "Volume in reservoir = 20.53253734832926 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.2020125688419678 m³/s \n", - "Current outflux vel = -1.5304499359182906 m/s \n", - "Current pipe pressure = 20.367 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.58862570218034 m\n", - "Volume in reservoir = 20.58862570218034 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.2425331126827037 m³/s \n", - "Current outflux vel = -1.5820422947104904 m/s \n", - "Current pipe pressure = 20.418 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.64630312156812 m\n", - "Volume in reservoir = 20.64630312156812 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3060260513824604 m³/s \n", - "Current outflux vel = -1.6628840150744661 m/s \n", - "Current pipe pressure = 20.465 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.70617847484485 m\n", - "Volume in reservoir = 20.70617847484485 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.393836813657412 m³/s \n", - "Current outflux vel = -1.774688150056273 m/s \n", - "Current pipe pressure = 20.509 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.768747629193925 m\n", - "Volume in reservoir = 20.768747629193925 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.5014431048521455 m³/s \n", - "Current outflux vel = -1.911696735267695 m/s \n", - "Current pipe pressure = 20.55 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.83417492638746 m\n", - "Volume in reservoir = 20.83417492638746 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.615592963616129 m³/s \n", - "Current outflux vel = -2.0570368494719324 m/s \n", - "Current pipe pressure = 20.588 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.90217178725596 m\n", - "Volume in reservoir = 20.90217178725596 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.7181911431919652 m³/s \n", - "Current outflux vel = -2.1876689089257266 m/s \n", - "Current pipe pressure = 20.626 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 20.97195232918963 m\n", - "Volume in reservoir = 20.97195232918963 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.7893805171107589 m³/s \n", - "Current outflux vel = -2.278310034964073 m/s \n", - "Current pipe pressure = 20.669 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.04237003305259 m\n", - "Volume in reservoir = 21.04237003305259 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.8147716545636676 m³/s \n", - "Current outflux vel = -2.310639035254922 m/s \n", - "Current pipe pressure = 20.72 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.11213179166616 m\n", - "Volume in reservoir = 21.11213179166616 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.7885269706181088 m³/s \n", - "Current outflux vel = -2.2772232658163607 m/s \n", - "Current pipe pressure = 20.784 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.180071479380477 m\n", - "Volume in reservoir = 21.180071479380477 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.7157286492253474 m³/s \n", - "Current outflux vel = -2.184533564228757 m/s \n", - "Current pipe pressure = 20.86 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.245363112832944 m\n", - "Volume in reservoir = 21.245363112832944 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6099627954092735 m³/s \n", - "Current outflux vel = -2.0498682966674533 m/s \n", - "Current pipe pressure = 20.947 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.30766139723813 m\n", - "Volume in reservoir = 21.30766139723813 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.4904215775794276 m³/s \n", - "Current outflux vel = -1.8976636909006934 m/s \n", - "Current pipe pressure = 21.039 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.367072877230736 m\n", - "Volume in reservoir = 21.367072877230736 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3751433915649605 m³/s \n", - "Current outflux vel = -1.750886945821738 m/s \n", - "Current pipe pressure = 21.13 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.42405596720504 m\n", - "Volume in reservoir = 21.42405596720504 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.2781761438437853 m³/s \n", - "Current outflux vel = -1.627424411479007 m/s \n", - "Current pipe pressure = 21.215 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.479219286337575 m\n", - "Volume in reservoir = 21.479219286337575 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.2055162622053546 m³/s \n", - "Current outflux vel = -1.5349109768611808 m/s \n", - "Current pipe pressure = 21.293 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.5331734126517 m\n", - "Volume in reservoir = 21.5331734126517 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.1572406807426256 m³/s \n", - "Current outflux vel = -1.4734445974977506 m/s \n", - "Current pipe pressure = 21.363 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.5864349825532 m\n", - "Volume in reservoir = 21.5864349825532 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.1295972698818353 m³/s \n", - "Current outflux vel = -1.4382479136384307 m/s \n", - "Current pipe pressure = 21.427 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.63944855218317 m\n", - "Volume in reservoir = 21.63944855218317 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.119708542859994 m³/s \n", - "Current outflux vel = -1.425657195347131 m/s \n", - "Current pipe pressure = 21.486 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.69260373407292 m\n", - "Volume in reservoir = 21.69260373407292 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.1253819206024565 m³/s \n", - "Current outflux vel = -1.4328807642410546 m/s \n", - "Current pipe pressure = 21.541 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.746309469323027 m\n", - "Volume in reservoir = 21.746309469323027 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.1473916339027932 m³/s \n", - "Current outflux vel = -1.460904401583327 m/s \n", - "Current pipe pressure = 21.593 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.801031752450093 m\n", - "Volume in reservoir = 21.801031752450093 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.1880183868977128 m³/s \n", - "Current outflux vel = -1.5126319900706462 m/s \n", - "Current pipe pressure = 21.643 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.857345033994374 m\n", - "Volume in reservoir = 21.857345033994374 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.251594797955796 m³/s \n", - "Current outflux vel = -1.5935799907421357 m/s \n", - "Current pipe pressure = 21.689 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.91585856179202 m\n", - "Volume in reservoir = 21.91585856179202 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3395074426143718 m³/s \n", - "Current outflux vel = -1.705513846403685 m/s \n", - "Current pipe pressure = 21.732 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 21.97706978427533 m\n", - "Volume in reservoir = 21.97706978427533 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.4472820617679163 m³/s \n", - "Current outflux vel = -1.8427367534287493 m/s \n", - "Current pipe pressure = 21.772 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 22.041146392852603 m\n", - "Volume in reservoir = 22.041146392852603 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.5617363219374585 m³/s \n", - "Current outflux vel = -1.9884644435400172 m/s \n", - "Current pipe pressure = 21.81 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 22.107803712027494 m\n", - "Volume in reservoir = 22.107803712027494 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6647951002678616 m³/s \n", - "Current outflux vel = -2.1196829555423817 m/s \n", - "Current pipe pressure = 21.848 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 22.17625457106648 m\n", - "Volume in reservoir = 22.17625457106648 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.736390899028211 m³/s \n", - "Current outflux vel = -2.2108415577609595 m/s \n", - "Current pipe pressure = 21.89 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 22.245348792607786 m\n", - "Volume in reservoir = 22.245348792607786 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.7620368280919678 m³/s \n", - "Current outflux vel = -2.243494968806407 m/s \n", - "Current pipe pressure = 21.94 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 22.31378905643092 m\n", - "Volume in reservoir = 22.31378905643092 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.7358713853227865 m³/s \n", - "Current outflux vel = -2.210180092367181 m/s \n", - "Current pipe pressure = 22.003 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 22.380405292741628 m\n", - "Volume in reservoir = 22.380405292741628 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6629874159457236 m³/s \n", - "Current outflux vel = -2.117381340379038 m/s \n", - "Current pipe pressure = 22.078 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 22.444369720484765 m\n", - "Volume in reservoir = 22.444369720484765 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.5570582824005497 m³/s \n", - "Current outflux vel = -1.9825081786097902 m/s \n", - "Current pipe pressure = 22.163 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 22.505340416528007 m\n", - "Volume in reservoir = 22.505340416528007 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.4374853863000652 m³/s \n", - "Current outflux vel = -1.8302632388161446 m/s \n", - "Current pipe pressure = 22.252 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 22.563426627312 m\n", - "Volume in reservoir = 22.563426627312 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3222831032412903 m³/s \n", - "Current outflux vel = -1.6835831363819387 m/s \n", - "Current pipe pressure = 22.341 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 22.619087753692764 m\n", - "Volume in reservoir = 22.619087753692764 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.225432974972575 m³/s \n", - "Current outflux vel = -1.5602697231575373 m/s \n", - "Current pipe pressure = 22.424 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 22.672932180452573 m\n", - "Volume in reservoir = 22.672932180452573 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.152883810664468 m³/s \n", - "Current outflux vel = -1.467897258222967 m/s \n", - "Current pipe pressure = 22.5 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 22.725569225918324 m\n", - "Volume in reservoir = 22.725569225918324 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.1046719032623575 m³/s \n", - "Current outflux vel = -1.4065119511914899 m/s \n", - "Current pipe pressure = 22.568 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 22.777515299268664 m\n", - "Volume in reservoir = 22.777515299268664 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.0770860589752504 m³/s \n", - "Current outflux vel = -1.3713885633702385 m/s \n", - "Current pipe pressure = 22.63 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 22.829215155062776 m\n", - "Volume in reservoir = 22.829215155062776 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.0672653883661851 m³/s \n", - "Current outflux vel = -1.358884497194958 m/s \n", - "Current pipe pressure = 22.687 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 22.881059056997156 m\n", - "Volume in reservoir = 22.881059056997156 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.0730349506435433 m³/s \n", - "Current outflux vel = -1.3662305320423027 m/s \n", - "Current pipe pressure = 22.741 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 22.93345666132728 m\n", - "Volume in reservoir = 22.93345666132728 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.0951714201729486 m³/s \n", - "Current outflux vel = -1.3944155604279667 m/s \n", - "Current pipe pressure = 22.792 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 22.986874536293662 m\n", - "Volume in reservoir = 22.986874536293662 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.135949926222516 m³/s \n", - "Current outflux vel = -1.4463363669054978 m/s \n", - "Current pipe pressure = 22.84 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 23.0418866650965 m\n", - "Volume in reservoir = 23.0418866650965 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.1996618962089072 m³/s \n", - "Current outflux vel = -1.527456966565151 m/s \n", - "Current pipe pressure = 22.885 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 23.0991026451491 m\n", - "Volume in reservoir = 23.0991026451491 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.287726882600313 m³/s \n", - "Current outflux vel = -1.639584789745253 m/s \n", - "Current pipe pressure = 22.928 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 23.159021371898923 m\n", - "Volume in reservoir = 23.159021371898923 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3957145039788712 m³/s \n", - "Current outflux vel = -1.7770788996263214 m/s \n", - "Current pipe pressure = 22.967 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 23.22181402800154 m\n", - "Volume in reservoir = 23.22181402800154 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.510523234357305 m³/s \n", - "Current outflux vel = -1.9232579152249805 m/s \n", - "Current pipe pressure = 23.004 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 23.287199037747357 m\n", - "Volume in reservoir = 23.287199037747357 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6140605668895498 m³/s \n", - "Current outflux vel = -2.055085741361429 m/s \n", - "Current pipe pressure = 23.042 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 23.3543876246899 m\n", - "Volume in reservoir = 23.3543876246899 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6860684103525867 m³/s \n", - "Current outflux vel = -2.146768975189667 m/s \n", - "Current pipe pressure = 23.083 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 23.422225646046805 m\n", - "Volume in reservoir = 23.422225646046805 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.7119631933289958 m³/s \n", - "Current outflux vel = -2.179739236877566 m/s \n", - "Current pipe pressure = 23.133 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 23.489411304268124 m\n", - "Volume in reservoir = 23.489411304268124 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6858614150340647 m³/s \n", - "Current outflux vel = -2.1465054205645497 m/s \n", - "Current pipe pressure = 23.194 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 23.554770290090087 m\n", - "Volume in reservoir = 23.554770290090087 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6128653438851728 m³/s \n", - "Current outflux vel = -2.053563936167479 m/s \n", - "Current pipe pressure = 23.268 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 23.617472717969513 m\n", - "Volume in reservoir = 23.617472717969513 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.506734951780665 m³/s \n", - "Current outflux vel = -1.9184345240417713 m/s \n", - "Current pipe pressure = 23.351 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 23.677179268015884 m\n", - "Volume in reservoir = 23.677179268015884 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.387062108090111 m³/s \n", - "Current outflux vel = -1.766062327024048 m/s \n", - "Current pipe pressure = 23.439 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 23.734002075881172 m\n", - "Volume in reservoir = 23.734002075881172 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.27187500047835 m³/s \n", - "Current outflux vel = -1.6194015465690892 m/s \n", - "Current pipe pressure = 23.525 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 23.78840143427218 m\n", - "Volume in reservoir = 23.78840143427218 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.175077256258814 m³/s \n", - "Current outflux vel = -1.4961548307876167 m/s \n", - "Current pipe pressure = 23.606 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 23.8409854938324 m\n", - "Volume in reservoir = 23.8409854938324 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.102573683544752 m³/s \n", - "Current outflux vel = -1.4038404148734913 m/s \n", - "Current pipe pressure = 23.679 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 23.892362522127698 m\n", - "Volume in reservoir = 23.892362522127698 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.0543682339790428 m³/s \n", - "Current outflux vel = -1.342463330214694 m/s \n", - "Current pipe pressure = 23.746 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 23.943049051753 m\n", - "Volume in reservoir = 23.943049051753 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.0267963646884244 m³/s \n", - "Current outflux vel = -1.3073577359116095 m/s \n", - "Current pipe pressure = 23.806 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 23.993490583816477 m\n", - "Volume in reservoir = 23.993490583816477 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.0170217344262042 m³/s \n", - "Current outflux vel = -1.294912290126586 m/s \n", - "Current pipe pressure = 23.862 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 24.044078840215036 m\n", - "Volume in reservoir = 24.044078840215036 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.022897453836472 m³/s \n", - "Current outflux vel = -1.3023934884335067 m/s \n", - "Current pipe pressure = 23.914 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 24.095224740713782 m\n", - "Volume in reservoir = 24.095224740713782 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.0451923162118377 m³/s \n", - "Current outflux vel = -1.3307801888542505 m/s \n", - "Current pipe pressure = 23.964 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 24.147395739528175 m\n", - "Volume in reservoir = 24.147395739528175 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.0861664258359045 m³/s \n", - "Current outflux vel = -1.382950045537926 m/s \n", - "Current pipe pressure = 24.011 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 24.201165526676114 m\n", - "Volume in reservoir = 24.201165526676114 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.1500644448293835 m³/s \n", - "Current outflux vel = -1.464307530150662 m/s \n", - "Current pipe pressure = 24.056 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 24.25714400653118 m\n", - "Volume in reservoir = 24.25714400653118 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.2383302053362941 m³/s \n", - "Current outflux vel = -1.5766909868741836 m/s \n", - "Current pipe pressure = 24.097 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 24.31583138062451 m\n", - "Volume in reservoir = 24.31583138062451 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.3465732811783269 m³/s \n", - "Current outflux vel = -1.7145103514800273 m/s \n", - "Current pipe pressure = 24.136 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 24.377402465571596 m\n", - "Volume in reservoir = 24.377402465571596 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.4617844188632798 m³/s \n", - "Current outflux vel = -1.8612017279744368 m/s \n", - "Current pipe pressure = 24.173 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 24.4415779582571 m\n", - "Volume in reservoir = 24.4415779582571 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.5658151860839618 m³/s \n", - "Current outflux vel = -1.9936578146689476 m/s \n", - "Current pipe pressure = 24.21 mWS \n", - "----------------------------- \n", - "\n", - "The current attributes are: \n", - "----------------------------- \n", - "Current level = 24.507567237955474 m\n", - "Volume in reservoir = 24.507567237955474 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6382407090070015 m³/s \n", - "Current outflux vel = -2.085872854502685 m/s \n", - "Current pipe pressure = 24.251 mWS \n", - "----------------------------- \n", - "\n" - ] - } - ], - "source": [ - "# time loop\n", - "%matplotlib qt5\n", - "\n", - "# create a figure and subplots to display the velocity and pressure distribution across the pipeline in each pipeline step\n", - "fig1,axs1 = plt.subplots(2,1)\n", - "fig1.suptitle(str(0) +' s / '+str(round(t_vec[-1],2)) + ' s' )\n", - "axs1[0].set_title('Pressure distribution in pipeline')\n", - "axs1[1].set_title('Velocity distribution in pipeline')\n", - "axs1[0].set_xlabel(r'$x$ [$\\mathrm{m}$]')\n", - "axs1[0].set_ylabel(r'$p$ ['+conversion_pressure_unit+']')\n", - "axs1[1].set_xlabel(r'$x$ [$\\mathrm{m}$]')\n", - "axs1[1].set_ylabel(r'$v$ [$\\mathrm{m} / \\mathrm{s}$]')\n", - "lo_00, = axs1[0].plot(pl_vec,pressure_conversion(pipe.p_old,initial_pressure_unit, conversion_pressure_unit)[0],marker='.')\n", - "lo_01, = axs1[1].plot(pl_vec,pipe.v_old,marker='.')\n", - "axs1[0].autoscale()\n", - "axs1[1].autoscale()\n", - "# displaying the reservoir level within each pipeline timestep\n", - "# axs1[2].set_title('Level reservoir')\n", - "# axs1[2].set_xlabel(r'$t$ [$\\mathrm{s}$]')\n", - "# axs1[2].set_ylabel(r'$h$ [m]')\n", - "# lo_02, = axs1[2].plot(level_vec_2)\n", - "# axs1[2].autoscale()\n", - "fig1.tight_layout()\n", - "fig1.show()\n", - "plt.pause(2)\n", - "\n", - "# loop through time steps of the pipeline\n", - "for it_pipe in range(1,pipe.nt+1):\n", - "\n", - "# for each pipeline timestep, execute nt_eRK4 timesteps of the reservoir code\n", - " # set initial conditions for the reservoir time evolution calculated with e-RK4\n", - " V.pressure = p_old[0]\n", - " V.outflux_vel = v_old[0]\n", - " # V.influx = v_boundary_tur[it_pipe]\n", - " # calculate the time evolution of the reservoir level within each pipeline timestep to avoid runaway numerical error\n", - " for it_res in range(nt_eRK4):\n", - " V.e_RK_4() # call e-RK4 to update outflux\n", - " V.level = V.update_level(V.timestep) # \n", - " V.set_volume() # update volume in reservoir\n", - " level_vec_2[it_res] = V.level # save for plotting\n", - " if (V.level < critical_level_low) or (V.level > critical_level_high): # make sure to never exceed critical levels\n", - " i_max = it_pipe # for plotting only calculated values\n", - " break \n", - " level_vec[it_pipe] = V.level \n", - "\n", - " V.get_info()\n", - "\n", - " # set boundary conditions for the next timestep of the characteristic method\n", - " p_boundary_res[it_pipe] = rho*g*V.level-v_old[1]**2*rho/2\n", - " v_boundary_res[it_pipe] = v_old[1]+1/(rho*c)*(p_boundary_res[it_pipe]-p_old[1])-f_D*dt/(2*D)*abs(v_old[1])*v_old[1] \\\n", - " +dt*g*np.sin(alpha)\n", - " \n", - "\n", - " # the the boundary conditions in the pipe.object and thereby calculate boundary pressure at turbine\n", - " pipe.set_boundary_conditions_next_timestep(v_boundary_res[it_pipe],p_boundary_res[it_pipe],v_boundary_tur[it_pipe])\n", - " p_boundary_tur[it_pipe] = pipe.p_boundary_tur\n", - "\n", - " # perform the next timestep via the characteristic method\n", - " pipe.timestep_characteristic_method()\n", - "\n", - " # plot some stuff\n", - " # remove line-objects to autoscale axes (there is definetly a better way, but this works ¯\\_(ツ)_/¯ )\n", - " lo_00.remove()\n", - " lo_01.remove()\n", - " # lo_02.remove()\n", - " # plot new pressure and velocity distribution in the pipeline\n", - " lo_00, = axs1[0].plot(pl_vec,pressure_conversion(pipe.p_old,initial_pressure_unit, conversion_pressure_unit)[0],marker='.',c='blue')\n", - " lo_01, = axs1[1].plot(pl_vec,pipe.v_old,marker='.',c='blue')\n", - " # lo_02, = axs1[2].plot(level_vec_2,c='blue')\n", - " fig1.suptitle(str(round(t_vec[it_pipe],2))+ ' s / '+str(round(t_vec[-1],2)) + ' s' )\n", - " fig1.canvas.draw()\n", - " fig1.tight_layout()\n", - " fig1.show()\n", - " plt.pause(0.00001) \n", - "\n", - " # prepare for next loop\n", - " p_old = pipe.p_old\n", - " v_old = pipe.v_old \n", - "\n", - " \n", - " " - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [], - "source": [ - "# plot time evolution of boundary pressure and velocity as well as the reservoir level\n", - "\n", - "fig2,axs2 = plt.subplots(3,2)\n", - "axs2[0,0].plot(t_vec,pressure_conversion(p_boundary_res,initial_pressure_unit, conversion_pressure_unit)[0])\n", - "axs2[0,1].plot(t_vec,v_boundary_res)\n", - "axs2[1,0].plot(t_vec,pressure_conversion(p_boundary_tur,initial_pressure_unit, conversion_pressure_unit)[0])\n", - "axs2[1,1].plot(t_vec,v_boundary_tur)\n", - "axs2[2,0].plot(t_vec,level_vec)\n", - "axs2[0,0].set_title('Pressure reservoir')\n", - "axs2[0,1].set_title('Velocity reservoir')\n", - "axs2[1,0].set_title('Pressure turbine')\n", - "axs2[1,1].set_title('Velocity turbine')\n", - "axs2[2,0].set_title('Level reservoir')\n", - "axs2[0,0].set_xlabel(r'$t$ [$\\mathrm{s}$]')\n", - "axs2[0,0].set_ylabel(r'$p$ ['+conversion_pressure_unit+']')\n", - "axs2[0,1].set_xlabel(r'$t$ [$\\mathrm{s}$]')\n", - "axs2[0,1].set_ylabel(r'$v$ [$\\mathrm{m}/\\mathrm{s}$]')\n", - "axs2[1,0].set_xlabel(r'$t$ [$\\mathrm{s}$]')\n", - "axs2[1,0].set_ylabel(r'$p$ ['+conversion_pressure_unit+']')\n", - "axs2[1,1].set_xlabel(r'$t$ [$\\mathrm{s}$]')\n", - "axs2[1,1].set_ylabel(r'$v$ [$\\mathrm{m}/\\mathrm{s}$]')\n", - "axs2[2,0].set_xlabel(r'$t$ [$\\mathrm{s}$]')\n", - "axs2[2,0].set_ylabel(r'$h$ [m]')\n", - "axs2[2,1].axis('off')\n", - "fig2.tight_layout()\n", - "plt.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The cuboid reservoir has the following attributes: \n", - "----------------------------- \n", - "Base area = 1.0 m² \n", - "Outflux area = 0.785 m² \n", - "Current level = 24.507567237955474 m\n", - "Critical level low = 0.0 m \n", - "Critical level high = inf m \n", - "Volume in reservoir = 24.507567237955474 m³ \n", - "Current influx = 1.0 m³/s \n", - "Current outflux = -1.6382407090070015 m³/s \n", - "Current outflux vel = -2.085872854502685 m/s \n", - "Current pipe pressure = 24.251 mWS \n", - "Simulation timestep = 2.5e-05 s \n", - "----------------------------- \n", - "\n" - ] - } - ], - "source": [ - "V.get_info(full=True)" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.13 ('DT_Slot_3')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Regler/Regler_class_file.py b/Regler/Regler_class_file.py new file mode 100644 index 0000000..01597de --- /dev/null +++ b/Regler/Regler_class_file.py @@ -0,0 +1,35 @@ +import numpy as np +#based on https://en.wikipedia.org/wiki/PID_controller#Discrete_implementation + + +class P_controller_class: + def __init__(self,setpoint,proportionality_constant): + self.SP = setpoint + self.Kp = proportionality_constant + self.error_history = [] + self.control_variable = 0.1 + self.lower_limit = -0.1 # default + self.upper_limit = +0.1 # default + + def set_control_variable_limits(self,lower_limit,upper_limit): + self.lower_limit = lower_limit + self.upper_limit = upper_limit + + def calculate_error(self,process_variable): + self.error = self.SP-process_variable + self.error_history.append(self.error) + + def get_control_variable(self): + new_control = self.control_variable+self.Kp*(self.error_history[-1]-self.error_history[-2]) + if new_control < self.lower_limit: + new_control = self.lower_limit + + if new_control > self.upper_limit: + new_control = self.upper_limit + + self.control_variable = new_control + # print(new_control) + return new_control + + + diff --git a/Regler/regler_test.ipynb b/Regler/regler_test.ipynb new file mode 100644 index 0000000..704ad3b --- /dev/null +++ b/Regler/regler_test.ipynb @@ -0,0 +1,148 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from Regler_class_file import P_controller_class" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "controller = P_controller_class(setpoint=1,proportionality_constant=0.1)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "t_max = 100 #s\n", + "dt = 0.1 #s\n", + "nt = int(t_max//dt)\n", + "t_vec = np.arange(0,nt+1,1)*dt\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "PV_0 = 0.5\n", + "\n", + "PV_vec = np.full_like(t_vec,PV_0)\n", + "controller.calculate_error(PV_vec[0])\n", + "controller.calculate_error(PV_vec[0])\n", + "\n", + "\n", + "for i in range(2,nt+1):\n", + " controller.calculate_error(PV_vec[i-1])\n", + "\n", + " if i == 100:\n", + " controller.SP = 0.\n", + " PV_vec[i] = PV_vec[i-1]+controller.get_control_variable()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "befd47eba60b46139eda036a958f6ca7", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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\n", + " Figure\n", + "
\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib widget\n", + "\n", + "plt.plot(t_vec,PV_vec,'+')" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.1\n" + ] + } + ], + "source": [ + "print(controller.lower_limit)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.13 ('DT_Slot_3')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Turbinen/Turbinen_class_file.py b/Turbinen/Turbinen_class_file.py new file mode 100644 index 0000000..908e692 --- /dev/null +++ b/Turbinen/Turbinen_class_file.py @@ -0,0 +1,19 @@ +def turbine_flux(p,LA,p_exp,cubic_coeff,quadratic_coeff,linear_coeff,const_coeff): + return (p*1e-5)**p_exp*(cubic_coeff*LA**3+quadratic_coeff*LA**2+linear_coeff*LA+const_coeff) + + +class Francis_turbine_class: + def __init__(self): + pass + + def set_turbine_flux_parameters(self,p_exp,cubic_coeff,quadratic_coeff,linear_coeff,const_coeff): + # extracted from the Muschelkurve of the Turbine and used to calculate the turbine flux for a given pressure + self.p_exp = p_exp + self.cubic_coeff = cubic_coeff + self.quadratic_coeff = quadratic_coeff + self.linear_coeff = linear_coeff + self.const_coeff = const_coeff + + def get_turbine_flux(self,pressure,Leitapparatöffnung): + self.flux = turbine_flux(pressure,Leitapparatöffnung,self.p_exp,self.cubic_coeff,self.quadratic_coeff,self.linear_coeff,self.const_coeff) + return self.flux \ No newline at end of file diff --git a/Turbinen/messy.ipynb b/Turbinen/messy.ipynb new file mode 100644 index 0000000..adbe507 --- /dev/null +++ b/Turbinen/messy.ipynb @@ -0,0 +1,174 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from Turbinen_class_file import Francis_turbine_class\n", + "#importing pressure conversion function\n", + "import sys\n", + "import os\n", + "current = os.path.dirname(os.path.realpath('Main_Programm.ipynb'))\n", + "parent = os.path.dirname(current)\n", + "sys.path.append(parent)\n", + "from functions.pressure_conversion import pressure_conversion\n", + "from matplotlib import pyplot as plt\n", + "\n", + "p_exp=0.75\n", + "cubic_coeff=-0.7157\n", + "quadratic_coeff=0.9374\n", + "linear_coeff=0.9696\n", + "const_coeff=-0.0011\n", + "T1 = Francis_turbine_class()\n", + "T1.set_turbine_flux_parameters(p_exp,cubic_coeff,quadratic_coeff,linear_coeff,const_coeff)\n", + "\n", + "pressure,_ = pressure_conversion(5,'bar','Pa')\n", + "\n", + "LA_vec = np.array([0,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.,1.05,1.1,1.15])/1.18\n", + "flux_vec1 = np.empty_like(LA_vec)\n", + "\n", + "n = np.size(flux_vec1)\n", + "for i in range(n):\n", + " flux_vec1[i] = T1.get_turbine_flux(pressure,LA_vec[i])\n", + "\n", + "LA = 0.5\n", + "pressure_vec = np.linspace(0,pressure,500)\n", + "flux_vec2 = np.empty_like(pressure_vec)\n", + "m = np.size(flux_vec2)\n", + "\n", + "for i in range(m):\n", + " flux_vec2[i] = T1.get_turbine_flux(pressure_vec[i],LA)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + 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+ "text/html": [ + "\n", + "
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\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib widget\n", + "fig1 = plt.figure()\n", + "plt.plot(LA_vec,flux_vec1)\n", + "\n", + "fig2 = plt.figure()\n", + "plt.plot(pressure_vec,flux_vec2)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[0. 0.33898305 0.38135593 0.42372881 0.46610169 0.50847458\n", + " 0.55084746 0.59322034 0.63559322 0.6779661 0.72033898 0.76271186\n", + " 0.80508475 0.84745763 0.88983051 0.93220339 0.97457627]\n", + "[-3.67807168e-03 1.36227725e+00 1.55581498e+00 1.75077663e+00\n", + " 1.94606981e+00 2.14060215e+00 2.33328126e+00 2.52301477e+00\n", + " 2.70871029e+00 2.88927545e+00 3.06361786e+00 3.23064514e+00\n", + " 3.38926492e+00 3.53838482e+00 3.67691244e+00 3.80375543e+00\n", + " 3.91782138e+00]\n" + ] + } + ], + "source": [ + "print(LA_vec)\n", + "print(flux_vec1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.13 ('DT_Slot_3')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} From 6d56e3d1f28ae5ca83f82586c367fe903ce05d0f Mon Sep 17 00:00:00 2001 From: Brantegger Georg Date: Mon, 18 Jul 2022 08:00:29 +0200 Subject: [PATCH 03/12] updated test programm for the Ausgleichsbecken class to reflect the use Pa as pressure unit within the class --- Ausgleichsbecken/Main_Program.ipynb | 35 +++++++++++++++++------------ 1 file changed, 21 insertions(+), 14 deletions(-) diff --git a/Ausgleichsbecken/Main_Program.ipynb b/Ausgleichsbecken/Main_Program.ipynb index 5cf46d7..9ca27cd 100644 --- a/Ausgleichsbecken/Main_Program.ipynb +++ b/Ausgleichsbecken/Main_Program.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 16, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -21,16 +21,16 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ "# define constants\n", "initial_level = 5. # m\n", - "initial_influx = 0.5 # m³/s\n", + "initial_influx = 0. # m³/s\n", "initial_outflux = 0. # m³/s\n", - "initial_pipeline_pressure = 1\n", - "initial_pressure_unit = 'bar'\n", + "initial_pipeline_pressure = 5.\n", + "initial_pressure_unit = 'mWS'\n", "conversion_pressure_unit = 'mWS'\n", "\n", "area_base = 1. # m²\n", @@ -46,7 +46,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ @@ -57,7 +57,7 @@ "V.set_influx(initial_influx)\n", "V.set_outflux(initial_outflux)\n", "\n", - "converted_pressure, V.pressure_unit = pressure_conversion(initial_pipeline_pressure,input_unit = initial_pressure_unit, target_unit = conversion_pressure_unit)\n", + "converted_pressure,_ = pressure_conversion(initial_pipeline_pressure,input_unit = initial_pressure_unit, target_unit = 'Pa')\n", "V.pressure = converted_pressure\n", "\n", "time_vec = np.arange(0,total_max_time,simulation_timestep)\n", @@ -66,8 +66,7 @@ "level_vec = np.empty_like(time_vec)\n", "level_vec[0] = initial_level\n", "\n", - "pressure_vec = np.full_like(time_vec,converted_pressure)*((np.sin(time_vec/5)+1)*np.exp(-time_vec/50))\n", - " \n", + "pressure_vec = np.full_like(time_vec,converted_pressure)*((np.sin(time_vec)+1)*np.exp(-time_vec/50))\n", " \n", "i_max = -1\n", "\n", @@ -82,7 +81,15 @@ " if V.level < total_min_level:\n", " i_max = i\n", " break\n", - "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": {}, + "outputs": [], + "source": [ "\n", "fig1, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1)\n", "fig1.set_figheight(10)\n", @@ -98,8 +105,8 @@ "ax2.set_xlabel(r'$t$ ['+V.time_unit+']')\n", "ax2.legend()\n", "\n", - "ax3.plot(time_vec[:i_max],pressure_vec[:i_max], label='Pipeline pressure at reservoir')\n", - "ax3.set_ylabel(r'$p_{pipeline}$ ['+V.pressure_unit+']')\n", + "ax3.plot(time_vec[:i_max],pressure_conversion(pressure_vec[:i_max],'Pa',conversion_pressure_unit)[0], label='Pipeline pressure at reservoir')\n", + "ax3.set_ylabel(r'$p_{pipeline}$ ['+conversion_pressure_unit+']')\n", "ax3.set_xlabel(r'$t$ ['+V.time_unit+']')\n", "ax3.legend()\n", "\n", @@ -133,7 +140,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.13 ('DT_Slot_3')", + "display_name": "Python 3.8.13 ('Georg_DT_Slot3')", "language": "python", "name": "python3" }, @@ -152,7 +159,7 @@ "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48" + "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" } } }, From 5c25e81f375b6f4adb40115d22290b0fbaa9597b Mon Sep 17 00:00:00 2001 From: Brantegger Georg Date: Mon, 18 Jul 2022 08:15:11 +0200 Subject: [PATCH 04/12] updated to use outflux_vel instead of outflux --- Main_Programm.ipynb | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/Main_Programm.ipynb b/Main_Programm.ipynb index a537c58..1570767 100644 --- a/Main_Programm.ipynb +++ b/Main_Programm.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 8, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ @@ -16,7 +16,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 57, "metadata": {}, "outputs": [], "source": [ @@ -32,14 +32,14 @@ "A_pipe = D**2/4*np.pi # pipeline area\n", "h_pipe = 200 # hydraulic head without reservoir [m] \n", "alpha = np.arcsin(h_pipe/L) # Höhenwinkel der Druckrohrleitung \n", - "n = 10 # number of pipe segments in discretization\n", + "n = 50 # number of pipe segments in discretization\n", "# consider replacing Q0 with a vector be be more flexible in initial conditions\n", "Q0 = 2. # initial flow in whole pipe [m³/s]\n", "v0 = Q0/A_pipe # initial flow velocity [m/s]\n", - "f_D = 0.1 # Darcy friction factor\n", + "f_D = 0.01 # Darcy friction factor\n", "c = 400. # propagation velocity of the pressure wave [m/s]\n", "# consider prescribing a total simulation time and deducting the number of timesteps from that\n", - "nt = 100 # number of time steps after initial conditions\n", + "nt = 500 # number of time steps after initial conditions\n", "\n", "# derivatives of the pipeline constants\n", "dx = L/n # length of each pipe segment\n", @@ -60,7 +60,7 @@ "initial_outflux = Q0 # initial outflux of volume from the reservoir to the pipeline [m³/s]\n", "initial_pipeline_pressure = p0 # Initial condition for the static pipeline pressure at the reservoir (= hydrostatic pressure - dynamic pressure) \n", "initial_pressure_unit = 'Pa' # DO NOT CHANGE! for pressure conversion in print statements and plot labels \n", - "conversion_pressure_unit = 'mWS' # for pressure conversion in print statements and plot labels\n", + "conversion_pressure_unit = 'bar' # for pressure conversion in print statements and plot labels\n", "area_base = 20. # total base are of the cuboid reservoir [m²] \n", "area_outflux = A_pipe # outlfux area of the reservoir, given by pipeline area [m²]\n", "critical_level_low = 0. # for yet-to-be-implemented warnings[m]\n", @@ -91,7 +91,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 58, "metadata": {}, "outputs": [], "source": [ @@ -116,7 +116,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 59, "metadata": {}, "outputs": [], "source": [ @@ -144,8 +144,8 @@ "v_boundary_tur[0] = v_old[-1] \n", "v_boundary_tur[1:] = 0 # instantaneous closing\n", "# v_boundary_tur[0:20] = np.linspace(v_old[-1],0,20) # overwrite for finite closing time - linear case\n", - "const = int(np.min([100,round(nt/1.1)]))\n", - "v_boundary_tur[0:const] = v_old[1]*np.cos(t_vec[0:const]*2*np.pi/5)**2\n", + "# const = int(np.min([100,round(nt/1.1)]))\n", + "# v_boundary_tur[0:const] = v_old[1]*np.cos(t_vec[0:const]*2*np.pi/5)**2\n", "p_boundary_res[0] = p_old[0]\n", "p_boundary_tur[0] = p_old[-1]\n", "\n" @@ -153,7 +153,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 60, "metadata": {}, "outputs": [], "source": [ @@ -186,7 +186,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 61, "metadata": {}, "outputs": [], "source": [ @@ -195,8 +195,8 @@ "\n", "# for each pipeline timestep, execute nt_eRK4 timesteps of the reservoir code\n", " # set initial conditions for the reservoir time evolution calculted with e-RK4\n", - " V.pressure = p_old[0]\n", - " V.outflux = v_old[0]\n", + " V.pressure = p_old[0]\n", + " V.outflux_vel = v_old[0]\n", " # calculate the time evolution of the reservoir level within each pipeline timestep to avoid runaway numerical error\n", " for it_res in range(nt_eRK4):\n", " V.e_RK_4() # call e-RK4 to update outflux\n", @@ -209,7 +209,7 @@ " level_vec[it_pipe] = V.level \n", "\n", " # set boundary conditions for the next timestep of the characteristic method\n", - " p_boundary_res[it_pipe] = rho*g*V.level-v_old[1]**2*rho/2\n", + " p_boundary_res[it_pipe] = rho*g*V.level-V.outflux_vel**2*rho/2\n", " v_boundary_res[it_pipe] = v_old[1]+1/(rho*c)*(p_boundary_res[it_pipe]-p_old[1])-f_D*dt/(2*D)*abs(v_old[1])*v_old[1] \\\n", " +dt*g*np.sin(alpha)\n", "\n", @@ -245,7 +245,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 62, "metadata": {}, "outputs": [], "source": [ @@ -280,7 +280,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.13 ('DT_Slot_3')", + "display_name": "Python 3.8.13 ('Georg_DT_Slot3')", "language": "python", "name": "python3" }, @@ -299,7 +299,7 @@ "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48" + "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" } } }, From 08edd2f85ceedefd40452dd93d0f4a48d746e1fb Mon Sep 17 00:00:00 2001 From: Brantegger Georg Date: Mon, 18 Jul 2022 15:16:16 +0200 Subject: [PATCH 05/12] end of day commit - work on PI controller --- Regler/Regler_class_file.py | 95 ++++++++++ Regler/regler_test.ipynb | 337 +++++++++++++++++++++++++++++++----- 2 files changed, 387 insertions(+), 45 deletions(-) diff --git a/Regler/Regler_class_file.py b/Regler/Regler_class_file.py index 01597de..3d81a62 100644 --- a/Regler/Regler_class_file.py +++ b/Regler/Regler_class_file.py @@ -1,6 +1,45 @@ import numpy as np #based on https://en.wikipedia.org/wiki/PID_controller#Discrete_implementation +def trap_int(vec,timestep): + l = np.size(vec) + int = 0 + for i in range(l-1): + int = int + (vec[i]+vec[i+1])/2*timestep + return int + + +def ISE_fun(error_history,timestep): + # calcuate the integral of square error + e = np.array(error_history) + dt = timestep + ise = trap_int(e**2,dt) + return ise + +def IAE_fun(error_history,timestep): + # calcuate the integral of absolute error + e = np.array(error_history) + dt = timestep + iae = trap_int(np.abs(e),dt) + return iae + +def ITSE_fun(error_history,timestep): + # calcuate the integral of time multiply square error + e = np.array(error_history) + dt = timestep + n = np.size(e) + t = np.arange(0,n)*dt + itse = trap_int(t*e**2,dt) + return itse + +def ITAE_fun(error_history,timestep): + # calcuate the integral of time multiply absolute error + e = np.array(error_history) + dt = timestep + n = np.size(e) + t = np.arange(0,n)*dt + itae = trap_int(np.abs(e),dt) + return itae class P_controller_class: def __init__(self,setpoint,proportionality_constant): @@ -32,4 +71,60 @@ class P_controller_class: return new_control +class PI_controller_class: + def __init__(self,setpoint,proportionality_constant,Ti, timestep): + self.SP = setpoint + self.Kp = proportionality_constant + self.Ti = Ti + self.dt = timestep + self.error_history = [0,0] + self.control_variable = 0.0 + self.lower_limit = -1.3 # default + self.upper_limit = +1.3 # default + + def set_control_variable_limits(self,lower_limit,upper_limit): + self.lower_limit = lower_limit + self.upper_limit = upper_limit + + def calculate_error(self,process_variable): + self.error = self.SP-process_variable + self.error_history.append(self.error) + + def get_control_variable(self): + cv = self.control_variable + Kp = self.Kp + Ti = self.Ti + dt = self.dt + + e0 = self.error_history[-1] + e1 = self.error_history[-2] + new_control = cv+Kp*(e0-e1)+dt/Ti*e0 + if new_control < self.lower_limit: + new_control = self.lower_limit + + if new_control > self.upper_limit: + new_control = self.upper_limit + + self.control_variable = new_control + # print(new_control) + return new_control + + def get_performance_indicators(self,ISE=True,IAE=True,ITSE=True,ITAE=True): + ise = np.nan + iae = np.nan + itse = np.nan + itae = np.nan + + if ISE == True: + ise = ISE_fun(self.error_history[2:],self.dt) + if IAE == True: + iae = IAE_fun(self.error_history[2:],self.dt) + if ITSE == True: + itse = ITSE_fun(self.error_history[2:],self.dt) + if ITAE == True: + itae = ITAE_fun(self.error_history[2:],self.dt) + + return ise,iae,itse,itae + + diff --git a/Regler/regler_test.ipynb b/Regler/regler_test.ipynb index 704ad3b..6a42038 100644 --- a/Regler/regler_test.ipynb +++ b/Regler/regler_test.ipynb @@ -2,87 +2,229 @@ "cells": [ { "cell_type": "code", - "execution_count": 7, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from Regler_class_file import P_controller_class" + "from Regler_class_file import P_controller_class\n", + "from Regler_class_file import PI_controller_class\n" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "controller = P_controller_class(setpoint=1,proportionality_constant=0.1)" + "# # %matplotlib widget\n", + "# pi_controller = PI_controller_class(setpoint=100,proportionality_constant=1,Ti = 30,timestep = 0.1)\n", + "\n", + "# t_max = 100 #s\n", + "# dt = 0.1 #s\n", + "# nt = int(t_max//dt)\n", + "# t_vec = np.arange(0,nt+1,1)*dt\n", + "\n", + "# PV_0 = 50\n", + "\n", + "# PV_vec = np.full_like(t_vec,PV_0)\n", + "\n", + "# for i in range(1,nt+1):\n", + "# pi_controller.calculate_error(PV_vec[i-1])\n", + "\n", + "# if i == 500:\n", + "# pi_controller.SP = 0.\n", + "# PV_vec[i] = PV_vec[i-1]+pi_controller.get_control_variable()\n", + "\n", + "\n", + "\n", + "# plt.plot(t_vec,PV_vec,'.')" ] }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ + "SP_0 = 1.1\n", + "SP_1 = 0.24\n", + "PV_0 = 0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "10\n", + "20\n", + "30\n", + "40\n", + "50\n", + "60\n", + "70\n", + "80\n", + "90\n" + ] + } + ], + "source": [ + "n = 100\n", + "Kp_max = 5.\n", + "Ti_max = 5.\n", + "d_Kp = Kp_max/n\n", + "d_Ti = Ti_max/n\n", + "kp_vec = np.arange(1,n+1,1)*d_Kp\n", + "Ti_vec = np.arange(1,n+1,1)*d_Ti\n", + "\n", + "XX,YY = np.meshgrid(kp_vec,Ti_vec)\n", + "\n", + "ise_mat = np.empty_like(XX)\n", + "iae_mat = np.empty_like(XX)\n", + "itse_mat = np.empty_like(XX)\n", + "itae_mat = np.empty_like(XX)\n", + "\n", + "\n", "t_max = 100 #s\n", - "dt = 0.1 #s\n", + "dt = 0.05 #s\n", "nt = int(t_max//dt)\n", - "t_vec = np.arange(0,nt+1,1)*dt\n" + "t_vec = np.arange(0,nt+1,1)*dt\n", + "\n", + "for i in range(n):\n", + " if i%10 == 0:\n", + " print(i)\n", + " for j in range(n):\n", + " Kp = XX[i,j]\n", + " Ti = YY[i,j]\n", + "\n", + " c = PI_controller_class(SP_0,Kp,Ti,dt)\n", + "\n", + " PV_vec = np.full_like(t_vec,PV_0)\n", + "\n", + " for t in range(1,nt+1):\n", + " c.calculate_error(PV_vec[t-1])\n", + "\n", + " if t == 500:\n", + " c.SP = SP_1\n", + " PV_vec[t] = PV_vec[t-1]+c.get_control_variable()\n", + " \n", + " ise_mat[i,j],iae_mat[i,j],itse_mat[i,j],itae_mat[i,j] = np.log(c.get_performance_indicators())\n", + "\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "PV_0 = 0.5\n", - "\n", - "PV_vec = np.full_like(t_vec,PV_0)\n", - "controller.calculate_error(PV_vec[0])\n", - "controller.calculate_error(PV_vec[0])\n", - "\n", - "\n", - "for i in range(2,nt+1):\n", - " controller.calculate_error(PV_vec[i-1])\n", - "\n", - " if i == 100:\n", - " controller.SP = 0.\n", - " PV_vec[i] = PV_vec[i-1]+controller.get_control_variable()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "" ] }, - "execution_count": 11, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "befd47eba60b46139eda036a958f6ca7", + "model_id": "8c03c1f5a874409cbf1feff48c6ef0d7", "version_major": 2, "version_minor": 0 }, - "image/png": 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", + "image/png": 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IAIBlzk0BRzkkaWRkJHDkcrkLvt4HH3ygvr4+fe5zn9P3v/99Pfjgg/rzP/9zvfjii5X8slEApoCBCmFKF0CllKoGsL29PXD7E088oTVr1kx4vOd5WrJkiZ566ilJ0he/+EXt379ffX19+qM/+qOi+4HyIQACAIALGhoaUjabHW9nMpkLPu6KK67QvHnzArd94Qtf0KuvvlrW/qF4BEAAACyTdiKOAJ5dCDqbzQYC4GRuvvlmvffee4HbfvrTn2rOnDlF9wHlRQAEAMAylV4I+i/+4i9000036amnntIf/MEf6D/+4z+0YcMGbdiwoeg+oLy4CAQAyiyVSgcOwDbXX3+9tm7dqpdfflkLFizQ3/3d3+mZZ57RvffeG3fXMAlGAAEAsEylF4KWpC9/+cv68pe/XPRrorIIgAAAWCaOAIjaQgAEAMAy6VS0EJemQMx6BEDgIrm+H3cXAAAoCQIgAACWYQoYYQiAAABYhgCIMARAoETMrd48N/+UsR9yPwAA5UIABADAMpVeCBq1hwAIAIBl0o4zvp1bsefDblzoDQAAkDCMAAJFKrSEz/Pyn+B5Xt77K8333Nhe22G7NCCSlOMoFWEUL8q5qA0EQAAALJOWlI6Q4fgIZj+mgAEAABKGEUAAACyTSjmRruTlKmD7EQABWIcaQiQdVwEjDAEQAADLcBEIwlADCAAAkDCMAAIAYJmUE+0qYEoA7UcABFBxZo1enGsOAjbiIhCEYQoYAAAgYRgBBADAMlwEgjAEQCAmnpt/6zc/5H4AmEw6Yg1glHNRG5gCBgAASBhGAAEAsAxTwAhDAAQAwDLplKN0hCt5o5yL2kAABMqk0Bq+JNf8TVgWxmVZGAAoJwIgAACWYQoYYQiAAABYhquAEYYACACAZZyII4AOI4DWIwACJeK5ft77/ZD7AQCoFAIgAACW4SpghCEAAgBgmZSkKBmOXSLsRwAEAFStCUsEeSwRBJQCARAAAMukHUfpCBdyRDkXtYEACACAZVgHEGGY5gcAAJGsWbNGjuMEjpkzZ8bdLeTBCCAA65h1Y0DSpFNnjijnF2r+/Pl68803f/McaX4PqxkBEAAAy6ScaNO4xVxBXFdXx6hfDWEKGAAAXNDIyEjgyOVykz72wIEDamtr09y5c3X33Xfrgw8+qGBPUSgCIAAAlkmdvQq42OPc6GF7e7taWlrGj97e3gu+3g033KAXX3xR3//+97Vx40YdPnxYN910k371q19V8stGAZgCLjOHHbUxCd+r3a3hqLEDqluprgIeGhpSNpsdvz2TyVzw8Z2dneP/XrhwoTo6OnTVVVdp8+bN6unpKbofKJ/EjgD29fVp0aJFymazymaz6ujo0LZt2+LuFgAAkZ27CCTKIWn8/8hzx2QB0NTU1KSFCxfqwIEDZfwqEUViA+CsWbP09NNPa2BgQAMDA/rSl76klStXav/+/XF3DQCAmpbL5fTuu+/qiiuuiLsrmERip4BXrFgRaK9bt059fX3q7+/X/PnzY+oVksx32eIKQGlUeiHoRx99VCtWrNDs2bN19OhRrV27ViMjI+rq6iq6DyivxAbA87muq+9973saHR1VR0fHpI/L5XKBK6BGRkYq0T0AAAriOGeOKOcX4qOPPtI999yjjz/+WDNmzNCNN96o/v5+zZkzp/hOoKwSHQD37dunjo4OnTx5UlOnTtXWrVs1b968SR/f29urJ598soI9BACg+m3ZsiXuLqBAia0BlKRrrrlGe/fuVX9/v/70T/9UXV1d+slPfjLp41evXq3h4eHxY2hoqIK9BQDg4qTkRD5gt0SPANbX1+vqq6+WJC1ZskR79uzRs88+q+eee+6Cj89kMhd9BRTs5/r5l3Hx3eD9nuuVszsAMK7SU8CoPYkeATT5vp93lXMAAAAbJHYE8PHHH1dnZ6fa29t1/PhxbdmyRTt37tT27dvj7hoAAJGc2Qs42vmwW2ID4JEjR3Tffffp0KFDamlp0aJFi7R9+3bdeeedcXcNAIBImAJGmMQGwOeffz7uLgC4SE4qWrUKW9cBQFBiAyAAALaKeiUvVwHbjwAIAIBtIk4Bk//sRwAEAMAyXASCMARAoETMdf/CeCGP9z3WDSxWipo/AMiLAAgAgGUcRZvFZQDQfgRAAAAsk3IcpSIUAUY5F7WBAAhYylz6xPfcmHoCAKg2BEAAACzjKOJC0CXrCaoVARAAAMukzh5Rzofd+B4DAAAkDCOAQIWELRPDsi8ASsVxHDkR5oCjnIvaQAAEAMAyLASNMEwBAwAAJAwjgAAAWMaJuBcwM8D2IwAiIG380he4uxnOE7bVG+xhrrkIxI2rgBGGAAgAgGW4CARhCPkAAAAJwwggEBPfZdkXAOXBVcAIQwAEAMBCZDjkwxQwAABAwjACCACAZZgCRhgCIAAAluEqYIRhChgAACBhGAEEAMAyTAEjDAEQAADLOIp2FTD5z34EwBJz0syq2ypsZzfPz/8An63hKoat2QAgPwIgAACWSTmOUhEu5IhyLmoDw1UAAFjGcaIfxert7ZXjOHrkkUdK9vWg9BgBBIpU6hldtoa7eL7nxt0FoKo5vi8npCwl7Pxi7NmzRxs2bNCiRYuKfm1UBiOAAAAgshMnTujee+/Vxo0bdemll8bdHYQgAAIAYBvfi34UqLu7W8uXL9cdd9xRhi8IpcYUMAAAlnF8T04RIe788yVpZGQkcHsmk1Emk5nw+C1btmhwcFADAwNFvyYqixFAy6SMA/HxPS9wAECtaW9vV0tLy/jR29s74TFDQ0N6+OGH9d3vflcNDQ0x9BLFYAQQAADbFDmNGzhfZ8JdNpsdv/lCo3+Dg4M6evSoFi9ePH6b67ravXu31q9fr1wup3SatTmrDQEQAADb+P6ZI8r5krLZbCAAXsjtt9+uffv2BW574IEHdO211+qxxx4j/FUpAiAAAChac3OzFixYELitqalJ06dPn3A7qgcBEIgJW8MBKJsSTQHDXgRAAAAsc2Yh6ChXAUf7gLpz585I56P8uFAUAAAgYRgBBGA9J0UROhKGKWCEIAACAGAbAiBCEAABALANARAhqAEEAABIGEYAgQrxXD5RA6gQ35OibEHJCKD1CIAAAFjG8b2Iy8AQAG3HFDAAAEDCMAIIAIBtuAgEIQiAVSbtOMYtbBdWK9yIK+f71AgCKBXfP3NEOR9WYwoYAAAgYRgBBADANkwBIwQBEAAAyzi+H/EqYKaAbUcABMrEc/P/AQ27HwCAciEAAgBgG6aAEYIACACAbQiACEEABADANgRAhGAZGAAAgIRhBBAAAMuwFzDCEAABALCN5505opwPqxEAgQrxveQs+5JKpQNtz3Nj6gkA4EIIgAAA2Ia9gBGCAAgAgG24ChghuAoYAAAgYRgBBJB4jlGzCNQ6rgJGmMSOAPb29ur6669Xc3OzWltbddddd+m9996Lu1sAAER3bgo4ygGrJTYA7tq1S93d3erv79eOHTt0+vRpLVu2TKOjo3F3DQCAaHw/YgDkIhDbJXYKePv27YH2pk2b1NraqsHBQd16660x9Qq1xI34B9J3g5+wPdfeT9wsC4OkCpQXeJQaoHokNgCahoeHJUnTpk2b9DG5XE65XG68PTIyUvZ+AQBQMN+VonzQ8vmQZrvETgGfz/d99fT06JZbbtGCBQsmfVxvb69aWlrGj/b29gr2EgCAi+N7XuQDdiMASnrooYf0zjvv6OWXX877uNWrV2t4eHj8GBoaqlAPAQAASifxU8CrVq3SG2+8od27d2vWrFl5H5vJZJTJZCrUM6SduHsQje9SRF0tWOYFieNFnAKmTtd6iQ2Avu9r1apV2rp1q3bu3Km5c+fG3SUAAEqDAIgQiQ2A3d3deumll/T666+rublZhw8fliS1tLSosbEx5t4BAACUT2JrAPv6+jQ8PKylS5fqiiuuGD9eeeWVuLsGAEAkvutGPgrR19enRYsWKZvNKpvNqqOjQ9u2bSvTV4dSSOwIoM8ilygxSv4AVA3PO3NEOb8As2bN0tNPP62rr75akrR582atXLlSb7/9tubPn198P1A2iQ2AAACgNFasWBFor1u3Tn19ferv7ycAVikCIAAAtvG8iBeBnBkBNDc8uJjVMFzX1fe+9z2Njo6qo6Oj+D6grBJbAwhUmuf6gQOTc9LpwAGgML7nRj4kqb29PbABQm9v76SvuW/fPk2dOlWZTEYPPvigtm7dqnnz5lXqS0aBGAEEAMA2fsQaQP/MuUNDQ8pms+M35xv9u+aaa7R3714dO3ZMr776qrq6urRr1y5CYJUiAAIAgAs6d1Xvxaivrx+/CGTJkiXas2ePnn32WT333HPl7CKKRAAEAMAy50/jFnt+5D74vnK5XOTnQXkQAAFUPbZyAwpU4Z1AHn/8cXV2dqq9vV3Hjx/Xli1btHPnTm3fvr34PqCsCIAAACCSI0eO6L777tOhQ4fU0tKiRYsWafv27brzzjvj7homQQAEAMA2FV4I+vnnny/+tRALAiAAAJYpZjs383zYjQBYY9KOY9zCenK1yvPs+d6lKlyjF1agXooCdqAY5rqVBClUKwIgAAC2KdFOILAXARAAANtU+Cpg1B62ggMAAEgYRgCBMvHdwqZQfKZcyob9hJPDXDMyqfWgvudF+pvC3yP7EQABALANU8AIQQAEAMA2fsQA6BMAbUcARGJMXEKnsgqdEq4m5d6KjWm72sX3DqhNBEAAACxDDSDCEAABALAN6wAiBMvAAAAAJAwjgMAk3AJ3anN9e7Z2A1DjuAoYIQiAAABYxnfdSPsQs4ex/ZgCBgAASBhGAAEAsI3nRbuQg4tArEcABGLihxQZOikG6IvlUb+EpKMGECH4HwYAACBhGAEEAMAyvudG2pWFHV3sRwBE0dLGzmqFLptS65L29ZZSytg+jClb1Kpq/VlmJxCEIQACAGAZ3/Mj7T/ue3zCtR01gAAAAAnDCCAAAJbxXS/aCGCEc1EbCIDARZq41Ztzwced41EkWDUco04LsB01gAjDFDAAAEDCMAIIAIBlmAJGGAIgAACWIQAiDAEQmIS5zmFUYVu/mZw0FRqTCVuklkVsASA/AiAAAJbxXVeeG2EnkAjnojYQAAEAsIzvR7wK2GcK2HYEwApzUkzr4cKouSleqad8mUIGYDsCIAAAluEiEIQhAAIAYBkCIMIwHwkAgGV8zx/fDaS4o7BVC3p7e3X99derublZra2tuuuuu/Tee++V6atDKRAAgZh4rhc4akkqlQ4c5eak0oEjjO+5gaPUzw8gaNeuXeru7lZ/f7927Nih06dPa9myZRodHY27a5gEU8AAAFgm6gfLQs/dvn17oL1p0ya1trZqcHBQt956a9H9QPkQAAEAsEzcNYDDw8OSpGnTpkV6HpQPARAAAFzQyMhIoJ3JZJTJZPKe4/u+enp6dMstt2jBggXl7B4ioAYQKBHXDx5ROelU4EiSqDV5vusGDmr8UCq18rN0bgQwyiFJ7e3tamlpGT96e3tDX/uhhx7SO++8o5dffrncXyYiYAQQAADLlGonkKGhIWWz2fHbw0b/Vq1apTfeeEO7d+/WrFmzin59lB8BEAAAXFA2mw0EwMn4vq9Vq1Zp69at2rlzp+bOnVuB3iEKAiASK+04ZX1+35gH9gqcF2bbQADFqvRFIN3d3XrppZf0+uuvq7m5WYcPH5YktbS0qLGxseh+oHwIgAAAWKbSAbCvr0+StHTp0sDtmzZt0v333190P1A+BEAAABCJ75fgyjdUFAEQAADLeJ4nL8JFIFHORW0gAAKoOWFXN17M9m+AzeJeCBrVjwAIAIBlzgTA4j8IEQDtx2WGAAAACcMIIAAAlvG9iAtBUwNoPQIgUKVKvc1UNW9bVWre6bFAO5Wgrx2QzgbAKDWABEDrMQUMAACQMIwAAgBgm4hXAYuLQKxHAAQAwDKe68mLEOKinIvaQACMWarM+9HapNC9e9O1/tYadWvlXtvOM56/kjWDZo2eG/FrpX4JpWL+HrDGJGyR6BrA3bt3a8WKFWpra5PjOHrttdfi7hIAAJGduwo4ygG7JToAjo6O6rrrrtP69evj7goAACVzbieQKAfslugp4M7OTnV2dsbdDZxV81O2BfLdkM3TU7X7+Sxs+rjQabRCp+GYpgOA/BIdAAuVy+WUy+XG2yMjIzH2BgCAC/NdP/xDZsj5sFvtDjHEoLe3Vy0tLeNHe3t73F0CAGACz/PGrwQu6qAG0HoEwAKsXr1aw8PD48fQ0FDcXQIAYALf8yMfsBtTwAXIZDLKZDJxdwOWctLBz2POlPqYejJRtW+lZi5hYwqrSXRquN4SAIpBAAQAwDKeK3mp4kfxuI7KfokOgCdOnND7778/3j548KD27t2radOmafbs2TH2DACA4vmuJz9VfB0fy8DYL9EBcGBgQLfddtt4u6enR5LU1dWlF154IaZeAQAAlFeiA+DSpUvl+xS62qLQreLKzTN+tgpeWX9C3Vr1/qwWum1c2Lp+4TV70e4HbOe7vvwIU8AsA2O/RAdAAABs5Ll+xBpAAqDtuPQNAAAgYRgBRM2Ke+u4cn9AduobjFs+Le8LWiTuZV2Ygq5d5s9OwaUbVYKLQBCGAAgAgGU835cXYTFns4YZ9mEKGAAAIGEYAQQAwDauL9+JMIrHRSDWIwCiZMyavKT9/XBLPGXi1E0xbkluDaCTjrbMTNyqrT82MX82fJctLCTJcz15TvF1fB41gNYjAAIAYBk/4ggg6wDajxpAAACAhGEEEAAAyzACiDAEwIQxt0srdd1aIeJex6/amOuPOY1NxiNGKteZEHHXtE1cqy1Y95Wqqy/w+fJvTYfSSRnvtcd7XRbUACIMU8AAAAAJwwggAACW8X1ffoSFoH0WgrYeI4AoSNpxxo9ak3aCR7VLXdIcOCr++qn0+FFuTiodOFLGUfDzpdOBo9qYXy9+g/emNDzXj3wUYvfu3VqxYoXa2trkOI5ee+218nxhKBkCIAAAiGR0dFTXXXed1q9fH3dXcJGYAgYAwDK+68tX8RdyFHoVcGdnpzo7O4t+PVQeARAAAMucCYDRl4EZGQmuPpDJZJTJZCL1DdWBAIiyqYU6uzg5xhuUSgcrMlJTP1PW16+lpU7MWrCwZVvMZWDc02OBdrrAZWJgL5YAyq+9vT3QfuKJJ7RmzZp4OoOSIgACAGAZz/XlRRgBPHcRyNDQkLLZ7PjtjP7ZgwAIAIBlfM+TH2G1Bt87Uz+YzWYDARD2IAACAGCZUo0Awl4EQFStWlxrMArHqAF0p86I9HxmLVOUNdWqbT22CXVbrlEDWB+8/9SnJwJtswYw6XVg1fb9Re05ceKE3n///fH2wYMHtXfvXk2bNk2zZ8+OsWeYDAEQAADL+F7Eq4AL3EVkYGBAt91223i7p6dHktTV1aUXXnih6H6gfAiAAADYxvXk+xFmUbzC1hBcunQp28fVGHYCAQAASBhGAEvASaXkpMjS1abWaghTU4K/jr9uaI2pJ/ELW/fPFFbveOqT4GK2UxqnFtQfz3j+tPH85n7FyaogrC1Jqff0XF9ehBE5r8ApYNQeAiAAAJbxXT/SlGyhNYCoPQxbAQAAJAwjgDVu4jRnYZ/azO3azKWfwu6vpLCt5Sq99Vyhr2eWCUzY+q1+SqC97n9/UFS/fvN6hS3tYU5jVvPSIGbf3LFP8z6+aUZwO6tSLpEDVCPPjzgFzAUd1iMAAgBgGdf35UYIcVHORW1gChgAACBhGAEEAMAyrh+tZIed4OxHALScWSMYNqxfyOMLfe4whS7bUu7Hl/p8k2MUEdY1BLcn+96Wt0r6eiZzaROzBrCamH3z0kb79KmCns/82p10ab928/moMSydpCzjEhVTwAhDAAQAwDKMACIMNYAAAAAJwwggAACW8SJOAbMMjP0IgFWu0LXmzDq1av4lrrat2kpdI5gy28Y3M5UKtqc0NUR6/ULV8rp/ZjtVF1xD0XRy+JeBdjrTmPf5fM8rtIsFqeb3Oqpqq9Erd38m/Cwa9wd+kvzKTbq5ijgFXLKeoFoxBQwAAJAwjAACAGAZ1/flFrgzlHk+7EYALLGw7b7CVNt2Z/lef+K2caXtXNTp76jvVdmXfWkM/vo1TG8JtEs9VRX3VFwppeuCS+a4Zvv0WKCdaZ4WaI+NDgfadY1TS9i76lfNS/5UG/Nvuqlaf69cP9o0LlcB248pYAAAgIRhBBAAAMswAogwBEAAACxDDSDCEAArzFwKxKwL0+nyvr75ctX0KS+8/rG0NX5R6x/M1zfb5vfWMepB6xqCv36Nn18QfILtH0fqX5haXorE7Lu5rMvpT08E2qkpwRrB0ydHA+36ptLWX05ctiaV9/5qrSOrhAnb/Jnb9BX4Xpnb8PluYe8t3xskBQEQAADLeBGngL0qGhxAeRAAAQCwDFPACEMABADAMlwEgjAEwBoXVgdn1rmZfxAmrnVn/tYH7z//U2G51yQMq/krdU1geH/C7jf6Ux+sJZpi1vxd1hxoP3zkC8Yz/mtB/TOZtUuVrPkrtG4qat/MdQHNGsApDVPztk1m/0PrzkL6H7aVnc11ZpXeii3q98rm7wVwPgIgAACWOTMCGGUKuISdQVUiAAIAYBmmgBGGAFgCTjo1YYmP39xX2LzkxGnNsGlOP+/9Ydu1hRX6Tny9yb+eQouGw7Zaq/SUb9iyLubr16fyt81lXi65LLhUyeX/37xA+7VXok35mko55Rs2LWYu3VGosCV5Cp1iNbd+Ozn8y0C7oWVG3udL1zcE2lGntCe0jaVKoi7/VMtL+piqfVmYQp3/s+1b9H1C7SMAAgBgGa4CRhgCIAAAlvEleRHPh92iboYAAACAGsMIYAk4KWe8BjA1SS3g+GONwjJzazhToXVpYY+Xn79mcGKNoNmjyT8XhtX0hSl1jZ/5fOZ3JqzecmKNX/D8RuN7nWkOLkXS3BZcaqTtpmsC7c+9FqxTi5tZS3V+7VWhy6KE1VmFLVFj1m1N2PrNWPYlrObOrPkbGx0OtKc0Br9XjZfODLTNGsKwZWTMOjZzKzqz/27u07zPZ5NCa/gKrQk0TfhZM+svQ5R0OaUK1gAyBYwwBEAAACzDVcAIwxQwAABAwhAAAQCwjOv7kY9ifPvb39bcuXPV0NCgxYsX61//tbTLW6F0mAIuASeVkpM6WwNYH3xL01OM2iVje7CU0a7PBQftxzyzTi34S2nWqYWZsC6gn39dwELWAYwqas1JoTV/jen8NX9T64LPMM34XjXPbAq0r1h8RaDds+T/D7Tf/sGPJna6hKLWKk2os4txHcGwmkOzhs87PRZop+uDay6aNX71TS15+2Myawhzx/9voG2uO2i+l3VGf/xL8n99pwusCYxz27+oCq3hM2sCTeaVr6VeN7DQreZ87zd/R/xI1+UWJo4p4FdeeUWPPPKIvv3tb+vmm2/Wc889p87OTv3kJz/R7NmzI/QG5cAIIAAAloljBPBb3/qWvv71r+uP//iP9YUvfEHPPPOM2tvb1dfXV4avEFERAAEAQCRjY2MaHBzUsmXLArcvW7ZM//Zv/xZTr5APU8AAAFjGizgF7J0dABwZGQncnslklMlkJjz+448/luu6uvzyywO3X3755Tp8+HCEnqBcCIAlkGrKKnXJmb1D65uDdWGZ7PFA+/TJ4KafnlFoYa4LOOXT4OOnTqgR9PO2P3U9434Z7fznm+3zpwXMapZSrxtl1uxNCVmnz6zpa5kSHOC+bGpw7bXWha2B9levXhVoH/vow4vvrCQNG+0y1/yZqrnuK6xvBdcbBrfqLXyvYqNtrstnCquxG/3lUKDdNKM90HaNGkVzXcDG6W15+3Pq5IlA+5OPfxHsTzp/zWHKeH0/Y9xvvN75dXaF/lyVuh6x0PML/VkqdJ/nsHrVfM/vnarcf7mlWgewvT34s/zEE09ozZo1k57nGH+nfd+fcBuqAwEQAABc0NDQkLLZ7Hj7QqN/knTZZZcpnU5PGO07evTohFFBVAcCYAT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\n", + " Figure\n", + "
\n", + " \n", "
\n", " " ], @@ -96,31 +238,136 @@ ], "source": [ "%matplotlib widget\n", - "\n", - "plt.plot(t_vec,PV_vec,'+')" + "fig1,axs1 = plt.subplots(1,1)\n", + "ise_min = np.min(ise_mat)\n", + "ise_max = np.max(ise_mat)\n", + "mesh1 = axs1.pcolormesh(XX,YY,ise_mat,cmap='RdBu', vmin=ise_min, vmax=ise_max,shading='nearest')\n", + "fig1.colorbar(mesh1)\n", + "fig2,axs2 = plt.subplots(1,1)\n", + "iae_min = np.min(iae_mat)\n", + "iae_max = np.max(iae_mat)\n", + "mesh2 = axs2.pcolormesh(XX,YY,iae_mat,cmap='RdBu', vmin=iae_min, vmax=iae_max,shading='nearest')\n", + "fig2.colorbar(mesh2)\n", + "fig3,axs3 = plt.subplots(1,1)\n", + "itse_min = np.min(itse_mat)\n", + "itse_max = np.max(itse_mat)\n", + "mesh3 = axs3.pcolormesh(XX,YY,itse_mat,cmap='RdBu', vmin=itse_min, vmax=itse_max,shading='nearest')\n", + "fig3.colorbar(mesh3)\n", + "fig4,axs4 = plt.subplots(1,1)\n", + "itae_min = np.min(itae_mat)\n", + "itae_max = np.max(itae_mat)\n", + "mesh4 = axs4.pcolormesh(XX,YY,itae_mat,cmap='RdBu', vmin=itae_min, vmax=itae_max,shading='nearest')\n", + "fig4.colorbar(mesh4)\n" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "ise_ind = np.unravel_index(np.argmin(ise_mat,axis=None),ise_mat.shape)\n", + "Kp_ise = XX[ise_ind]\n", + "Ti_ise = YY[ise_ind]\n", + "iae_ind = np.unravel_index(np.argmin(iae_mat,axis=None),iae_mat.shape)\n", + "Kp_iae = XX[iae_ind]\n", + "Ti_iae = YY[iae_ind]\n", + "itse_ind = np.unravel_index(np.argmin(itse_mat,axis=None),itse_mat.shape)\n", + "Kp_itse = XX[itse_ind]\n", + "Ti_itse = YY[itse_ind]\n", + "itae_ind = np.unravel_index(np.argmin(itae_mat,axis=None),itae_mat.shape)\n", + "Kp_itae = XX[itae_ind]\n", + "Ti_itae = YY[itae_ind]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "-0.1\n" - ] + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d320b9d6dc844aa183152834ba47ef78", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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+ "text/html": [ + "\n", + "
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" c_itse.calculate_error(PV_vec_itse[i-1])\n", + " c_itae.calculate_error(PV_vec_itae[i-1])\n", + "\n", + " if i == 500:\n", + " c_ise.SP = SP_1\n", + " c_iae.SP = SP_1\n", + " c_itse.SP = SP_1\n", + " c_itae.SP = SP_1\n", + "\n", + " PV_vec_ise[i] = PV_vec_ise[i-1]+c_ise.get_control_variable()\n", + " PV_vec_iae[i] = PV_vec_iae[i-1]+c_iae.get_control_variable()\n", + " PV_vec_itse[i] = PV_vec_itse[i-1]+c_itse.get_control_variable()\n", + " PV_vec_itae[i] = PV_vec_itae[i-1]+c_itae.get_control_variable()\n", + "\n", + "fig5 = plt.figure()\n", + "plt.plot(t_vec,PV_vec_ise,label='ise')\n", + "# plt.plot(t_vec,PV_vec_iae,'.-',label='iae')\n", + "plt.plot(t_vec,PV_vec_itse,'+',label='itse')\n", + "# plt.plot(t_vec,PV_vec_itae,'*',label='itae')" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.13 ('DT_Slot_3')", + "display_name": "Python 3.8.13 ('Georg_DT_Slot3')", "language": "python", "name": "python3" }, @@ -139,7 +386,7 @@ "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48" + "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" } } }, From 5654a41d4855d5a7ceee8e0b6298d5777b6d17b1 Mon Sep 17 00:00:00 2001 From: GeorgBrantegger <62994745+GeorgBrantegger@users.noreply.github.com> Date: Tue, 19 Jul 2022 13:54:25 +0200 Subject: [PATCH 06/12] Add files via upload --- ... - Francisturbinen M1 und M2_Untertweng.csv | 22 +++++++++++++++++++ 1 file changed, 22 insertions(+) create mode 100644 Turbinendurchfluss über dem Zylinderhub - Francisturbinen M1 und M2_Untertweng.csv diff --git a/Turbinendurchfluss über dem Zylinderhub - Francisturbinen M1 und M2_Untertweng.csv b/Turbinendurchfluss über dem Zylinderhub - Francisturbinen M1 und M2_Untertweng.csv new file mode 100644 index 0000000..ad80ef2 --- /dev/null +++ b/Turbinendurchfluss über dem Zylinderhub - Francisturbinen M1 und M2_Untertweng.csv @@ -0,0 +1,22 @@ +,11.4,11.2,11,10.8,10.6,10.4,10.2,10,9.8 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+0.5,478.129951,472.075209,466.032607,459.983487,453.910176,447.796055,441.625591,435.384378,429.059145 +0.55,523.873268,517.285198,510.689413,504.069281,497.409128,490.694283,483.911113,477.047044,470.090565 +0.6,568.778912,561.677293,554.548395,547.377555,540.151033,532.856054,525.480827,518.014558,510.447451 +0.65,612.763186,605.169605,597.529525,589.830179,582.059697,574.207132,566.262474,558.216649,550.061519 +0.7,655.7481,647.685753,639.558081,631.354134,623.063835,614.677994,606.188309,597.587364,588.868614 +0.75,697.661758,689.155243,680.565018,671.881864,663.097416,654.204159,645.195426,636.065384,626.809013 +0.8,738.438667,729.51377,720.487263,711.35157,702.099947,692.726469,683.226022,673.594278,663.827671 +0.85,778.019972,768.703447,759.267942,749.707427,740.016685,730.191293,720.227602,710.122707,699.874419 +0.9,816.35361,806.672962,796.856534,786.899741,776.798797,766.550685,756.153132,745.604572,734.904109 +0.95,853.394385,843.377654,833.208949,822.885029,812.403437,801.762466,790.961126,779.999101,768.876705 +1,889.103974,878.779525,868.287549,857.626044,846.793778,835.790258,824.615682,813.270891,801.757325 From 7e67979a827f0b2d5b581c007731e0b79dce120b Mon Sep 17 00:00:00 2001 From: Brantegger Georg Date: Tue, 19 Jul 2022 15:51:57 +0200 Subject: [PATCH 07/12] end of day commit - working on turbine class --- Messing Around/messy_nb.ipynb | 270 +++++++++++++++ Pegelregler_test.ipynb | 274 ++++++++++++++++ Regler/Regler_class_file.py | 81 ++--- Regler/regler_test.ipynb | 55 ++-- .../Durchflusskennlinie.csv | 44 +-- Turbinen/Turbinen_class_file.py | 39 ++- Turbinen/messy.ipynb | 288 +++++++++------- Untertweng.ipynb | 308 ++++++++++++++++++ untertweng.txt | 20 ++ 9 files changed, 1149 insertions(+), 230 deletions(-) create mode 100644 Pegelregler_test.ipynb rename Turbinendurchfluss über dem Zylinderhub - Francisturbinen M1 und M2_Untertweng.csv => Turbinen/Durchflusskennlinie.csv (98%) create mode 100644 Untertweng.ipynb create mode 100644 untertweng.txt diff --git a/Messing Around/messy_nb.ipynb b/Messing Around/messy_nb.ipynb index e69de29..8414209 100644 --- a/Messing Around/messy_nb.ipynb +++ b/Messing Around/messy_nb.ipynb @@ -0,0 +1,270 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scipy\n", + "from scipy.interpolate import interp2d\n", + "from mpl_toolkits import mplot3d\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "x = np.linspace(-1,1,10)\n", + "y = np.linspace(-1,1,10)\n", + "\n", + "XX,YY = np.meshgrid(x,y)\n", + "\n", + "Z = np.cos((XX ** 2 + YY ** 2)*5)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7d8f779df98b4f9cb839fd0a1be814fa", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", 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\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n = 5\n", + "\n", + "fig2 = plt.figure()\n", + "ax2 = plt.axes(projection='3d')\n", + "\n", + "ax2.scatter(XX[n,:], YY[n,:], Z[n,:],cmap='viridis', edgecolor='none')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "interpol_fun = interp2d(x,y,Z,kind='quintic')\n", + "\n", + "x2 = np.linspace(-1,1,100)\n", + "y2 = np.linspace(-1,1,100)\n", + "\n", + "XX2,YY2 = np.meshgrid(x2,y2)\n", + "\n", + "\n", + "Z2 = np.reshape(interpol_fun(x2,y2),[100,100])" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "13a8b08bf612438f8b68aae47fc15736", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", 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a/Pegelregler_test.ipynb b/Pegelregler_test.ipynb new file mode 100644 index 0000000..d69ccf4 --- /dev/null +++ b/Pegelregler_test.ipynb @@ -0,0 +1,274 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "from functions.pressure_conversion import pressure_conversion\n", + "from Ausgleichsbecken.Ausgleichsbecken_class_file import Ausgleichsbecken_class\n", + "from Regler.Regler_class_file import PI_controller_class" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# define reservoir constants\n", + "initial_level = 5. # m\n", + "initial_influx = 1. # m³/s\n", + "initial_outflux = 0. # m³/s\n", + "initial_pipeline_pressure = 5.\n", + "initial_pressure_unit = 'mWS'\n", + "conversion_pressure_unit = 'mWS'\n", + "\n", + "area_base = 1. # m²\n", + "area_outflux = 0.5 # m²\n", + "critical_level_low = 0. # m\n", + "critical_level_high = 10. # m\n", + "simulation_timestep = 0.001 # s\n", + "\n", + "# for while loop\n", + "total_min_level = 0.01 # m\n", + "total_max_time = 200 # s" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "# define controller constants\n", + "target_level = 4.5 # m\n", + "Kp = 0.15\n", + "Ti = 20\n", + "\n", + "deadband_range = 0.1 # m\n", + "deadband_lo = target_level-deadband_range\n", + "deadband_hi = target_level+deadband_range\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "# define pressure modulation function\n", + "d_ppfun_max = +0.001\n", + "d_pp_fun_max_unit = 'mWS'\n", + "d_ppfun_min = -0.001\n", + "d_pp_fun_min_unit = 'mWS'\n", + "\n", + "d_pp_max,_ = pressure_conversion(d_ppfun_max,d_pp_fun_max_unit,'Pa')\n", + "d_pp_min,_ = pressure_conversion(d_ppfun_min,d_pp_fun_min_unit,'Pa')\n", + "\n", + "pp_fun_max = +1.1*target_level\n", + "pp_fun_max_unit = 'mWS'\n", + "pp_fun_min = +0.5*target_level\n", + "pp_fun_min_unit = 'mWS'\n", + "\n", + "pp_max,_ = pressure_conversion(pp_fun_max,pp_fun_max_unit,'Pa')\n", + "pp_min,_ = pressure_conversion(pp_fun_min,pp_fun_min_unit,'Pa')\n", + "\n", + "\n", + "def pipe_pressure_fun(p,control_variable,d_pp_max=d_pp_max,d_pp_min=d_pp_min):\n", + " cv = control_variable\n", + " if cv >= 0:\n", + " return_val = p+cv*d_pp_max\n", + " else:\n", + " return_val = p-cv*d_pp_min\n", + "\n", + " if return_val > pp_max:\n", + " return_val = pp_max\n", + " elif return_val < pp_min:\n", + " return_val = pp_min\n", + " \n", + " return return_val\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "c = PI_controller_class(target_level,Kp,Ti,simulation_timestep)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "V = Ausgleichsbecken_class(area_base, area_outflux, critical_level_low, critical_level_high,simulation_timestep)\n", + "V.set_initial_level(initial_level) \n", + "V.set_influx(initial_influx)\n", + "V.set_outflux(initial_outflux)\n", + "\n", + "converted_pressure,_ = pressure_conversion(initial_pipeline_pressure,input_unit = initial_pressure_unit, target_unit = 'Pa')\n", + "V.pressure = converted_pressure\n", + "\n", + "time_vec = np.arange(0,total_max_time,simulation_timestep)\n", + "outflux_vec = np.empty_like(time_vec)\n", + "outflux_vec[0] = initial_outflux\n", + "level_vec = np.empty_like(time_vec)\n", + "level_vec[0] = initial_level" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "i_max = -1\n", + "\n", + "pressure_vec = np.full_like(time_vec,V.pressure)\n", + "\n", + "for i in range(np.size(time_vec)-1):\n", + " # update to include p_halfstep\n", + "\n", + " if time_vec[i] >= 25 and time_vec[i] < 35:\n", + " V.influx = 1+(4-1)/10*(time_vec[i]-25)\n", + "\n", + " if time_vec[i] >= 70 and time_vec[i] < 100:\n", + " V.influx = 4-(4-0.2)/30*(time_vec[i]-70)\n", + "\n", + "\n", + " c.calculate_error(V.level)\n", + "\n", + " if abs(c.error) > deadband_range:\n", + " cv = c.get_control_variable()\n", + " V.pressure = pipe_pressure_fun(V.pressure,cv)\n", + "\n", + " pressure_vec[i+1] = V.pressure\n", + " V.e_RK_4()\n", + " V.level = V.update_level(V.timestep)\n", + " V.set_volume()\n", + " outflux_vec[i+1] = V.outflux\n", + " level_vec[i+1] = V.level\n", + " if V.level < total_min_level:\n", + " i_max = i\n", + " break\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "b67e1ad42a2e4b9f932b947588eac3e9", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "%matplotlib widget\n", + "\n", + "fig1, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1)\n", + "fig1.set_figheight(10)\n", + "fig1.suptitle('Ausgleichsbecken')\n", + "\n", + "ax1.plot(time_vec[:i_max],level_vec[:i_max], label='Water level')\n", + "ax1.set_ylabel(r'$h$ ['+V.level_unit+']')\n", + "ax1.set_xlabel(r'$t$ ['+V.time_unit+']')\n", + "ax1.legend()\n", + "\n", + "ax2.plot(time_vec[:i_max],outflux_vec[:i_max], label='Outflux')\n", + "ax2.set_ylabel(r'$Q_{out}$ ['+V.flux_unit+']')\n", + "ax2.set_xlabel(r'$t$ ['+V.time_unit+']')\n", + "ax2.legend()\n", + "\n", + "ax3.plot(time_vec[:i_max],pressure_conversion(pressure_vec[:i_max],'Pa',conversion_pressure_unit)[0], label='Pipeline pressure at reservoir')\n", + "ax3.set_ylabel(r'$p_{pipeline}$ ['+conversion_pressure_unit+']')\n", + "ax3.set_xlabel(r'$t$ ['+V.time_unit+']')\n", + "ax3.legend()\n", + "\n", + "# plt.subplots_adjust(left=0.2, bottom=0.2)\n", + "ax4.set_axis_off()\n", + "cell_text = np.array([[initial_level, V.level_unit], \\\n", + " [initial_influx, V.flux_unit], \\\n", + " [initial_outflux, V.flux_unit], \\\n", + " [simulation_timestep, V.time_unit], \\\n", + " [area_base, V.area_unit], \\\n", + " [area_outflux, V.area_unit]])\n", + "\n", + "row_labels =['initial_level', \\\n", + " 'initial_influx', \\\n", + " 'initial_outflux', \\\n", + " 'simulation_timestep', \\\n", + " 'area_base', \\\n", + " 'area_outflux']\n", + "\n", + "plt.table(cellText=cell_text, \\\n", + " cellLoc='center', \\\n", + " colWidths=[0.3,0.1,0.3], \\\n", + " rowLabels=row_labels, \\\n", + " loc = 1, \\\n", + " rowLoc='left', \\\n", + " fontsize = 15.)\n", + "\n", + "fig1.tight_layout() " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.13 ('Georg_DT_Slot3')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Regler/Regler_class_file.py b/Regler/Regler_class_file.py index 3d81a62..7d5cca0 100644 --- a/Regler/Regler_class_file.py +++ b/Regler/Regler_class_file.py @@ -42,33 +42,35 @@ def ITAE_fun(error_history,timestep): return itae class P_controller_class: - def __init__(self,setpoint,proportionality_constant): - self.SP = setpoint - self.Kp = proportionality_constant - self.error_history = [] - self.control_variable = 0.1 - self.lower_limit = -0.1 # default - self.upper_limit = +0.1 # default + # def __init__(self,setpoint,proportionality_constant): + # self.SP = setpoint + # self.Kp = proportionality_constant + # self.error_history = [] + # self.control_variable = 0.1 + # self.lower_limit = -0.1 # default + # self.upper_limit = +0.1 # default - def set_control_variable_limits(self,lower_limit,upper_limit): - self.lower_limit = lower_limit - self.upper_limit = upper_limit + # def set_control_variable_limits(self,lower_limit,upper_limit): + # self.lower_limit = lower_limit + # self.upper_limit = upper_limit - def calculate_error(self,process_variable): - self.error = self.SP-process_variable - self.error_history.append(self.error) + # def calculate_error(self,process_variable): + # self.error = self.SP-process_variable + # self.error_history.append(self.error) - def get_control_variable(self): - new_control = self.control_variable+self.Kp*(self.error_history[-1]-self.error_history[-2]) - if new_control < self.lower_limit: - new_control = self.lower_limit + # def get_control_variable(self): + # new_control = self.control_variable+self.Kp*(self.error_history[-1]-self.error_history[-2]) + # if new_control < self.lower_limit: + # new_control = self.lower_limit - if new_control > self.upper_limit: - new_control = self.upper_limit + # if new_control > self.upper_limit: + # new_control = self.upper_limit - self.control_variable = new_control - # print(new_control) - return new_control + # self.control_variable = new_control + # # print(new_control) + # return new_control + def __init__(self): + pass class PI_controller_class: @@ -77,21 +79,25 @@ class PI_controller_class: self.Kp = proportionality_constant self.Ti = Ti self.dt = timestep - self.error_history = [0,0] + self.error_history = [0] self.control_variable = 0.0 - self.lower_limit = -1.3 # default - self.upper_limit = +1.3 # default + self.cv_lower_limit = -1 # default + self.cv_upper_limit = +1 # default + def set_control_variable_limits(self,lower_limit,upper_limit): - self.lower_limit = lower_limit - self.upper_limit = upper_limit + self.cv_lower_limit = lower_limit + self.cv_upper_limit = upper_limit def calculate_error(self,process_variable): self.error = self.SP-process_variable self.error_history.append(self.error) def get_control_variable(self): + # if np.isclose(self.error,0,atol = 0.1): + # self.control_variable = 0 + cv = self.control_variable Kp = self.Kp Ti = self.Ti @@ -100,15 +106,14 @@ class PI_controller_class: e0 = self.error_history[-1] e1 = self.error_history[-2] new_control = cv+Kp*(e0-e1)+dt/Ti*e0 - if new_control < self.lower_limit: - new_control = self.lower_limit + if new_control < self.cv_lower_limit: + new_control = self.cv_lower_limit - if new_control > self.upper_limit: - new_control = self.upper_limit + if new_control > self.cv_upper_limit: + new_control = self.cv_upper_limit self.control_variable = new_control - # print(new_control) - return new_control + return self.control_variable def get_performance_indicators(self,ISE=True,IAE=True,ITSE=True,ITAE=True): ise = np.nan @@ -116,14 +121,16 @@ class PI_controller_class: itse = np.nan itae = np.nan + # self.error_history[1:] because the first value of the error history is set to [0] + # to avoid special case handling in the calculation of the controll variable if ISE == True: - ise = ISE_fun(self.error_history[2:],self.dt) + ise = ISE_fun(self.error_history[1:],self.dt) if IAE == True: - iae = IAE_fun(self.error_history[2:],self.dt) + iae = IAE_fun(self.error_history[1:],self.dt) if ITSE == True: - itse = ITSE_fun(self.error_history[2:],self.dt) + itse = ITSE_fun(self.error_history[1:],self.dt) if ITAE == True: - itae = ITAE_fun(self.error_history[2:],self.dt) + itae = ITAE_fun(self.error_history[1:],self.dt) return ise,iae,itse,itae diff --git a/Regler/regler_test.ipynb b/Regler/regler_test.ipynb index 6a42038..7bf1ed9 100644 --- a/Regler/regler_test.ipynb +++ b/Regler/regler_test.ipynb @@ -62,21 +62,12 @@ "name": "stdout", "output_type": "stream", "text": [ - "0\n", - "10\n", - "20\n", - "30\n", - "40\n", - "50\n", - "60\n", - "70\n", - "80\n", - "90\n" + "0\n" ] } ], "source": [ - "n = 100\n", + "n = 10\n", "Kp_max = 5.\n", "Ti_max = 5.\n", "d_Kp = Kp_max/n\n", @@ -128,7 +119,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 5, @@ -138,18 +129,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "8c03c1f5a874409cbf1feff48c6ef0d7", + "model_id": "d9226014321c46daa6791bc1dfd0f06d", "version_major": 2, "version_minor": 0 }, - "image/png": 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", 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", 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", + "image/png": 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", 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JybLjV5H1pM0IGACgYnQAASAxHZFFRxQYARdYS3sQAAEgMUbAtGIEDABQMTqAAJCY488CLraetAmAAJAYI2BaMQIGAKgYHUAASIxTwLQiAAJAagqOgOW/9AmAAJAYh0BoxR5AAICK0QEEgMRkUWyKqwGYvsp2AAcHB2PVqlXR3d0d3d3d0dfXFy+++GLZZQFAYR1ZVvgibZUNgEuXLo1HH300du/eHbt3746vfOUrcfPNN8frr79edmkAADOqsiPgNWvWjHv98MMPx+DgYAwNDcXy5ctLqgoAisui4I2gp60S5qrKBsCf1Gg04nvf+14cPnw4+vr6yi4HAArpiGIjvsqOByuk0gFwz5490dfXF0eOHInTTz89tm7dGhdddNGEP1+v16Ner4+9Hh0dnY0yAQCmVaVD/gUXXBCvvvpqDA0NxR/8wR/E2rVr41//9V8n/PmBgYHo6ekZu3p7e2exWgD4bLIsK3yRtkoHwPnz58f5558fl112WQwMDMTFF18cjz322IQ/v3Hjxjhw4MDYNTIyMovVAsBnc+JG0EUu0lbpEfBPy/N83Ij3p9VqtajVarNYEQDA9KtsAHzggQeiv78/ent74+DBg7Fly5bYvn17bNu2rezSAKCQrOCzgE2A01fZAPjBBx/E7bffHu+//3709PTEqlWrYtu2bXHjjTeWXRoAFOIUMK1U9u/4ySefjP/4j/+Ier0e+/fvj1deeUX4AyAJZR4CGRgYiCzL4t57752+L8S0q2wABACm165du+KJJ56IVatWlV0KLQiAAJCYMk4BHzp0KG677bbYtGlTfO5zn5v+L8W0EgABIEFZgWsq1q1bFzfddFPccMMNBStnNlT2EAgAcGo//cSriW6HtmXLlhgeHo7du3fPVmkUpAMIAImZrhFwb2/vuCdgDQwMfOqzRkZG4p577onvfOc7sWDBgln+pkyVDiAAJKboSd4Ta0dGRqK7u3vs/ZN1/4aHh2P//v1x6aWXjr3XaDRi586d8fjjj0e9Xo/Ozs4p18LMEAABgJPq7u4eFwBP5vrrr489e/aMe+/OO++MCy+8MO6//37hb44SAAEgMUWf5zuZtV1dXbFixYpx7y1cuDDOPPPMT73P3CEAAkBiipzmPbGetAmAAMC02r59e9kl0IIACACJ6ciy6ChwCKTIWtqDAAgAicmy41eR9aRNAKQ0nxz6pOwSTq2n7AIApibL88jyvNB60uZG0AAAFaMDCACpyZvHryLrSZoACACJyfJmZAVCXJG1tAcjYACAitEBBIDUGAHTggAIAKnJ8+NXkfUkzQgYAKBidAABIDVGwLQgAAJAYo7fCLrIKWAj4NQZAQMAVIwOIACkxgiYFgRAAEiNAEgLAiAApEYApAV7AAEAKkYHEABSkzcjmjqATEwABIDEZHmz4G1gBMDUGQEDAFSMDiAApMYhEFoQAAEgNXl+/CqynqQZAQMAVIwOIACkxgiYFgRAAEhMlucFTwEbAafOCBgAoGJ0AAEgNUbAtCAAAkBqBEBaEAABIDUCIC3YAwgAUDE6gACQGM8CphUBEABS02wev4qsJ2lGwAAAFaMDCACp8SxgWhAAASA1TgHTghEwAEDF6AACQGKcAqYVHUAASM2JEXCRaxIGBwdj1apV0d3dHd3d3dHX1xcvvvjiDH05poMOIACkJs8L7gGc3CGQpUuXxqOPPhrnn39+REQ89dRTcfPNN8f3v//9WL58+dTrYMYIgJSmPlovu4RT+x9lFwDQHtasWTPu9cMPPxyDg4MxNDQkAM5RAiAApCZvRDQbxdZHxOjo6Li3a7Va1Gq1Uy5tNBrxve99Lw4fPhx9fX1Tr4EZZQ8gACQmbzYLXxERvb290dPTM3YNDAxM+Jl79uyJ008/PWq1Wtx1112xdevWuOiii2brKzNJOoAAwEmNjIxEd3f32OtTdf8uuOCCePXVV+Pjjz+OZ599NtauXRs7duwQAucoARAAUtMsOAL+8doTp3o/i/nz548dArnsssti165d8dhjj8W3vvWtqdfBjBEAASA10xQAi8jzPOr1OX7Yr8IEQACgkAceeCD6+/ujt7c3Dh48GFu2bInt27fHtm3byi6NCQiAAJCYvNGIvDH1Lt5k137wwQdx++23x/vvvx89PT2xatWq2LZtW9x4441TroGZJQACQGqazeNXkfWT8OSTT079syiF28AAAFSMDiAApKbZLHgIpED3kLYgAAJAYvJmI/ICAbDIWtqDAAgAqckL7gHMdQBTZw8gAEDFVDYADgwMxOWXXx5dXV2xaNGiuOWWW+LNN98suywAKOzECLjIRdoqGwB37NgR69ati6GhoXj55Zfj2LFjsXr16jh8+HDZpQFAMSeeBFLkImmV3QP403cn37x5cyxatCiGh4fjmmuuKakqAICZV9kA+NMOHDgQERFnnHFGyZUAQEGzfCNo2o8AGMcfWL1hw4a4+uqrY8WKFRP+XL1eH/dg69HR0dkoDwAmZbYfBUf7qewewJ909913x2uvvRbf/e53T/lzAwMD0dPTM3b19vbOUoUAANOn8gFw/fr18cILL8Q//MM/xNKlS0/5sxs3bowDBw6MXSMjI7NUJQBMwokngUz5MgJOXWVHwHmex/r162Pr1q2xffv2WLZsWcs1tVotarXaLFQHAAUUPcnrFHDyKhsA161bF88880w8//zz0dXVFfv27YuIiJ6enjjttNNKrg4AYOZUNgAODg5GRMS111477v3NmzfHHXfcMfsFAcA0yZvNyAuMcYuspT1UNgDmeV52CQAwM4yAaaGyARAAkpUXDIC5AJi6yp8CBgCoGh1AAEiMPYC0IgACQGpO3AewyHqSZgQMAFAxOoAAkBqngGlBAASAxOSNRuSNqYe4ImtpD0bAAAAVowMIAKlpNosd5HAIJHkCIACkxh5AWjACBgCoGB1AAEhM3mxEXqCLV2Qt7UEApDQ/+u8jZZcAkCRPAqEVARAAEpM388gbRQJgPo3VMBfZAwgAUDE6gACQmLzRLNYBLLCW9iAAAkBi7AGkFSNgAICK0QEEgMQYAdOKAAgAiREAacUIGAAoZGBgIC6//PLo6uqKRYsWxS233BJvvvlm2WVxCgIgACQmbzSiWeDKG5N7EsiOHTti3bp1MTQ0FC+//HIcO3YsVq9eHYcPH56hb0hRRsAAkJg8L3gKOJ/c2m3bto17vXnz5li0aFEMDw/HNddcM+U6mDk6gADAtDpw4EBERJxxxhklV8JEdAABIDHTdQhkdHR03Pu1Wi1qtdqp1+Z5bNiwIa6++upYsWLFlGtgZukAAkBiTgTAIldERG9vb/T09IxdAwMDLT/77rvvjtdeey2++93vzvTXpAAdQABITN7MCz4JJI+IiJGRkeju7h57v1X3b/369fHCCy/Ezp07Y+nSpVP+fGaeAAgAnFR3d/e4ADiRPM9j/fr1sXXr1ti+fXssW7ZsFqqjCAEQABLTbDSjWWAP4GTXrlu3Lp555pl4/vnno6urK/bt2xcRET09PXHaaadNuQ5mjgAIAImZ7SeBDA4ORkTEtddeO+79zZs3xx133DHlOpg5AiAAUEie52WXwCQJgACQGM8CphUBEAASM9tPAqH9uA8gAEDF6AACQGKMgGlFAASAxAiAtGIEDABQMTqAAJCYZrMZzQKHQIqspT0IgACQGCNgWhEAASAxxwNgo9B60mYPIABAxegAAkBi8mbBG0HbA5g8ARAAEpM3C+4BFACTZwQMAFAxOoAAkJqCp4DDIZDkCYAAkJhmoxnNAiGuyFragxEwAEDF6AACQGKcAqYVARAAEuNJILQiAFKaA0eOlV0CzElZh905wMwSAAEgMXkjj7yRF1pP2gRAAEhMs1nwFLA9gMkTAAEgMXkzj7xZoANYYC3twUYTAICK0QEEgMQ0GxHNjql38ZqNaSyGOUkABIDE5I1m5B1uA8PEjIABACpGBxAAEpM38sgLjIDdBiZ9AiAAJKbZyAvuARQAU2cEDABQMTqAAJAYh0BoRQAEgMQ08zyaBW7m3MyNgFNnBAwAUDGVDoA7d+6MNWvWxJIlSyLLsnjuuefKLgkAimvkx08CT/EKh0CSV+kAePjw4bj44ovj8ccfL7sUAJg2zUaz8EXaKr0HsL+/P/r7+8suAwCmVd7II8/cB5CJVboDCABQRZXuAE5WvV6Per0+9np0dLTEagDg5HQAaUUHcBIGBgaip6dn7Ort7S27JAD4lNneA+hQZfsRACdh48aNceDAgbFrZGSk7JIAoHQOVbYfI+BJqNVqUavVyi4DAE4pz/PIC9wIOp/kjaAdqmw/lQ6Ahw4dirfeemvs9TvvvBOvvvpqnHHGGXHOOeeUWBkATF2zkUczCjwJ5Md7AH96r7tGSDoqPQLevXt3XHLJJXHJJZdERMSGDRvikksuiT/5kz8puTIAKF9vb++4ve8DAwNll8Q0qXQH8Nprr510mxsA5rq8kUceU7+Z84lTwCMjI9Hd3T32vu5fOiodAAEgRccDYPHbwHR3d48LgKSj0iNgAIAq0gEEgMRM1yGQz8qhyvYjAAJAYvJmM/IsK7R+Mnbv3h3XXXfd2OsNGzZERMTatWvj29/+9pTrYOYIgACQmNnuADpU2X7sAQQAqBgdQABITN4seAq4wFNEaA8CIACkptGMPJ/6HsCY5B5A2o8RMABAxegAAkBimo08mgUOZTSNgJMnAAJAYvJGXuhUrj2A6TMCBgCoGB3AadCZZdFZ4IabM6lzbpYVERH/p94ou4S2lnV0ll3ChDrmcG0REc3OuV3fXJbN8d/dXP7nIvLZq62ZFxwBu6df8gRAAEhMI8+jUSDEFVlLezACBgCoGB1AAEhMIz9+FVlP2gRAAEiMETCtCIAAkBgdQFqxBxAAoGJ0AAEgMc2CI2C3gUmfAAgAiWlEwRHwtFXCXGUEDABQMTqAAJCYRp5HI5wCZmICIAAkppEXG+M6BZw+I2AAgIrRAQSAxOgA0ooACACJsQeQVoyAAQAqRgcQABLTLDgCbmoAJk8ABIDEGAHTigAIAIlxCIRW7AEEAKgYHUAASMzxDmCREfA0FsOcJAACQGKMgGnFCBgAoGJ0AAEgMU4B04oACACJySOiWXA9aTMCBgCoGB1AAEiMETCtCIAAkBingGnFCBgAoGIEQABITCPPC19T8Zd/+ZexbNmyWLBgQVx66aXxj//4j9P8zZguAiAAJKaRF78m66//+q/j3nvvjT/+4z+O73//+/HlL385+vv74913353+L0hhAiAAJKaMDuBf/MVfxO/8zu/E7/7u78YXv/jF+OY3vxm9vb0xODg4A9+QogRAAKCQo0ePxvDwcKxevXrc+6tXr45//ud/LqkqTsUpYABITLPgKeDmjxuAo6Oj496v1WpRq9U+9fMffvhhNBqN+PznPz/u/c9//vOxb9++ApUwU3QAASAx0zUC7u3tjZ6enrFrYGDglJ+bZdm413mef+o95gYdQADgpEZGRqK7u3vs9cm6fxERP//zPx+dnZ2f6vbt37//U11B5gYBsID8x/8PqZ4XeeLizDo6h2s7khcZUMy85ic/KruEttU89knZJZxS85MjZZdwSvmxetklTGgu1xYRkTeOll3ChE7Ulk/hgMVk/SiahW7mfPTHTxLu7u4eFwAnMn/+/Lj00kvj5Zdfjl/7tV8be//ll1+Om2++eeqFMGMEwAIOHjwYERH/q/4f5RbSrg6VXUALW/+/sisAEnTw4MHo6emZkT97/vz5sXjx4vjOvv8q/GctXrw45s+f/5l/fsOGDXH77bfHZZddFn19ffHEE0/Eu+++G3fddVfhWph+AmABS5YsiZGRkejq6qrEHofR0dHo7e391EiA1vzups7vrhi/v6mb7t9dnudx8ODBWLJkyTRUd3ILFiyId955J44eLd4JnT9/fixYsOAz//xv/uZvxkcffRTf+MY34v33348VK1bE3/3d38W5555buBamX5bPRi+aJIyOjkZPT08cOHDAv0gmye9u6vzuivH7mzq/O1LmFDAAQMUIgAAAFSMA8pnVarX40z/90wlvA8DE/O6mzu+uGL+/qfO7I2X2AAIAVIwOIABAxQiAAAAVIwACAFSMAAgAUDECIC3t3Lkz1qxZE0uWLIksy+K5554ru6S2MDAwEJdffnl0dXXFokWL4pZbbok333yz7LLaxuDgYKxatWrsWaR9fX3x4osvll1WWxoYGIgsy+Lee+8tu5S28OCDD0aWZeOuxYsXl10WTCsBkJYOHz4cF198cTz++ONll9JWduzYEevWrYuhoaF4+eWX49ixY7F69eo4fPhw2aW1haVLl8ajjz4au3fvjt27d8dXvvKVuPnmm+P1118vu7S2smvXrnjiiSdi1apVZZfSVpYvXx7vv//+2LVnz56yS4Jp5VnAtNTf3x/9/f1ll9F2tm3bNu715s2bY9GiRTE8PBzXXHNNSVW1jzVr1ox7/fDDD8fg4GAMDQ3F8uXLS6qqvRw6dChuu+222LRpUzz00ENll9NW5s2bp+tH0nQAYZYcOHAgIiLOOOOMkitpP41GI7Zs2RKHDx+Ovr6+sstpG+vWrYubbropbrjhhrJLaTt79+6NJUuWxLJly+LrX/96vP3222WXBNNKBxBmQZ7nsWHDhrj66qtjxYoVZZfTNvbs2RN9fX1x5MiROP3002Pr1q1x0UUXlV1WW9iyZUsMDw/H7t27yy6l7VxxxRXx9NNPxy/90i/FBx98EA899FBcddVV8frrr8eZZ55ZdnkwLQRAmAV33313vPbaa/FP//RPZZfSVi644IJ49dVX4+OPP45nn3021q5dGzt27BACWxgZGYl77rkn/v7v/z4WLFhQdjlt5ye3vKxcuTL6+vrivPPOi6eeeio2bNhQYmUwfQRAmGHr16+PF154IXbu3BlLly4tu5y2Mn/+/Dj//PMjIuKyyy6LXbt2xWOPPRbf+ta3Sq5sbhseHo79+/fHpZdeOvZeo9GInTt3xuOPPx71ej06OztLrLC9LFy4MFauXBl79+4tuxSYNgIgzJA8z2P9+vWxdevW2L59eyxbtqzsktpenudRr9fLLmPOu/766z91avXOO++MCy+8MO6//37hb5Lq9Xq88cYb8eUvf7nsUmDaCIC0dOjQoXjrrbfGXr/zzjvx6quvxhlnnBHnnHNOiZXNbevWrYtnnnkmnn/++ejq6op9+/ZFRERPT0+cdtppJVc39z3wwAPR398fvb29cfDgwdiyZUts3779U6er+bSurq5P7TVduHBhnHnmmfagfgb33XdfrFmzJs4555zYv39/PPTQQzE6Ohpr164tuzSYNgIgLe3evTuuu+66sdcn9sCsXbs2vv3tb5dU1dw3ODgYERHXXnvtuPc3b94cd9xxx+wX1GY++OCDuP322+P999+Pnp6eWLVqVWzbti1uvPHGsksjce+9917ceuut8eGHH8ZZZ50VV155ZQwNDcW5555bdmkwbbI8z/OyiwAAYPa4DyAAQMUIgAAAFSMAAgBUjAAIAFAxAiAAQMUIgAAAFSMAAgBUjAAIAFAxAiAAQMUIgAAAFSMAAgBUjAAIAFAxAiAAQMUIgAAAFSMAAgBUjAAIAFAxAiAAQMUIgAAAFSMAAgBUjAAIAFAxAiAAQMUIgAAAFSMAAgBUjAAIAFAxAiAAQMUIgAAAFSMAAgBUjAAIAFAxAiAAQMUIgAAAFSMAAgBUzP8FnJJAwKr6vMAAAAAASUVORK5CYII=", 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", + "image/png": 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", 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", 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\n", " Figure\n", "
\n", - " \n", + " \n", "
\n", " " ], @@ -359,9 +350,9 @@ "\n", "fig5 = plt.figure()\n", "plt.plot(t_vec,PV_vec_ise,label='ise')\n", - "# plt.plot(t_vec,PV_vec_iae,'.-',label='iae')\n", + "plt.plot(t_vec,PV_vec_iae,'.-',label='iae')\n", "plt.plot(t_vec,PV_vec_itse,'+',label='itse')\n", - "# plt.plot(t_vec,PV_vec_itae,'*',label='itae')" + "plt.plot(t_vec,PV_vec_itae,'*',label='itae')" ] } ], diff --git a/Turbinendurchfluss über dem Zylinderhub - Francisturbinen M1 und M2_Untertweng.csv b/Turbinen/Durchflusskennlinie.csv similarity index 98% rename from Turbinendurchfluss über dem Zylinderhub - Francisturbinen M1 und M2_Untertweng.csv rename to Turbinen/Durchflusskennlinie.csv index ad80ef2..2eff1de 100644 --- a/Turbinendurchfluss über dem Zylinderhub - Francisturbinen M1 und M2_Untertweng.csv +++ b/Turbinen/Durchflusskennlinie.csv @@ -1,22 +1,22 @@ -,11.4,11.2,11,10.8,10.6,10.4,10.2,10,9.8 -0,0,0,0,0,0,0,0,0,0 -0.05,44.6719225,43.934144,43.3914212,43.005945,42.7411852,42.5620659,42.4351104,42.3285595,42.2124611 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+0.9,816.35361,806.672962,796.856534,786.899741,776.798797,766.550685,756.153132,745.604572,734.904109 +0.95,853.394385,843.377654,833.208949,822.885029,812.403437,801.762466,790.961126,779.999101,768.876705 +1,889.103974,878.779525,868.287549,857.626044,846.793778,835.790258,824.615682,813.270891,801.757325 diff --git a/Turbinen/Turbinen_class_file.py b/Turbinen/Turbinen_class_file.py index 908e692..1b4cecb 100644 --- a/Turbinen/Turbinen_class_file.py +++ b/Turbinen/Turbinen_class_file.py @@ -1,19 +1,30 @@ -def turbine_flux(p,LA,p_exp,cubic_coeff,quadratic_coeff,linear_coeff,const_coeff): - return (p*1e-5)**p_exp*(cubic_coeff*LA**3+quadratic_coeff*LA**2+linear_coeff*LA+const_coeff) +from matplotlib.pyplot import fill +import numpy as np +from scipy.interpolate import interp2d +#importing pressure conversion function +import sys +import os +current = os.path.dirname(os.path.realpath(__file__)) +parent = os.path.dirname(current) +sys.path.append(parent) +from functions.pressure_conversion import pressure_conversion class Francis_turbine_class: - def __init__(self): - pass + def __init__(self,CSV_name='Durchflusskennlinie.csv'): + self.raw_csv = np.genfromtxt(CSV_name,delimiter=',') + + def extract_csv(self,CSV_pressure_unit='bar'): + self.raw_ps_vec,_ = pressure_conversion(self.raw_csv[0,1:],CSV_pressure_unit,'Pa') + self.raw_LA_vec = self.raw_csv[1:,0] + self.raw_Qs_mat = self.raw_csv[1:,1:] + + def get_Q_fun(self): + Q_fun = interp2d(self.raw_ps_vec,self.raw_LA_vec,self.raw_Qs_mat,bounds_error=False,fill_value=None) + return Q_fun + + + + - def set_turbine_flux_parameters(self,p_exp,cubic_coeff,quadratic_coeff,linear_coeff,const_coeff): - # extracted from the Muschelkurve of the Turbine and used to calculate the turbine flux for a given pressure - self.p_exp = p_exp - self.cubic_coeff = cubic_coeff - self.quadratic_coeff = quadratic_coeff - self.linear_coeff = linear_coeff - self.const_coeff = const_coeff - def get_turbine_flux(self,pressure,Leitapparatöffnung): - self.flux = turbine_flux(pressure,Leitapparatöffnung,self.p_exp,self.cubic_coeff,self.quadratic_coeff,self.linear_coeff,self.const_coeff) - return self.flux \ No newline at end of file diff --git a/Turbinen/messy.ipynb b/Turbinen/messy.ipynb index adbe507..b0f9650 100644 --- a/Turbinen/messy.ipynb +++ b/Turbinen/messy.ipynb @@ -2,151 +2,189 @@ "cells": [ { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", + "from numpy.polynomial import Polynomial\n", "from Turbinen_class_file import Francis_turbine_class\n", - "#importing pressure conversion function\n", - "import sys\n", - "import os\n", - "current = os.path.dirname(os.path.realpath('Main_Programm.ipynb'))\n", - "parent = os.path.dirname(current)\n", - "sys.path.append(parent)\n", - "from functions.pressure_conversion import pressure_conversion\n", - "from matplotlib import pyplot as plt\n", - "\n", - "p_exp=0.75\n", - "cubic_coeff=-0.7157\n", - "quadratic_coeff=0.9374\n", - "linear_coeff=0.9696\n", - "const_coeff=-0.0011\n", - "T1 = Francis_turbine_class()\n", - "T1.set_turbine_flux_parameters(p_exp,cubic_coeff,quadratic_coeff,linear_coeff,const_coeff)\n", - "\n", - "pressure,_ = pressure_conversion(5,'bar','Pa')\n", - "\n", - "LA_vec = np.array([0,0.4,0.45,0.5,0.55,0.6,0.65,0.7,0.75,0.8,0.85,0.9,0.95,1.,1.05,1.1,1.15])/1.18\n", - "flux_vec1 = np.empty_like(LA_vec)\n", - "\n", - "n = np.size(flux_vec1)\n", - "for i in range(n):\n", - " flux_vec1[i] = T1.get_turbine_flux(pressure,LA_vec[i])\n", - "\n", - "LA = 0.5\n", - "pressure_vec = np.linspace(0,pressure,500)\n", - "flux_vec2 = np.empty_like(pressure_vec)\n", - "m = np.size(flux_vec2)\n", - "\n", - "for i in range(m):\n", - " flux_vec2[i] = T1.get_turbine_flux(pressure_vec[i],LA)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f1690b8372ac484f9c3554e7f6d3ae5c", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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", 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+ "\n", + "ps_vec = np.linspace(p_min,p_max,n_p)\n", + "ind1 = np.argmin(np.abs(ps_vec-np.min(ps)))\n", + "ind2 = np.argmin(np.abs(ps_vec-np.max(ps)))\n", + "LA_vec = np.linspace(0,1,n_LA)\n", + "\n", + "Q_int = np.reshape(Q_fun(ps_vec,LA_vec),[n_LA,n_p])" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "fit_coeff_matrx = np.empty([n_LA,6])\n", + "\n", + "for i in range(n_LA):\n", + " x = ps_vec\n", + " y = Q_int[i,:]\n", + " fit_coeff_matrx[i,:] = np.polynomial.polynomial.Polynomial.fit(x,y,5).convert().coef\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0. 0.33898305 0.38135593 0.42372881 0.46610169 0.50847458\n", - " 0.55084746 0.59322034 0.63559322 0.6779661 0.72033898 0.76271186\n", - " 0.80508475 0.84745763 0.88983051 0.93220339 0.97457627]\n", - "[-3.67807168e-03 1.36227725e+00 1.55581498e+00 1.75077663e+00\n", - " 1.94606981e+00 2.14060215e+00 2.33328126e+00 2.52301477e+00\n", - " 2.70871029e+00 2.88927545e+00 3.06361786e+00 3.23064514e+00\n", - " 3.38926492e+00 3.53838482e+00 3.67691244e+00 3.80375543e+00\n", - " 3.91782138e+00]\n" - ] + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ddc6b00b9a93476d9413e042c79ee435", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", 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\n", + " \n", + "
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", + "text/html": [ + "\n", + "
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for pressure conversion in print statements and plot labels \n", + "conversion_pressure_unit = 'bar' # for pressure conversion in print statements and plot labels\n", + "area_base = 20. # total base are of the cuboid reservoir [m²] \n", + "area_outflux = A_pipe # outlfux area of the reservoir, given by pipeline area [m²]\n", + "critical_level_low = 0. # for yet-to-be-implemented warnings[m]\n", + "critical_level_high = np.inf # for yet-to-be-implemented warnings[m]\n", + "\n", + "# make sure e-RK4 method of reservoir has a small enough timestep to avoid runaway numerical error\n", + "nt_eRK4 = 1000 # number of simulation steps of reservoir in between timesteps of pipeline \n", + "simulation_timestep = dt/nt_eRK4\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Ideas for checks after constant definitions: \n", + "\n", + "- Check that the initial pressure is not negative:\n", + " - may happen, if there is too little hydraulic head to create the initial flow conditions with the given friction\n", + "
\n", + "
\n", + "- plausbility checks?\n", + " - area > area_outflux ?\n", + " - propable ranges for parameters?\n", + " - angle and height/length fit together?\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": {}, + "outputs": [], + "source": [ + "# create objects\n", + "\n", + "V = Ausgleichsbecken_class(area_base,area_outflux,critical_level_low,critical_level_high,simulation_timestep)\n", + "V.set_initial_level(initial_level) \n", + "V.set_influx(initial_influx)\n", + "V.set_outflux(initial_outflux)\n", + "V.set_pressure(initial_pipeline_pressure,initial_pressure_unit,conversion_pressure_unit)\n", + "\n", + "pipe = Druckrohrleitung_class(L,D,n,alpha,f_D)\n", + "pipe.set_pressure_propagation_velocity(c)\n", + "pipe.set_number_of_timesteps(nt)\n", + "pipe.set_initial_pressure(p_init,initial_pressure_unit,conversion_pressure_unit)\n", + "pipe.set_initial_flow_velocity(v_init)\n", + "\n", + "# display the attributes of the created reservoir and pipeline object\n", + "# V.get_info(full=True)\n", + "# pipe.get_info()" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": {}, + "outputs": [], + "source": [ + "# initialization for timeloop\n", + "\n", + "# prepare the vectors in which the pressure and velocity distribution in the pipeline from the previous timestep are stored\n", + "v_old = v_init.copy()\n", + "p_old = p_init.copy()\n", + "\n", + "# prepare the vectors in which the temporal evolution of the boundary conditions are stored\n", + " # keep in mind, that the velocity at the turbine and the pressure at the reservoir are set manually and\n", + " # through the time evolution of the reservoir respectively \n", + " # the pressure at the turbine and the velocity at the reservoir are calculated from the method of characteristics\n", + "v_boundary_res = np.empty_like(t_vec)\n", + "v_boundary_tur = np.empty_like(t_vec)\n", + "p_boundary_res = np.empty_like(t_vec)\n", + "p_boundary_tur = np.empty_like(t_vec)\n", + "\n", + "# prepare the vectors that store the temporal evolution of the level in the reservoir\n", + "level_vec = np.full(nt+1,initial_level) # level at the end of each pipeline timestep\n", + "level_vec_2 = np.empty([nt_eRK4]) # level throughout each reservoir timestep-used for plotting and overwritten afterwards\n", + "\n", + "# set the boudary conditions for the first timestep\n", + "v_boundary_res[0] = v_old[0]\n", + "v_boundary_tur[0] = v_old[-1] \n", + "v_boundary_tur[1:] = 0 # instantaneous closing\n", + "# v_boundary_tur[0:20] = np.linspace(v_old[-1],0,20) # overwrite for finite closing time - linear case\n", + "# const = int(np.min([100,round(nt/1.1)]))\n", + "# v_boundary_tur[0:const] = v_old[1]*np.cos(t_vec[0:const]*2*np.pi/5)**2\n", + "p_boundary_res[0] = p_old[0]\n", + "p_boundary_tur[0] = p_old[-1]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": {}, + "outputs": [], + "source": [ + "%matplotlib qt5\n", + "# time loop\n", + "\n", + "# create a figure and subplots to display the velocity and pressure distribution across the pipeline in each pipeline step\n", + "fig1,axs1 = plt.subplots(2,1)\n", + "fig1.suptitle(str(0) +' s / '+str(round(t_vec[-1],2)) + ' s' )\n", + "axs1[0].set_title('Pressure distribution in pipeline')\n", + "axs1[1].set_title('Velocity distribution in pipeline')\n", + "axs1[0].set_xlabel(r'$x$ [$\\mathrm{m}$]')\n", + "axs1[0].set_ylabel(r'$p$ ['+conversion_pressure_unit+']')\n", + "axs1[1].set_xlabel(r'$x$ [$\\mathrm{m}$]')\n", + "axs1[1].set_ylabel(r'$v$ [$\\mathrm{m} / \\mathrm{s}$]')\n", + "lo_00, = axs1[0].plot(pl_vec,pressure_conversion(pipe.p_old,initial_pressure_unit, conversion_pressure_unit)[0],marker='.')\n", + "lo_01, = axs1[1].plot(pl_vec,pipe.v_old,marker='.')\n", + "axs1[0].autoscale()\n", + "axs1[1].autoscale()\n", + "# displaying the reservoir level within each pipeline timestep\n", + "# axs1[2].set_title('Level reservoir')\n", + "# axs1[2].set_xlabel(r'$t$ [$\\mathrm{s}$]')\n", + "# axs1[2].set_ylabel(r'$h$ [m]')\n", + "# lo_02, = axs1[2].plot(level_vec_2)\n", + "# axs1[2].autoscale()\n", + "fig1.tight_layout()\n", + "fig1.show()\n", + "plt.pause(1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": {}, + "outputs": [], + "source": [ + "# loop through time steps of the pipeline\n", + "for it_pipe in range(1,pipe.nt+1):\n", + "\n", + "# for each pipeline timestep, execute nt_eRK4 timesteps of the reservoir code\n", + " # set initial conditions for the reservoir time evolution calculted with e-RK4\n", + " V.pressure = p_old[0]\n", + " V.outflux_vel = v_old[0]\n", + " # calculate the time evolution of the reservoir level within each pipeline timestep to avoid runaway numerical error\n", + " for it_res in range(nt_eRK4):\n", + " V.e_RK_4() # call e-RK4 to update outflux\n", + " V.level = V.update_level(V.timestep) # \n", + " V.set_volume() # update volume in reservoir\n", + " level_vec_2[it_res] = V.level # save for plotting\n", + " if (V.level < critical_level_low) or (V.level > critical_level_high): # make sure to never exceed critical levels\n", + " i_max = it_pipe # for plotting only calculated values\n", + " break \n", + " level_vec[it_pipe] = V.level \n", + "\n", + " # set boundary conditions for the next timestep of the characteristic method\n", + " p_boundary_res[it_pipe] = rho*g*V.level-V.outflux_vel**2*rho/2\n", + " v_boundary_res[it_pipe] = v_old[1]+1/(rho*c)*(p_boundary_res[it_pipe]-p_old[1])-f_D*dt/(2*D)*abs(v_old[1])*v_old[1] \\\n", + " +dt*g*np.sin(alpha)\n", + "\n", + " # the the boundary conditions in the pipe.object and thereby calculate boundary pressure at turbine\n", + " pipe.set_boundary_conditions_next_timestep(v_boundary_res[it_pipe],p_boundary_res[it_pipe],v_boundary_tur[it_pipe])\n", + " p_boundary_tur[it_pipe] = pipe.p_boundary_tur\n", + "\n", + " # perform the next timestep via the characteristic method\n", + " pipe.timestep_characteristic_method()\n", + "\n", + " # plot some stuff\n", + " # remove line-objects to autoscale axes (there is definetly a better way, but this works ¯\\_(ツ)_/¯ )\n", + " lo_00.remove()\n", + " lo_01.remove()\n", + " # lo_02.remove()\n", + " # plot new pressure and velocity distribution in the pipeline\n", + " lo_00, = axs1[0].plot(pl_vec,pressure_conversion(pipe.p_old,initial_pressure_unit, conversion_pressure_unit)[0],marker='.',c='blue')\n", + " lo_01, = axs1[1].plot(pl_vec,pipe.v_old,marker='.',c='blue')\n", + " # lo_02, = axs1[2].plot(level_vec_2,c='blue')\n", + " fig1.suptitle(str(round(t_vec[it_pipe],2))+ ' s / '+str(round(t_vec[-1],2)) + ' s' )\n", + " fig1.canvas.draw()\n", + " fig1.tight_layout()\n", + " fig1.show()\n", + " plt.pause(0.00001) \n", + "\n", + " # prepare for next loop\n", + " p_old = pipe.p_old\n", + " v_old = pipe.v_old \n", + "\n", + " \n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": {}, + "outputs": [], + "source": [ + "# plot time evolution of boundary pressure and velocity as well as the reservoir level\n", + "\n", + "fig2,axs2 = plt.subplots(3,2)\n", + "axs2[0,0].plot(t_vec,pressure_conversion(p_boundary_res,initial_pressure_unit, conversion_pressure_unit)[0])\n", + "axs2[0,1].plot(t_vec,v_boundary_res)\n", + "axs2[1,0].plot(t_vec,pressure_conversion(p_boundary_tur,initial_pressure_unit, conversion_pressure_unit)[0])\n", + "axs2[1,1].plot(t_vec,v_boundary_tur)\n", + "axs2[2,0].plot(t_vec,level_vec)\n", + "axs2[0,0].set_title('Pressure reservoir')\n", + "axs2[0,1].set_title('Velocity reservoir')\n", + "axs2[1,0].set_title('Pressure turbine')\n", + "axs2[1,1].set_title('Velocity turbine')\n", + "axs2[2,0].set_title('Level reservoir')\n", + "axs2[0,0].set_xlabel(r'$t$ [$\\mathrm{s}$]')\n", + "axs2[0,0].set_ylabel(r'$p$ ['+conversion_pressure_unit+']')\n", + "axs2[0,1].set_xlabel(r'$t$ [$\\mathrm{s}$]')\n", + "axs2[0,1].set_ylabel(r'$v$ [$\\mathrm{m}/\\mathrm{s}$]')\n", + "axs2[1,0].set_xlabel(r'$t$ [$\\mathrm{s}$]')\n", + "axs2[1,0].set_ylabel(r'$p$ ['+conversion_pressure_unit+']')\n", + "axs2[1,1].set_xlabel(r'$t$ [$\\mathrm{s}$]')\n", + "axs2[1,1].set_ylabel(r'$v$ [$\\mathrm{m}/\\mathrm{s}$]')\n", + "axs2[2,0].set_xlabel(r'$t$ [$\\mathrm{s}$]')\n", + "axs2[2,0].set_ylabel(r'$h$ [m]')\n", + "axs2[2,1].axis('off')\n", + "fig2.tight_layout()\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.13 ('Georg_DT_Slot3')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/untertweng.txt b/untertweng.txt new file mode 100644 index 0000000..7e77ac8 --- /dev/null +++ b/untertweng.txt @@ -0,0 +1,20 @@ +L = 535 m dn 800 mm +478 m dn 1000 mm +Ersatzdurchmesser + +h_pipe + +h 851.78 Pegel + Leitungsgefälle +Leitungsgefälle: 113 + +Fläche 4.25x10.5 + 30m² = 74 m² +Pegelminimum: 851.18 m + +Unterwasserpegel 738.56 +Gesamtfallhöhe = 851.78-738.56 + +Rohrreibung: 0.014 f_D = lambda +c = 500 m/s + +Q_0 = 100%*0.75+30%*0.75 +Q_extrem = 30%*0.75 From d966904606451ecd64c6df5441e89e30657fba74 Mon Sep 17 00:00:00 2001 From: Brantegger Georg Date: Wed, 20 Jul 2022 15:43:51 +0200 Subject: [PATCH 08/12] added first try for turbine flux based on pressure and LA opening --- Turbinen/Turbinen_class_file.py | 38 +-- Turbinen/Turbinen_test.ipynb | 239 +++++++++++++++ Turbinen/messy.ipynb | 212 -------------- Turbinen/{ => old}/Durchflusskennlinie.csv | 0 Turbinen/old/Turbinen_class_file.py | 33 +++ Turbinen/old/Turbinen_test.ipynb | 319 +++++++++++++++++++++ Turbinen/old/messy.ipynb | 278 ++++++++++++++++++ Untertweng.ipynb | 62 ++-- untertweng.txt | 2 + 9 files changed, 930 insertions(+), 253 deletions(-) create mode 100644 Turbinen/Turbinen_test.ipynb delete mode 100644 Turbinen/messy.ipynb rename Turbinen/{ => old}/Durchflusskennlinie.csv (100%) create mode 100644 Turbinen/old/Turbinen_class_file.py create mode 100644 Turbinen/old/Turbinen_test.ipynb create mode 100644 Turbinen/old/messy.ipynb diff --git a/Turbinen/Turbinen_class_file.py b/Turbinen/Turbinen_class_file.py index 1b4cecb..8290976 100644 --- a/Turbinen/Turbinen_class_file.py +++ b/Turbinen/Turbinen_class_file.py @@ -1,7 +1,4 @@ -from matplotlib.pyplot import fill import numpy as np -from scipy.interpolate import interp2d - #importing pressure conversion function import sys import os @@ -10,21 +7,28 @@ parent = os.path.dirname(current) sys.path.append(parent) from functions.pressure_conversion import pressure_conversion -class Francis_turbine_class: - def __init__(self,CSV_name='Durchflusskennlinie.csv'): - self.raw_csv = np.genfromtxt(CSV_name,delimiter=',') - - def extract_csv(self,CSV_pressure_unit='bar'): - self.raw_ps_vec,_ = pressure_conversion(self.raw_csv[0,1:],CSV_pressure_unit,'Pa') - self.raw_LA_vec = self.raw_csv[1:,0] - self.raw_Qs_mat = self.raw_csv[1:,1:] - - def get_Q_fun(self): - Q_fun = interp2d(self.raw_ps_vec,self.raw_LA_vec,self.raw_Qs_mat,bounds_error=False,fill_value=None) - return Q_fun - - +class Francis_Turbine: + def __init__(self, Q_nenn,p_nenn): + self.Q_n = Q_nenn + self.p_n = p_nenn + self.LA_n = 1. # 100% + h,_ = pressure_conversion(p_nenn,'Pa','MWs') + self.A = Q_nenn/(np.sqrt(2*9.81*h)*0.98) + def set_LA(self,LA): + self.LA = LA + def get_Q(self,p): + self.Q = self.Q_n*(self.LA/self.LA_n)*np.sqrt(p/self.p_n) + return self.Q + def set_closing_time(self,t_closing): + self.t_c = t_closing + self.d_LA_max_dt = 1/t_closing + def change_LA(self,LA_soll,timestep): + LA_diff = self.LA-LA_soll + LA_diff_max = self.d_LA_max_dt*timestep + if abs(LA_diff) > LA_diff_max: + LA_diff = np.sign(LA_diff)*LA_diff_max + self.LA = self.LA-LA_diff \ No newline at end of file diff --git a/Turbinen/Turbinen_test.ipynb b/Turbinen/Turbinen_test.ipynb new file mode 100644 index 0000000..23d5dd8 --- /dev/null +++ b/Turbinen/Turbinen_test.ipynb @@ -0,0 +1,239 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from Turbinen_class_file import Francis_Turbine\n", + "from mpl_toolkits import mplot3d\n", + "import matplotlib.pyplot as plt\n", + "%matplotlib widget\n", + "\n", + "#importing pressure conversion function\n", + "import sys\n", + "import os\n", + "current = os.path.dirname(os.path.realpath('messy.ipynb'))\n", + "parent = os.path.dirname(current)\n", + "sys.path.append(parent)\n", + "from functions.pressure_conversion import pressure_conversion\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.018834355072879172\n" + ] + } + ], + "source": [ + "Q_nenn = 0.85\n", + "p_nenn,_ = pressure_conversion(10.6,'bar','Pa')\n", + "Untertweng1 = Francis_Turbine(Q_nenn,p_nenn)\n", + "print(Untertweng1.A)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "n_p = 201\n", + "n_LA = 201\n", + "\n", + "ps = np.linspace(0,2*Untertweng1.p_n,n_p,endpoint=True)\n", + "LAs = np.linspace(0,1,n_LA,endpoint=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Q [m³/s]')" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a76ad86e29fd4594a88621371ce94083", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "Q_mat = np.empty([n_LA,n_p])\n", + "\n", + "pp,ll = np.meshgrid(ps,LAs)\n", + "Q_mat = Untertweng1.get_Q(pp,ll)\n", + "\n", + "fig1 = plt.figure()\n", + "ax1 = plt.axes(projection='3d')\n", + "\n", + "ax1.plot_surface(pp, ll, Q_mat,cmap='viridis', edgecolor='none')\n", + "ax1.set_xlabel('P [Pa]')\n", + "ax1.set_ylabel('LA')\n", + "ax1.set_zlabel('Q [m³/s]')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2b95e8650af64ab7a401d9624aef4b85", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", 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\n", + " \n", + "
\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "d_LA_test = 1/(n_LA-1)\n", + "\n", + "LA_test = np.arange(0,n_LA,1)*d_LA_test\n", + "\n", + "fig = plt.figure()\n", + "plt.xlabel('Druck [Pa]')\n", + "plt.ylabel('Q [m³/s]')\n", + "\n", + "for i in range(n_LA):\n", + " LA_index = int(np.argwhere(LAs == LA_test[i]))\n", + " plt.plot(ps,Q_mat[LA_index,:])" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'P = 1144800.0 [Pa]')" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "bce1ff4c4e1946ff93e954cd79af4bfe", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", 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+ "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Turbinen/messy.ipynb b/Turbinen/messy.ipynb deleted file mode 100644 index b0f9650..0000000 --- a/Turbinen/messy.ipynb +++ /dev/null @@ -1,212 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "from numpy.polynomial import Polynomial\n", - "from Turbinen_class_file import Francis_turbine_class\n", - "from mpl_toolkits import mplot3d\n", - "import matplotlib.pyplot as plt\n", - "%matplotlib widget" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "T1 = Francis_turbine_class()\n", - "csv = T1.raw_csv\n", - "\n", - "T1.extract_csv()\n", - "ps = T1.raw_ps_vec\n", - "LAs = T1.raw_LA_vec\n", - "Qs = T1.raw_Qs_mat\n", - "\n", - "Q_fun = T1.get_Q_fun()" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [], - "source": [ - "p_min = np.min(ps)\n", - "p_max = np.max(ps)\n", - "\n", - "n_p = 100\n", - "n_LA = 200\n", - "\n", - "ps_vec = np.linspace(p_min,p_max,n_p)\n", - "ind1 = np.argmin(np.abs(ps_vec-np.min(ps)))\n", - "ind2 = np.argmin(np.abs(ps_vec-np.max(ps)))\n", - "LA_vec = np.linspace(0,1,n_LA)\n", - "\n", - "Q_int = np.reshape(Q_fun(ps_vec,LA_vec),[n_LA,n_p])" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "fit_coeff_matrx = np.empty([n_LA,6])\n", - "\n", - "for i in range(n_LA):\n", - " x = ps_vec\n", - " y = Q_int[i,:]\n", - " fit_coeff_matrx[i,:] = np.polynomial.polynomial.Polynomial.fit(x,y,5).convert().coef\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - }, - { - 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", 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", - "text/html": [ - "\n", - "
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\n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "XX2,YY2 = np.meshgrid(ps_vec,LA_vec)\n", - "\n", - "fig2 = plt.figure()\n", - "ax2 = plt.axes(projection='3d')\n", - "\n", - "ax2.plot_surface(XX2, YY2, Q_int,cmap='viridis', edgecolor='none')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "fig3 = plt.figure()\n", - "ax3 = plt.axes()\n", - "\n", - "# ax3.scatter(XX1[-1,:], (Qs[-1,:]-((XX1[-1,:]-XX1[-1,0])/(XX1[-1,-1]-XX1[-1,0])*(Qs[-1,-1]-Qs[-1,0])+Qs[-1,0])))\n", - "ax3.scatter(XX2[-1,:], (Q_int[-1,:]-((XX2[-1,:]-XX2[-1,ind1])/(XX2[-1,ind2]-XX2[-1,ind1])*(Q_int[-1,ind2]-Q_int[-1,0])+Q_int[-1,ind1])))\n", - "\n" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.13 ('Georg_DT_Slot3')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Turbinen/Durchflusskennlinie.csv b/Turbinen/old/Durchflusskennlinie.csv similarity index 100% rename from Turbinen/Durchflusskennlinie.csv rename to Turbinen/old/Durchflusskennlinie.csv diff --git a/Turbinen/old/Turbinen_class_file.py b/Turbinen/old/Turbinen_class_file.py new file mode 100644 index 0000000..457d819 --- /dev/null +++ b/Turbinen/old/Turbinen_class_file.py @@ -0,0 +1,33 @@ +from matplotlib.pyplot import fill +import numpy as np +from scipy.interpolate import interp2d + +#importing pressure conversion function +import sys +import os +current = os.path.dirname(os.path.realpath(__file__)) +parent = os.path.dirname(current) +sys.path.append(parent) +from functions.pressure_conversion import pressure_conversion + +class Francis_turbine_class: + def __init__(self,CSV_name='Durchflusskennlinie.csv'): + csv = np.genfromtxt(CSV_name,delimiter=',') + n_rows,_ = np.shape(csv) + self.raw_csv = np.append(csv,np.zeros([n_rows,1]),axis = 1) + + def extract_csv(self,CSV_pressure_unit='bar'): + ps_vec,_ = pressure_conversion(self.raw_csv[0,1:],CSV_pressure_unit,'Pa') + self.raw_ps_vec = np.flip(ps_vec) + self.raw_LA_vec = self.raw_csv[1:,0] + self.raw_Qs_mat = np.fliplr(self.raw_csv[1:,1:])/1000. # convert from l/s to m³/s + + def get_Q_fun(self): + Q_fun = interp2d(self.raw_ps_vec,self.raw_LA_vec,self.raw_Qs_mat,bounds_error=False,fill_value=None) + return Q_fun + + + + + + diff --git a/Turbinen/old/Turbinen_test.ipynb b/Turbinen/old/Turbinen_test.ipynb new file mode 100644 index 0000000..52e26e6 --- /dev/null +++ b/Turbinen/old/Turbinen_test.ipynb @@ -0,0 +1,319 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from numpy.polynomial import Polynomial\n", + "from numpy.polynomial.polynomial import polyval\n", + "from Turbinen_class_file import Francis_turbine_class\n", + "from mpl_toolkits import mplot3d\n", + "import matplotlib.pyplot as plt\n", + "\n", + "#importing pressure conversion function\n", + "import sys\n", + "import os\n", + "current = os.path.dirname(os.path.realpath('messy.ipynb'))\n", + "parent = os.path.dirname(current)\n", + "sys.path.append(parent)\n", + "from functions.pressure_conversion import pressure_conversion\n", + "\n", + "%matplotlib widget\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [], + "source": [ + "# create turbine object\n", + "T1 = Francis_turbine_class()\n", + "csv = T1.raw_csv\n", + "\n", + "T1.extract_csv()\n", + "ps_raw = T1.raw_ps_vec\n", + "n_ps_raw = np.size(T1.raw_ps_vec)\n", + "LAs_raw = T1.raw_LA_vec\n", + "n_LAs_raw = np.size(T1.raw_LA_vec)\n", + "Qs_raw = T1.raw_Qs_mat\n", + "\n", + "Q_fun = T1.get_Q_fun()" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "# interpolate Qs for more LAs\n", + "\n", + "n_LA_int = 2001\n", + "\n", + "LA_int = np.linspace(0,1,n_LA_int,endpoint=True)\n", + "\n", + "Q_int = np.reshape(Q_fun(ps_raw,LA_int),[n_LA_int,n_ps_raw])" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "# fit a polynomial to the data from Qs_Raw\n", + " # receive a coefficient matrix \n", + "poly_deg = 3\n", + "fit_coeff_mat = np.empty([n_LA_int,poly_deg+1])\n", + "fit_weights = np.ones_like(ps_raw)\n", + "fit_weights[0] = 20 # put extra weight on the p=0 => Q=0 data\n", + "\n", + "for i in range(n_LA_int):\n", + " x = ps_raw\n", + " y = Q_int[i,:]\n", + " fit_coeff_mat[i,:] = np.polynomial.polynomial.Polynomial.fit(x,y,poly_deg,w=fit_weights).convert().coef\n" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "p_ext_min = 0\n", + "p_ext_max = 2.5*np.max(ps_raw)\n", + "n_p_ext = 200\n", + "\n", + "p_ext = np.linspace(p_ext_min,p_ext_max,n_p_ext)\n", + "\n", + "Qs_ext = np.zeros([n_LA_int,n_p_ext])\n", + "\n", + "for i in range(n_LA_int):\n", + " Qs_ext[i,:] = polyval(p_ext,fit_coeff_mat[i,:])\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'LA = 0.05')" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "a89d999414644e6a8d81f1e32c31e49b", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", + "text/html": [ + "\n", + "
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\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "LA_test = 0.05\n", + "LA_index = int(np.argwhere(LA_int == LA_test))\n", + "\n", + "fig = plt.figure()\n", + "plt.plot(p_ext,Qs_ext[LA_index,:])\n", + "plt.xlabel('Druck [Pa]')\n", + "plt.ylabel('Q [m³/s]')\n", + "plt.title('LA = '+ str(LA_test))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": {}, + "outputs": [], + "source": [ + "def Q_formel(p,LA):\n", + " x= LA\n", + " y = p*1e-5\n", + " a = -215.9324\n", + " b = +332.86766\n", + " c = -2.40164\n", + " d = +67.45335\n", + " e = -6.92688\n", + " f = +0.23341\n", + " g = -0.0225\n", + " h = +0.0499\n", + " i = -0.09458\n", + " j = +0.00324\n", + " k = +1.\n", + " return (a+b*x+c*x**2+d*y+e*y**2+f*y**3)/(1+g*x+h*x**2+i*y+j*y**2)*k/1000" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'P = 1145728.6432160805 [Pa]')" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "32ee8a91fb3c43dcb085155ea58f3b41", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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UCgwM1E033WRzn1u0aKFff/1VWVlZV3zNK/ljnY5q0KCBYmNj1bJlS5vwJ13arPLbb79pzZo16t+/v2699VbFxsZaj+gBfBUjgIATlecp4Hvuucc6mnO53r17q379+po+fbrNmq/iLFmyRFLRKXLp0tlzS5cuVevWrUsMA4Xh74cfflBqamqJ08jXXnut8vPztX37dpvpuv379+v48eOqXbv2Fevs0aOHxo8frw0bNthMRW/YsEGGYVjrL4t7snnzZhUUFBRp9+yzz2ry5Ml65plnrjhaaVZCQoJCQ0O1YcMGm5HGXbt2KSsry+bvpEePHho1apS2bdtmvX95eXlaunSp4uLirP/+Cv/vF198YXM2YG5urnbt2mVzn7t166ZnnnlGb775pv72t79Zry9evFgVKlTQPffcc8X6v/jiC3333XdO23leqHAq+vJRW8Mw9Prrrzv1fYHyjgAIOFGLFi3K7LV++uknbd++XdKlXY2SlJKSIunSUzoKpyGlSyHk119/lXRpA8tPP/1kbduxY0fr7s0/7uCULh2pUrVqVd1+++3Wa8uXL9eaNWvUpUsXRUdH69SpU1q1apVWrlypwYMHF7tZYu3atTpx4kSJo3/nzp3T3Xffrd27d+vFF19UXl6ezXRg9erV1bBhQ0mXNjr861//0gMPPKBnnnlGjRs31g8//KDnnntOYWFhNgdPL1myRA899JAWLVqkgQMHSpKaNGmiRx55RElJSapUqZISEhK0f/9+PfPMM2rZsqV69eolSXbdk3fffVevv/66unbtqujoaOsGhBdffFGNGjWy+dwvvPCCJk6cqHvuuUddunQpMu15eVj79ddfrYcQF46mbdiwQdWrV1f16tWtweyaa67R1KlT9eSTT2rw4MHq06ePsrKyNGHCBNWtW9fmCScPPfSQ5s6dqwcffFDPP/+8atSooaSkJP3f//2fPvzwQ2u7W2+9VbfccosmT56ss2fP6rbbbtPp06c1Z84cHThwwGazzw033KChQ4dq0qRJ8vf31y233KKNGzdq/vz5mjZtms0U8E033aT+/furadOm1unXmTNnqmbNmvrrX/9a7L+PstK5c2cFBQWpT58++utf/6rz589r3rx5OnnypFPfFyj33HT+IOARSjoI2p21FPfzx4OeO3bsWGLbTZs2XfF9ijv0+PPPPzfuuusuo2bNmkZgYKARGhpq3HLLLUZSUpLNwcWX69y5sxEWFmZkZ2cX+/sDBw6UWGNxn+m7774zBgwYYNSrV88IDg426tatayQmJhrffvttsffpjTfesLmel5dnPP/880ajRo2MwMBAIyoqyvjzn/9snDx58or3o6R7sm/fPqNnz55GdHS0ERISYoSEhBhNmjQxnnrqKZtDkA3jyn8ff/yv4cLDjYv76dixY5HaXn/9daN58+ZGUFCQUbVqVaNfv37GoUOHirTLysoyBg4caFSpUsUICQkx2rRpY6SmphZpd+rUKWP8+PFG06ZNjdDQUKNGjRrG7bffbqxfv75I2wsXLhiTJk0y6tatawQFBRnXX3+98fLLLxdp17t3b6NRo0ZGWFiYERgYaERHRxsjR440jh49Wuz9/iPZeRD0H73zzjvGTTfdZISEhBi1atUynnrqKWPDhg0l/ueBg6DhCyyGYWKFOgAAbmKxWDRhwgRNnDhR/v7+pg/ztldBQYEKCgo0dOhQrV69WmfOnHHK+wDlAZtAAADl3rPPPqvAwEC98MILTnuPsWPHKjAw0Lq2FfBmjAACAMq1HTt2WP//OnXqXPX8TUcdOnRIP//8s6RLjyosaVMS4A0IgAAAAD6GKWAAAAAfQwAEAADwMQRAAAAAH0MABAAA8DE8CaQUCgoKdPToUVWqVMlp51IBAICyZRiGcnJydO2115bJ86g9EQGwFI4ePVrkweMAAMAzHDp06KrPEvdWBMBSqFSpkqRL/4DCw8PdXA0AADAjOztbderUsX6P+yICYCkUTvuGh4cTAAEA8DC+vHzLNye+AQAAfBgBEAAAwMcQAAEAAHwMARAAAMDHEAABAAB8DAEQAADAxxAAAQAAfAwBEAAAwMcQAAEAAHwMARAAAMDHEAABAED5kpEmTY+WplSVti1wdzVeiQAIAADKh8x06cWbpaX3S7mnJCNP+vETd1fllQLcXQAAAPBxOVnSe09J+zdIBRdtfpWdm6dwN5XlzQiAAADAfdJTpLV/lgouFPvroB8+lF6Jk0IqSy16SnHDXFygdyIAAgAA18tMl5IHSqcOXLFZiHKlY//9X5+dBMAyQgAEAACuk5Ml/edRKWOjff0q1ZZuHeOcmnwQARAAALhGeor0n1FSfq4dnfykdo9LbUdKlWo6rTRfQwAEAADOlZkurXpIOpEhybCvb73bpPjJzqjKpxEAAQCAczg63VvI4i81va9sa4IkAiAAAHAGh6Z7C1mkRp2lbnOY9nUSAiAAACg7GWlSylDp/EnZPd0ri1T1OqnnQinqRmdUh/8hAAIAgNIrPMz5v+9Jyre/f0CI1HWudGPPMi8NRREAAQBA6ZRqutdPSpjJ+X4uRgAEAACOsU73nnCsf+UGUq83me51AwIgAACwT2mne4PDpQfflBrdWealwRwCIAAAMC8jTfr3IOlCtv19LYFS4wSpy0x297oZARAAAFxdaQ5zlpjuLWcIgAAAoGRM93olAiAAACheqaZ7A6TG9zLdW04RAAEAgK1SPcLNT6raiMOcyzkCIAAA+F16irT2z1LBBfv7Mt3rMQiAAADgsk0e39nfl+lej0MABADAl5Vqupdn93oqAiAAAL6qNI9w8w+WuiXx7F4PRQAEAMDXlOpMP3+pSRemez0cARAAAF+SniK9/bBk5Nnft0IV6YGFbPLwAgRAAAB8QUaalDJUOn/C/r7+QVK3eUz3ehECIAAA3sz6JI93JRXY379RvNRtDtO9XoYACACAtyrNkzw408+rEQABAPA2pTnaxRIoNU5gk4eXIwACAOBNSvMkj8oNpF5vcqafDyAAAgDgDUrzJA+me30OARAAAE+3bYG04Ulxph/M8nN3AWUpKSlJ9evXV0hIiGJiYrRly5Yrtl+2bJluuukmhYaGKioqSkOGDNHx48ddVC0AAKWUkSY9X1/a8ITsDn8Vqkj9U6TebxH+fJDXBMDk5GSNHj1a48eP1+7du9WhQwclJCTo4MGDxbb/9NNPNXDgQA0dOlTffvutVq1ape3bt2vYsGEurhwAADvlZEkrB0hLH7D/XD+/QOn+hdLfDjDl68MshmHYO15cLsXFxalVq1aaN2+e9VrTpk3VvXt3TZ8+vUj7WbNmad68efr++++t1+bMmaMZM2bo0KFDpt4zOztbEREROn36tMLDw0v/IQAAuBqHj3axSFWvk3ou9PlNHnx/e8kI4IULF7Rz507Fx8fbXI+Pj9fWrVuL7dOuXTsdPnxY69evl2EY+vnnn5WSkqIuXbq4omQAAOyTmS69HCstvd/+8OcfJN2/QHp0u8+HP1ziFZtAjh07pvz8fEVGRtpcj4yMVFZWVrF92rVrp2XLlikxMVHnz59XXl6eunbtqjlz5pT4Prm5ucrNzbX+OTvbgYM1AQCwV2me38uTPFAMrxgBLGSxWGz+bBhGkWuF9u7dq8cee0wTJ07Uzp079f777+vAgQMaOXJkia8/ffp0RUREWH/q1KlTpvUDAGAjM1168WZpzVD7w19wuNT/ban/KsIfivCKNYAXLlxQaGioVq1apR49elivP/7449qzZ482b95cpM+AAQN0/vx5rVq1ynrt008/VYcOHXT06FFFRUUV6VPcCGCdOnV8eg0BAMBJHB31swRIje/laJcrYA2gl0wBBwUFKSYmRqmpqTYBMDU1Vd26dSu2z9mzZxUQYPvx/f39JV0aOSxOcHCwgoODy6hqAACKUZoDnXmSB0zyigAoSWPHjtWAAQMUGxurtm3bav78+Tp48KB1SnfcuHE6cuSIlixZIkm67777NHz4cM2bN0933323MjMzNXr0aLVu3VrXXnutOz8KAMBXbVvwvzP97OQXKHV/VbqxZ9nXBK/kNQEwMTFRx48f19SpU5WZmanmzZtr/fr1io6OliRlZmbanAk4ePBg5eTk6JVXXtETTzyha665Rnfeeaf++c9/uusjAAB8VUaalDLU/jP9ONoFDvKKNYDuwhoCAECp5GRJ7z0l/fddSQX29fUPlrolMernAL6/vWgEEAAAj+Lwgc48vxelRwAEAMDVHF3rF3KN1PMNHuGGUiMAAgDgKhlp0qohUu4pOzv6SQkzpTieV4+yQQAEAMDZcrKk/zwqZWy0v29EXan3MjZ5oEwRAAEAcKb0FOk/o6T83Ku3vRwHOsOJCIAAADiDdYfvOvv7cqAznIwACABAWctIk5IHSBfP2NePA53hIgRAAADKSmke41b1eg50hssQAAEAKAsZadLKflLeWfv6+QdJ3eYx6geXIgACAFAapdnh2yhe6jaHTR5wOQIgAACOSk+R3h4pGRft68eoH9yMAAgAgL1Ks8OXUT+UAwRAAADskZ4irf2zVHDBvn5BlaReS3iMG8oFAiAAAGY4POrnJzX5Ewc6o1whAAIAcDWOnuvHWj+UUwRAAABK4vAOX0b9UL4RAAEAKE5GmvTvQdKFbPv6BYdLD77JWj+UawRAAAAuZ13r944kw76+7PCFhyAAAgBQyNFRP3b4wsMQAAEAcHjUj7V+8EwEQACAb8tMl5YlSmeO2tePUT94MAIgAMB3bVsgbXjCzk6M+sHzEQABAL4nM11KHiidOmBfP78gqTvn+sHzEQABAL7FoVE/scMXXoUACADwDZnp0qqHpBPf2dcvIFTqvYy1fvAqBEAAgPdLT5HWjJCUb0cni1T1OqnnQinqRmdVBrgFARAA4L0cfZQbo37wcgRAAIB3Sk+R3h4pGRft61f1ekb94PUIgAAA72I91Hmdff38AqXur7LDFz6BAAgA8B7pKdLaP0sFF+zrV7mB1OtNRv3gMwiAAADP5+ionyVA6vEao37wOQRAAIBnc/RRboz6wYcRAAEAnsuRQ50t/lKP+Yz6wacRAAEAnsfRR7mFXCP1fIPjXeDzCIAAAM+SniK9/bBk5NnRySIlzJLihjmtLMCTEAABAJ7B0Ue5BVWSei1h1A+4DAEQAFD+ZaRJK/pK+efs6OQnNfmT1GWmVKmm00oDPBEBEABQvjmy0SMwTEpcyqgfUAICIACgfMpIk/49SLqQbUcni9TkPkb9gKsgAAIAyh9HRv14lBtgGgEQAFB+OLrRIyJa6r2UQ50BkwiAAIDyIT1FWjNcUoH5PjzKDXAIARAA4F45WdJ/HpUyNtrXr1Itqe9KRv0ABxAAAQDuk5EmJQ+QLp6xoxPHuwClRQAEALhHeoq0Zqh9fYLDpQff5HgXoJQIgAAA18pIk1YNkXJP2devUbzUbQ6jfkAZIAACAFwnPUVaM0ySYb4Px7sAZY4ACABwPkc3ejDqBzgFARAA4FwZadLKflLeWfN9LP5Sj/mM+gFOQgAEADhHTpb03lPSf9fZ1y84QnpwMRs9ACciAAIAyp6jz/FNmCXFDXNaWQAuIQACAMqWIxs9AkKl3ssY9QNchAAIACgbOVnS6hHSj5vt61f1eqnnQp7oAbgQARAAUHoObfTgOb6AuxAAAQCl48gTPSKipd5LGfUD3IQACABwjENP9GCjB1AeEAABAPZjowfg0QiAAADzHHqih0Wqeh0bPYByhAAIADDHkY0eARWk3ssZ9QPKGQIgAODq2OgBeBUCIACgZA6d7cdGD6C8IwACAIrn0JQvGz0AT0AABAAUtW2BtOEJ+/rwRA/AYxAAAQC/c+RsP57oAXgcP3cXUJaSkpJUv359hYSEKCYmRlu2bLli+9zcXI0fP17R0dEKDg5Ww4YNtWjRIhdVCwDlTHqKtPR++8JfRLQ0YhPhD/AwXjMCmJycrNGjRyspKUnt27fXa6+9poSEBO3du1d169Yttk+vXr30888/a+HChWrUqJF++eUX5eXlubhyAHAzR8/2Y6MH4LEshmHYcYx7+RUXF6dWrVpp3rx51mtNmzZV9+7dNX369CLt33//ffXu3Vs//PCDqlSp4tB7ZmdnKyIiQqdPn1Z4eLjDtQOA23C2H3wQ399eMgV84cIF7dy5U/Hx8TbX4+PjtXXr1mL7rFu3TrGxsZoxY4Zq1aql66+/Xk8++aTOnTvnipIBwP22LZCW9rAv/FVuIA3dSPgDPJxXTAEfO3ZM+fn5ioyMtLkeGRmprKysYvv88MMP+vTTTxUSEqK3335bx44d06hRo3TixIkS1wHm5uYqNzfX+ufs7Oyy+xAA4CqZ6VLyQOnUATs6MeULeBOvGAEsZLFYbP5sGEaRa4UKCgpksVi0bNkytW7dWvfee69mz56txYsXlzgKOH36dEVERFh/6tSpU+afAQCcKiNNer2zfeEvqJLUfw3hD/AiXhEAq1WrJn9//yKjfb/88kuRUcFCUVFRqlWrliIiIqzXmjZtKsMwdPjw4WL7jBs3TqdPn7b+HDp0qOw+BAA4W+GUb8F5830axUuP7mDKF/AyXhEAg4KCFBMTo9TUVJvrqampateuXbF92rdvr6NHj+rMmTPWa/v375efn59q165dbJ/g4GCFh4fb/ABAuZeZLs1ubt/BzpYA6f6FUv9VUqWazqsNgFt4RQCUpLFjx2rBggVatGiR9u3bpzFjxujgwYMaOXKkpEujdwMHDrS279u3r6pWraohQ4Zo7969+uSTT/TUU0/poYceUoUKFdz1MQCgbBVO+WbbMWNRuQFn+wFezis2gUhSYmKijh8/rqlTpyozM1PNmzfX+vXrFR0dLUnKzMzUwYMHre0rVqyo1NRUPfroo4qNjVXVqlXVq1cvTZs2zV0fAQDKTk6W9N5T0n/X2dGJjR6Ar/CacwDdgXOEAJRLmenSWz2lsz+b7xMYJiUuZa0ffALf3140AggA0KXHua0ZISnffJ96HaUH5rPWD/AhBEAA8AaOPM7N4i/1mM9aP8AHEQABwNMx5QvATgRAAPBk6SnSmuGSCsz3YcoX8HkEQADwRDlZ0uoR0o+bzfdhyhfA/xAAAcDTZKRJyQOki2eu3rZQUCWp1xKmfAFIIgACgGdJT5HWDLWvT6N4qdscpnwBWBEAAcATODTlGyD1eI0pXwBFEAABoLzLSJNW9pPyzprvU7Gm1G+VFHWj8+oC4LEIgABQnqWnSGuGSTL70CaL1OQ+qctMpnwBlIgACADlkUMHOzPlC8AcAiAAlDeZ6dKSHtK5Y+b7RERLvZcy5QvAFAIgAJQndh/sbJESZklxw5xZFQAvQwAEgPLAkSnfgApS7+Wc7QfAbgRAAHA3R57lW7mB1OtNpnwBOIQACADu5MizfO+YJHUc67SSAHg/AiAAuIMjBzsHhEq9lzHlC6DUCIAA4GqOPMs3qpXUdwVn+wEoEwRAAHCljDRpaaKkC+b78CxfAGWMAAgArrJ5trRpivn2HOwMwEkIgADgbJnp0rJE6cxR8314li8AJyIAAoAzpadIa0ZIyjfZgWf5AnA+AiAAOENOlvTeU9J/15nvw5QvABchAAJAWXPkYGee5QvAhQiAAFCWMtKk5X2kgvPm+3CwMwAXIwACQFnZtkDa8IT59v4hUp8VHOwMwOVcHgCrVKliV3uLxaJdu3YpOjraSRUBQCllpksr+krZh8z3qVRL6ruSKV8AbuHyAHjq1Cm9+OKLioiIuGpbwzA0atQo5eeb3T0HAC7myJRvvY7SA/PZ5QvAbdwyBdy7d2/VqFHDVNtHH33UydUAgIPsnfJlly+AcsLlAbCgoMCu9jk5OU6qBAAc5MiULwc7AyhH2AQCAPbITJfe7CadP2G+D1O+AMoZP3e++Ztvvqn33nvP+ue//vWvuuaaa9SuXTv99NNPbqwMAIqRniK9dpv58GcJkO5fKA1eR/gDUK64NQA+99xzqlChgiTp888/1yuvvKIZM2aoWrVqGjNmjDtLA4Df5WRJi7tKa4ZKMsz1qVBVGrGJ9X4AyiW3TgEfOnRIjRo1kiStXbtWPXv21IgRI9S+fXvdfvvt7iwNAC7JSJOSB0gXz5jvU7mB1OtN1vsBKLfcOgJYsWJFHT9+XJK0ceNGderUSZIUEhKic+fOubM0ALgU/pYmmg9/Fv9LU76P7yb8ASjX3DoC2LlzZw0bNkwtW7bU/v371aVLF0nSt99+q3r16rmzNAC+bvNsadMU8+0Dw6TEpTzVA4BHcOsI4Ny5c9W2bVv9+uuvWr16tapWrSpJ2rlzp/r06ePO0gD4qsx0aVZT+8Jf7TbSY7sIfwA8hsUwDJMrmsvO/Pnz1bVrV9Ws6dm74rKzsxUREaHTp08rPDzc3eUAKC27n+rhJyXMlOKGObUsAGWL7283jQCuWLFC9erVU1xcnJ577jnt3bvXHWUAwO/SU6SlPcyHv4BQqf9qwh8Aj+SWALhp0yZlZmbq0Ucf1Z49e9S2bVs1bNhQY8eO1ccff2z300IAwGE5WdKCu/93xItJFWtKQz9gyheAx3LLFPAfXbhwQWlpaVq3bp3eeecdnT17Vl26dFHXrl2VkJCgsLAwd5dYLIaQAQ+XkSat7CflnTXZwSI16ix1m8PBzoAH4/u7nATAP9qxY4fWrVun//znP+rZs6cmTJjg7pKKxT8gwIOlp9g36ucfIvVZwagf4AX4/i4HAfD06dOKiIgo8fcXL15UYGCgCysyj39AgIey94iXijWlfqs42w/wEnx/u2kN4IQJE1S3bl3NmjVLtWvX1sSJE0tsW17DHwAP5MgRL/U6Sg9vJvwB8CpuCYBffvmlDMNQv3799NNPP2nNmjXuKAOAL8lIk+Z3ks4cNdnB79JTPQavY70fAK/jlieBWCwW3XTTTYqKipIkVa9e3R1lAPAFOVnSe09J/11nvg9P9QDg5dwyAhgTE6N3331X0qUdwBcvXnRHGQC8XU6WtLy3feGvXkee6gHA67l9E4gk5eXlKSDArY8ldgiLSIFyzJEjXhJmcbAz4AP4/nbTFPAfBQQE6MyZM0UOgPbVvxQApbRtgbThCfPtOeIFgI9xyxRwoQMHDqhLly4KCwtTRESEKleurMqVK+uaa65R5cqV3VkaAE+1ebZ94a9SLWlYKuEPgE9x6whgv379JEmLFi1SZGSkLBaLO8sB4Mky06VliXbs8pV0xySp41jn1QQA5ZRbA2B6erp27typxo0bu7MMAJ4uPUVaM1ySyeeIW/ylHvOlG3s6tSwAKK/cOgV8yy236NChQ+4sAYCnsz7SzWT4CwyT+qUQ/gD4NLeOAC5YsEAjR47UkSNH1Lx58yJP/bjxRk7eB1CCnCwpeZB0+Avzfep1lB6Yz8HOAHyeWwPgr7/+qu+//15DhgyxXrNYLDIMQxaLRfn5+W6sDkC5lZEmregr5Z8z154pXwCw4dYA+NBDD6lly5ZasWIFm0AAmJORJi1NlHTBXHuOeAGAItwaAH/66SetW7dOjRo1cmcZADxBTpb0n0eljI3m+0RES72XSlEsJwGAy7k1AN5555366quvCIAAriwzXVrSQzp3zHwfjngBgBK5NQDed999GjNmjL7++mu1aNGiyCaQrl27uqkyAOUG6/0AoMy59VnAfn4ln0LjCZtAeJYg4GT2PtIttIY0YDVTvgCuiO9vN48A/vHZvwAgiSNeAMDJ3BoAAaCIzHTprZ7S2Z/N92G9HwDYxeVPAnn55Zd1/vx50+1fffVV5eTkOLEiAOVGRpo0v5P58OcfIvV/m/AHAHZy+RpAf39/ZWVlqXr16qbah4eHa8+ePWrQoIGTK7MfawiAMuLIES+Vakl9V7LeD4Dd+P52wxSwYRi66667FBBg7q3PnTO5809SUlKSZs6cqczMTN1www168cUX1aFDh6v2++yzz9SxY0c1b95ce/bsMf1+AMpATpa0vLeUudt8H9b7AUCpuDwATpo0ya723bp1U5UqVa7aLjk5WaNHj1ZSUpLat2+v1157TQkJCdq7d6/q1q1bYr/Tp09r4MCBuuuuu/Tzz3asOQJQehlp0sp+Ut5Zc+054gUAyoRbj4EpS3FxcWrVqpXmzZtnvda0aVN1795d06dPL7Ff7969dd1118nf319r1661awSQIWSgFNJTpDUPS8oz175CVWngWqZ8AZQa399u2ATiDBcuXNDOnTsVHx9vcz0+Pl5bt24tsd8bb7yh77//3u5RSQCltHm2tGaoTIe/SrUIfwBQhrziGJhjx44pPz9fkZGRNtcjIyOVlZVVbJ/vvvtOTz/9tLZs2WJ6PWJubq5yc3Otf87Ozna8aMAXZaZLyxKlM0fN9+GIFwAoc14xAljIYrHY/NkwjCLXJCk/P199+/bVlClTdP3115t+/enTpysiIsL6U6dOnVLXDPiMjDRpQbz58OcfzBEvAOAkXhEAq1WrZj1e5nK//PJLkVFBScrJydGOHTv0l7/8RQEBAQoICNDUqVP11VdfKSAgQGlpacW+z7hx43T69Gnrz6FDh5zyeQCvk54iLe1h/nm+gWFSn5VSozudWxcA+KhyMQV87NgxBQUFObwQMygoSDExMUpNTVWPHj2s11NTU9WtW7ci7cPDw/X111/bXEtKSlJaWppSUlJUv379Yt8nODhYwcHBDtUI+KyNU6Sts82354gXAHA6twXAU6dOafz48UpOTtbJkyclSdWrV9eQIUM0YcIEhYaG2vV6Y8eO1YABAxQbG6u2bdtq/vz5OnjwoEaOHCnp0ujdkSNHtGTJEvn5+al58+Y2/WvUqKGQkJAi1wE4yO71fn5SwkwpbphTywIAuCkAnjhxQm3bttWRI0fUr18/NW3aVIZhaN++fZozZ45SU1P16aef6quvvtK2bdv02GOPXfU1ExMTdfz4cU2dOlWZmZlq3ry51q9fr+joaElSZmamDh486OyPBkC6tN5vWW/JyL16W+nSI936rGDKFwBcxC3nAI4ePVofffSRPvzwwyJr9LKyshQfH6/GjRtr48aNevnllzVo0CBXl2gK5wgBxUhP+d8RLybxSDcALsb3t5tGANeuXavXXnut2A0aNWvW1IwZM3Tvvfdq0qRJ5Tb8AfiDnCwpeZB0+AvzfWq3kRLfZL0fALiYWwJg4bN6S9K8eXP5+flxQDPgKTLTpbd6SmdNPk6RR7oBgFu55RiYatWq6ccffyzx9wcOHFCNGjVcVxAAx2WkSfM7mQ9/IZWlER8T/gDAjdwSAO+55x6NHz9eFy5cKPK73NxcTZgwQffcc48bKgNgl82zL53vZ3azR6Va0qB1rPcDADdzyyaQw4cPKzY2VsHBwXrkkUfUpEkTSdLevXuVlJSk3Nxcbd++XXXr1nV1aXZhESl8Vk6WtHqE9ONm8314pBuAcoLvbzetAaxdu7Y+//xzjRo1SuPGjVNhBrVYLOrcubNeeeWVch/+AJ+VmS4t6SGdO2ayg590/+tM+QJAOeK2g6Dr16+vDRs26OTJk/ruu+8kSY0aNVKVKlXcVRKAq7H3fL/AMClxKef7AUA54/ZHwVWuXFmtW7d2dxkArmbzbGnTFPPtOeIFAMottwdAAOUc6/0AwOsQAAGUzN71fv7BUp+VTPkCQDlHAARQvPQUac0wSSYPCggIlXovI/wBgAcgAAIoatsCacMT5tuz3g8APAoBEMDvMtOlFX2l7EMmO1ikhFlS3DCnlgUAKFsEQACXZKZLi++Tck+Za28JkHq8xvl+AOCBCIAA/rfeb7ikAnPtQ2tIA1bzSDcA8FAEQMDXcb4fAPgcAiDgq3KypORB0uEvTHZgvR8AeAsCIOCL7D3fj/V+AOBVCICAr7H3eb6s9wMAr0MABHyJvef71esoPTCf9X4A4GUIgIAvsHu9n5+UMJP1fgDgpQiAgLfLTJfe6imd/dlce/8Qqc8KHukGAF6MAAh4M9b7AQCKQQAEvBXP8wUAlIAACHibnCxp9Qjpx83m+9wxSeo41nk1AQDKFQIg4E3sXe/nFyT1TWa9HwD4GAIg4C3sXe8XGCYlLiX8AYAPIgAC3oDn+QIA7EAABDwZ6/0AAA4gAAKeyt7n+foFSt1f5Xm+AAACIOCRWO8HACgFAiDgaexd71ejhTQghfV+AAArAiDgKexe72eRGnWWus0h/AEAbBAAAU+QkyW99YD0yzfm2vsHS31WMuULACgWARAo7zLSpOV9pILz5toHhEq9lxH+AAAlIgAC5Vl6irTmYUl55tpzvh8AwAQCIFBe2bXZwyIlzJLihjm1JACAdyAAAuVNTpaUPEg6/IW59pYAqcdrnO8HADCNAAiUJxlp0sp+Ut5Zc+1Da0gDVktRNzq3LgCAVyEAAuVFRpq0tJeki+baR7WS+q5gvR8AwG4EQKA8sPdwZ57nCwAoBQIg4E72rveTn5Qwk80eAIBSIQAC7pKZLi3pIZ07Zq69f4jUZwXn+wEASo0ACLhDRpq0rLdk5JprX7Gm1G8Vmz0AAGWCAAi4mr3r/TjcGQBQxgiAgKvkZEmrR0g/bjbZgfV+AADnIAACrsB6PwBAOUIABJzN3vV+HO4MAHAyAiDgTNsWSBueMN+e9X4AABcgAALOYPd6P4uUMIv1fgAAlyAAAmXN3vV+lgCpx2vSjT2dWxcAAP9DAATKUkaatLyPVHDeXHvW+wEA3IAACJQV1vsBADwEARAoCxunSFtnm2xske6YKHUc69SSAAAoCQEQKI2cLCl5kHT4C3PtWe8HACgHCICAozLTpTe7SedPmGsfUlkatI71fgAAtyMAAo6w93DniGip91LCHwCgXCAAAvbaPFvaNMVkY9b7AQDKHwIgYJa9hztb/KUe81nvBwAodwiAgBn2Hu7Mej8AQDlGAASuxt71fhVrSv1WEf4AAOUWARC4EnsPd243Voqf5Lx6AAAoAwRAoDj2rveTRUqYJcUNc2pZAACUBQIg8Ec5WdJbD0i/fGOuPYc7AwA8jJ+7CyhLSUlJql+/vkJCQhQTE6MtW7aU2HbNmjXq3LmzqlevrvDwcLVt21YffPCBC6tFuZSRJr14s/nwF1pDGrGJ8AcA8CheEwCTk5M1evRojR8/Xrt371aHDh2UkJCggwcPFtv+k08+UefOnbV+/Xrt3LlTd9xxh+677z7t3r3bxZWj3EhPkZY+KOWfM9e+dhvpz1vY7AEA8DgWwzAMdxdRFuLi4tSqVSvNmzfPeq1p06bq3r27pk+fbuo1brjhBiUmJmrixImm2mdnZysiIkKnT59WeHi4Q3WjnOBwZwDwGXx/e8kI4IULF7Rz507Fx8fbXI+Pj9fWrVtNvUZBQYFycnJUpUoVZ5SI8ionS1pwt/nwZwmQ7l9A+AMAeDSv2ARy7Ngx5efnKzIy0uZ6ZGSksrKyTL3GCy+8oN9++029evUqsU1ubq5yc38/Cy47O9uxglE+cLgzAMBHecUIYCGLxWLzZ8MwilwrzooVKzR58mQlJyerRo0aJbabPn26IiIirD916tQpdc1wk4w0aX4n8+GvUi3CHwDAa3hFAKxWrZr8/f2LjPb98ssvRUYF/yg5OVlDhw7Vv//9b3Xq1OmKbceNG6fTp09bfw4dOlTq2uEGm2dLS3uYf7LHHZOkJ/YS/gAAXsMrAmBQUJBiYmKUmppqcz01NVXt2rUrsd+KFSs0ePBgLV++XF26dLnq+wQHBys8PNzmBx4kJ0ta3NWOzR5+UsILrPcDAHgdr1gDKEljx47VgAEDFBsbq7Zt22r+/Pk6ePCgRo4cKenS6N2RI0e0ZMkSSZfC38CBA/XSSy+pTZs21tHDChUqKCIiwm2fA05i7+HO/iFSnxVSozudWxcAAG7gNQEwMTFRx48f19SpU5WZmanmzZtr/fr1io6OliRlZmbanAn42muvKS8vT4888ogeeeQR6/VBgwZp8eLFri4fzpSRJi3vIxWcN9c+tIY0YDVTvgAAr+U15wC6A+cIeYD0FGnNw5LyzLWv3UZKfFOqVNOpZQEA3Ifvby8aAQRs5GRJ/3lUythosoNFSpglxQ1zalkAAJQHBEB4H3vX+1kCpB6v8TxfAIDPIADCu2SkSSv7SXlnzbVnvR8AwAcRAOE97F3vF9VK6ruC9X4AAJ9DAIR32DzbjvP9dOlwZ873AwD4KAIgPFtOlpQ8SDr8hckOflLCTDZ7AAB8GgEQniszXXqrp3T2Z3PtOdwZAABJBEB4qow0aWmipAvm2lesKfVbxWYPAABEAIQn2rZA2vCE+fb1OkoPzGezBwAA/0MAhOfIyZJWj5B+3GyyA+v9AAAoDgEQnsHew51Z7wcAQIkIgCj/MtKkFX2l/HPm2rPeDwCAKyIAonzjcGcAAMocARDll72bPRJeYL0fAAAmEABR/uRkScv7SJm7zLX3C5S6vyrd2NO5dQEA4CUIgChf7D3cOSBU6r2MzR4AANiBAIjyIyNNWtZbMnLNta/RQhqQwno/AADs5OfuAgBJl9b7Le1hPvzV60j4AwDAQYwAwr043BkAAJcjAMJ97F3v5x8s9VnJej8AAEqJAAj3yEyXFt8n5Z4y157DnQEAKDMEQLievef71W4jJb7Jej8AAMoIARCutXm2tGmK+fZ3TJI6jnVePQAA+CACIFwjJ0tKHiQd/sJcew53BgDAaQiAcL7MdGlJD+ncMXPtA8OkxKVs9gAAwEkIgHCu9BRpzXBJBebac7gzAABOx0HQcJ5tC6Q1Q2U6/HG4MwAALsEIIJxj4xRp62xzbVnvBwCASxEAUbbsPtw5ROqzgvV+AAC4EAEQZcfew51Da0gDVnO4MwAALkYARNlIT5HWDJNkmGtfr6P0wHzW+wEA4AYEQJQehzsDAOBRCIBwXGa6tKKvlH3IXHvW+wEAUC4QAOEYe9f7VagqDVzLej8AAMoBAiDsZ+/hzrXbSIlvst4PAIByggAI+9i73q/dWCl+kvPqAQAAdiMAwjx7DneWn5QwU4ob5tSSAACA/QiAuDp7D3cOqCD1Xs5mDwAAyikCIK4sI01a3kcqOG+uPYc7AwBQ7hEAUbL0FGnNUPPtOdwZAACPQABE8VjvBwCA1yIAwlZmurQsUTpz1Fx7DncGAMDjEADxOw53BgDAJxAAcUlGmrSst2TkmmvPej8AADwWARAc7gwAgI8hAPqynCxp9Qjpx83m2lv8pR7zpRt7OrcuAADgVARAX5WZLi3pIZ07Zq59SGVp0DrW+wEA4AUIgL7I3sOdK9WS+q4k/AEA4CUIgL5m2wJpwxPm27PeDwAAr0MA9CV2He5ske6YKHUc69SSAACA6xEAfYG9hzsHVJB6L+dwZwAAvBQB0NvZu96PzR4AAHg9P3cXACdKT5GW9jAf/iKiCX8AAPgARgC9Fev9AABACQiA3iYnS0oeJB3+wlx7DncGAMDnEAC9SWa69GY36fwJc+1Z7wcAgE8iAHqLjDRpWW/JyDXXPiJa6r2U8AcAgA8iAHqDzbOlTVPMt+dwZwAAfBoB0JPlZEmrR0g/bjbXnvV+AABABEDPlZkuLekhnTtmrj3r/QAAwP8QAD2Rvev9KtaU+q0i/AEAAEkEQM+zbYG04Qnz7VnvBwAA/oAA6Ek2TpGxdbYsphpbpIRZUtwwJxcFAAA8DQHQE2SmS8sSZZw5ai78WQKkHq+x2QMAABTLq54FnJSUpPr16yskJEQxMTHasmXLFdtv3rxZMTExCgkJUYMGDfTqq6+6qFI7ZKRJr3eWzIa/0BrSiE2EPwAAUCKvCYDJyckaPXq0xo8fr927d6tDhw5KSEjQwYMHi21/4MAB3XvvverQoYN2796tv//973rssce0evVqF1d+BekpMpb2kArOm2tfu4305y1s9gAAAFdkMQzDcHcRZSEuLk6tWrXSvHnzrNeaNm2q7t27a/r06UXa/+1vf9O6deu0b98+67WRI0fqq6++0ueff27qPbOzsxUREaHTp08rPDy89B/ichunqGDrbJMJ3SLdMVHqOLZsawAAwAs59fvbQ3jFCOCFCxe0c+dOxcfH21yPj4/X1q1bi+3z+eefF2l/9913a8eOHbp48aLTajUlI015n79i7i/H4i/dv4DwBwAATPOKTSDHjh1Tfn6+IiMjba5HRkYqKyur2D5ZWVnFts/Ly9OxY8cUFRVVpE9ubq5yc38/ey87O7sMqv+D9BRdeHuUgowLV2/L4c4AAMABXjECWMhisd0mYRhGkWtXa1/c9ULTp09XRESE9adOnTqlrPgPti2Q3n5YQWYOeI6IJvwBAACHeEUArFatmvz9/YuM9v3yyy9FRvkK1axZs9j2AQEBqlq1arF9xo0bp9OnT1t/Dh06VDYfoNDGcZKRd/V27cZKY9IJfwAAwCFeEQCDgoIUExOj1NRUm+upqalq165dsX3atm1bpP3GjRsVGxurwMDAYvsEBwcrPDzc5qdMxU+Xmna79FOhmBBq8ZfuX8iTPQAAQKl4RQCUpLFjx2rBggVatGiR9u3bpzFjxujgwYMaOXKkpEujdwMHDrS2HzlypH766SeNHTtW+/bt06JFi7Rw4UI9+eST7voIl57akbjk0k+9W21/V6mWNOJjzvcDAACl5hWbQCQpMTFRx48f19SpU5WZmanmzZtr/fr1io6OliRlZmbanAlYv359rV+/XmPGjNHcuXN17bXX6uWXX9YDDzzgro9QAj+paiOp50KmfAEAQJnwmnMA3cGp5whtW3BpTWD8dJ7nCwBAGeIcQC8aAfQ6ccMIfgAAwCm8Zg0gAAAAzCEAAgAA+BgCIAAAgI8hAAIAAPgYAiAAAICPIQACAAD4GAIgAACAjyEAAgAA+BgCIAAAgI8hAAIAAPgYHgVXCoWPUc7OznZzJQAAwKzC7+3C73FfRAAshZycHElSnTp13FwJAACwV05OjiIiItxdhltYDF+Ov6VUUFCgo0ePqlKlSrJYLGX62tnZ2apTp44OHTqk8PDwMn1t/I777BrcZ9fgPrsG99k1nHmfDcNQTk6Orr32Wvn5+eZqOEYAS8HPz0+1a9d26nuEh4fzXzAuwH12De6za3CfXYP77BrOus++OvJXyDdjLwAAgA8jAAIAAPgYAmA5FRwcrEmTJik4ONjdpXg17rNrcJ9dg/vsGtxn1+A+OxebQAAAAHwMI4AAAAA+hgAIAADgYwiAAAAAPoYACAAA4GMIgG6SlJSk+vXrKyQkRDExMdqyZcsV22/evFkxMTEKCQlRgwYN9Oqrr7qoUs9nz71es2aNOnfurOrVqys8PFxt27bVBx984MJqPZe9/6YLffbZZwoICNDNN9/s3AK9hL33OTc3V+PHj1d0dLSCg4PVsGFDLVq0yEXVei577/OyZct00003KTQ0VFFRURoyZIiOHz/uomo90yeffKL77rtP1157rSwWi9auXXvVPnwXliEDLrdy5UojMDDQeP311429e/cajz/+uBEWFmb89NNPxbb/4YcfjNDQUOPxxx839u7da7z++utGYGCgkZKS4uLKPY+99/rxxx83/vnPfxpffvmlsX//fmPcuHFGYGCgsWvXLhdX7lnsvc+FTp06ZTRo0MCIj483brrpJtcU68Ecuc9du3Y14uLijNTUVOPAgQPGtm3bjM8++8yFVXsee+/zli1bDD8/P+Oll14yfvjhB2PLli3GDTfcYHTv3t3FlXuW9evXG+PHjzdWr15tSDLefvvtK7bnu7BsEQDdoHXr1sbIkSNtrjVp0sR4+umni23/17/+1WjSpInNtYcfftho06aN02r0Fvbe6+I0a9bMmDJlSlmX5lUcvc+JiYnGM888Y0yaNIkAaIK993nDhg1GRESEcfz4cVeU5zXsvc8zZ840GjRoYHPt5ZdfNmrXru20Gr2NmQDId2HZYgrYxS5cuKCdO3cqPj7e5np8fLy2bt1abJ/PP/+8SPu7775bO3bs0MWLF51Wq6dz5F7/UUFBgXJyclSlShVnlOgVHL3Pb7zxhr7//ntNmjTJ2SV6BUfu87p16xQbG6sZM2aoVq1auv766/Xkk0/q3LlzrijZIzlyn9u1a6fDhw9r/fr1MgxDP//8s1JSUtSlSxdXlOwz+C4sWwHuLsDXHDt2TPn5+YqMjLS5HhkZqaysrGL7ZGVlFds+Ly9Px44dU1RUlNPq9WSO3Os/euGFF/Tbb7+pV69ezijRKzhyn7/77js9/fTT2rJliwIC+K8hMxy5zz/88IM+/fRThYSE6O2339axY8c0atQonThxgnWAJXDkPrdr107Lli1TYmKizp8/r7y8PHXt2lVz5sxxRck+g+/CssUIoJtYLBabPxuGUeTa1doXdx1F2XuvC61YsUKTJ09WcnKyatSo4azyvIbZ+5yfn6++fftqypQpuv76611Vntew599zQUGBLBaLli1bptatW+vee+/V7NmztXjxYkYBr8Ke+7x371499thjmjhxonbu3Kn3339fBw4c0MiRI11Rqk/hu7Ds8D+9XaxatWry9/cv8r8kf/nllyL/y6ZQzZo1i20fEBCgqlWrOq1WT+fIvS6UnJysoUOHatWqVerUqZMzy/R49t7nnJwc7dixQ7t379Zf/vIXSZeCimEYCggI0MaNG3XnnXe6pHZP4si/56ioKNWqVUsRERHWa02bNpVhGDp8+LCuu+46p9bsiRy5z9OnT1f79u311FNPSZJuvPFGhYWFqUOHDpo2bRojU2WE78KyxQigiwUFBSkmJkapqak211NTU9WuXbti+7Rt27ZI+40bNyo2NlaBgYFOq9XTOXKvpUsjf4MHD9by5ctZw2OCvfc5PDxcX3/9tfbs2WP9GTlypBo3bqw9e/YoLi7OVaV7FEf+Pbdv315Hjx7VmTNnrNf2798vPz8/1a5d26n1eipH7vPZs2fl52f7derv7y/p9xEqlB7fhWXMTZtPfFrhEQMLFy409u7da4wePdoICwszfvzxR8MwDOPpp582BgwYYG1fuPV9zJgxxt69e42FCxey9d0ke+/18uXLjYCAAGPu3LlGZmam9efUqVPu+ggewd77/EfsAjbH3vuck5Nj1K5d2+jZs6fx7bffGps3bzauu+46Y9iwYe76CB7B3vv8xhtvGAEBAUZSUpLx/fffG59++qkRGxtrtG7d2l0fwSPk5OQYu3fvNnbv3m1IMmbPnm3s3r3betwO34XORQB0k7lz5xrR0dFGUFCQ0apVK2Pz5s3W3w0aNMjo2LGjTfuPP/7YaNmypREUFGTUq1fPmDdvnosr9lz23OuOHTsakor8DBo0yPWFexh7/01fjgBonr33ed++fUanTp2MChUqGLVr1zbGjh1rnD171sVVex577/PLL79sNGvWzKhQoYIRFRVl9OvXzzh8+LCLq/YsmzZtuuJ/3/Jd6FwWw2B8GgAAwJewBhAAAMDHEAABAAB8DAEQAADAxxAAAQAAfAwBEAAAwMcQAAEAAHwMARAAAMDHEAABAAB8DAEQAEy6/fbbZbFYZLFYtGfPnhLbffzxx9Z23bt3d1l9AGAWARCA1xk8eLCp4HX48GEFBQWpSZMmpl97+PDhyszMVPPmzUts065dO2VmZqpXr16mXxcAXIkACMBnLV68WL169dLZs2f12WefmeoTGhqqmjVrKiAgoMQ2QUFBqlmzpipUqFBWpQJAmSIAAvBJhmHojTfe0IABA9S3b18tXLjQ3SUBgMsQAAH4pE2bNuns2bPq1KmTBgwYoH//+9/Kyclxd1kA4BIEQAA+aeHCherdu7f8/f11ww03qFGjRkpOTnZ3WQDgEgRAAD7n1KlTWrNmjfr372+91r9/fy1atMju17rhhhtUsWJFVaxYUQkJCWVZJgA4TcmrmAHASy1fvlznz59XXFyc9ZphGCooKNDevXvVrFkz06+1fv16Xbx4UZLY9AHAYxAAAfichQsX6oknntDgwYNtrj/22GNatGiRZs2aZfq1oqOjy7g6AHA+AiAAr3T69OkihzVXqVJFJ06c0K5du7Rs2bIi5//16dNH48eP1/Tp0xUYGOjCagHAtQiAALzSxx9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\n", + " " + ], + "text/plain": [ + "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p_test,_ = pressure_conversion(11.4,'bar','Pa')\n", + "p_index = int(np.argmin(abs(p_ext-p_test)))\n", + "p_test2 = p_ext[p_index]\n", + "\n", + "fig = plt.figure()\n", + "plt.plot(LA_int,Qs_ext[:,p_index])\n", + "plt.plot(LA_int,Q_formel(p_test2,LA_int),'+')\n", + "plt.xlabel('LA [-]')\n", + "plt.ylabel('Q [m³/s]')\n", + "plt.title('P = '+ str(p_test2) + ' [Pa]')" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Q [m³/s]')" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5b7343c1b0f244e8a40bcb8c91920c68", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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"pygments_lexer": "ipython3", + "version": "3.8.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Turbinen/old/messy.ipynb b/Turbinen/old/messy.ipynb new file mode 100644 index 0000000..b7cc1dc --- /dev/null +++ b/Turbinen/old/messy.ipynb @@ -0,0 +1,278 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "from numpy.polynomial import Polynomial\n", + "from numpy.polynomial.polynomial import polyval\n", + "from Turbinen_class_file import Francis_turbine_class\n", + "from mpl_toolkits import mplot3d\n", + "import matplotlib.pyplot as plt\n", + "\n", + "#importing pressure conversion function\n", + "import sys\n", + "import os\n", + "current = os.path.dirname(os.path.realpath('messy.ipynb'))\n", + "parent = os.path.dirname(current)\n", + "sys.path.append(parent)\n", + "from functions.pressure_conversion import pressure_conversion\n", + "%matplotlib widget" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": {}, + "outputs": [], + "source": [ + "T1 = Francis_turbine_class()\n", + "csv = T1.raw_csv\n", + "\n", + "T1.extract_csv()\n", + "ps = T1.raw_ps_vec\n", + "LAs = T1.raw_LA_vec\n", + "Qs = T1.raw_Qs_mat\n", + "\n", + "Q_fun = T1.get_Q_fun()" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": {}, + "outputs": [], + "source": [ + "p_min = np.min(ps)\n", + "p_max = np.max(ps)\n", + "\n", + "n_p = 100\n", + "n_LA = 200\n", + "\n", + "ps_vec = np.linspace(p_min,p_max,n_p)\n", + "ind1 = np.argmin(np.abs(ps_vec-np.min(ps)))\n", + "ind2 = np.argmin(np.abs(ps_vec-np.max(ps)))\n", + "LA_vec = np.linspace(0,1,n_LA)\n", + "\n", + "Q_int = np.reshape(Q_fun(ps_vec,LA_vec),[n_LA,n_p])" + ] + }, + { + "cell_type": "code", + "execution_count": 90, + "metadata": {}, + "outputs": [], + "source": [ + "poly_deg = 7\n", + "n_LAs = np.size(LAs)\n", + "fit_coeff_mat = np.empty([n_LAs,poly_deg+1])\n", + "fit_weights = np.ones_like(ps)\n", + "fit_weights[0] = 1000\n", + "\n", + "for i in range(n_LAs):\n", + " x = ps\n", + " y = Qs[i,:]\n", + " fit_coeff_mat[i,:] = np.polynomial.polynomial.Polynomial.fit(x,y,poly_deg,w=fit_weights).convert().coef\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [], + "source": [ + "Qs_poly = np.empty([n_LAs,n_p])\n", + "for i in range(n_LAs):\n", + " Qs_poly[i,:] = polyval(ps_vec,fit_coeff_mat[i,:])" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 92, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "667375b5fbc34d41b8d8238cc1cbfa8d", + "version_major": 2, + "version_minor": 0 + }, + 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", 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", 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", + "text/html": [ + "\n", + "
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+ "pygments_lexer": "ipython3", + "version": "3.8.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Untertweng.ipynb b/Untertweng.ipynb index 1570767..5518dec 100644 --- a/Untertweng.ipynb +++ b/Untertweng.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 56, + "execution_count": 46, "metadata": {}, "outputs": [], "source": [ @@ -11,41 +11,46 @@ "\n", "from functions.pressure_conversion import pressure_conversion\n", "from Ausgleichsbecken.Ausgleichsbecken_class_file import Ausgleichsbecken_class\n", - "from Druckrohrleitung.Druckrohrleitung_class_file import Druckrohrleitung_class" + "from Druckrohrleitung.Druckrohrleitung_class_file import Druckrohrleitung_class\n", + "from Turbinen.Turbinen_class_file import Francis_Turbine" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "#define constants\n", "\n", + "#Turbine\n", + "Q_nenn = 0.85\n", + "p_nenn,_ = pressure_conversion(10.6,'bar','Pa')\n", + "\n", "# physics\n", "g = 9.81 # gravitational acceleration [m/s²]\n", "rho = 1000. # density of water [kg/m³]\n", "\n", "# pipeline\n", - "L = 1000. # length of pipeline [m]\n", - "D = 1. # pipe diameter [m]\n", + "L = 535.+478. # length of pipeline [m]\n", + "D = 0.9 # pipe diameter [m]\n", "A_pipe = D**2/4*np.pi # pipeline area\n", - "h_pipe = 200 # hydraulic head without reservoir [m] \n", + "h_pipe = 105 # hydraulic head without reservoir [m] \n", "alpha = np.arcsin(h_pipe/L) # Höhenwinkel der Druckrohrleitung \n", "n = 50 # number of pipe segments in discretization\n", "# consider replacing Q0 with a vector be be more flexible in initial conditions\n", - "Q0 = 2. # initial flow in whole pipe [m³/s]\n", + "Q0 = Q_nenn # initial flow in whole pipe [m³/s]\n", "v0 = Q0/A_pipe # initial flow velocity [m/s]\n", - "f_D = 0.01 # Darcy friction factor\n", - "c = 400. # propagation velocity of the pressure wave [m/s]\n", + "f_D = 0.014 # Darcy friction factor\n", + "c = 500. # propagation velocity of the pressure wave [m/s]\n", "# consider prescribing a total simulation time and deducting the number of timesteps from that\n", - "nt = 500 # number of time steps after initial conditions\n", + "nt = 2000 # number of time steps after initial conditions\n", "\n", "# derivatives of the pipeline constants\n", "dx = L/n # length of each pipe segment\n", "dt = dx/c # timestep according to method of characterisitics\n", "nn = n+1 # number of nodes\n", - "initial_level = 20. # water level in upstream reservoir [m]\n", + "initial_level = 8. # water level in upstream reservoir [m]\n", "p0 = rho*g*initial_level-v0**2*rho/2\n", "pl_vec = np.arange(0,nn*dx,dx) # pl = pipe-length. position of the nodes on the pipeline\n", "t_vec = np.arange(0,nt+1)*dt # time vector\n", @@ -61,7 +66,7 @@ "initial_pipeline_pressure = p0 # Initial condition for the static pipeline pressure at the reservoir (= hydrostatic pressure - dynamic pressure) \n", "initial_pressure_unit = 'Pa' # DO NOT CHANGE! for pressure conversion in print statements and plot labels \n", "conversion_pressure_unit = 'bar' # for pressure conversion in print statements and plot labels\n", - "area_base = 20. # total base are of the cuboid reservoir [m²] \n", + "area_base = 74. # total base are of the cuboid reservoir [m²] \n", "area_outflux = A_pipe # outlfux area of the reservoir, given by pipeline area [m²]\n", "critical_level_low = 0. # for yet-to-be-implemented warnings[m]\n", "critical_level_high = np.inf # for yet-to-be-implemented warnings[m]\n", @@ -69,6 +74,7 @@ "# make sure e-RK4 method of reservoir has a small enough timestep to avoid runaway numerical error\n", "nt_eRK4 = 1000 # number of simulation steps of reservoir in between timesteps of pipeline \n", "simulation_timestep = dt/nt_eRK4\n", + "\n", "\n" ] }, @@ -91,7 +97,7 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 48, "metadata": {}, "outputs": [], "source": [ @@ -109,6 +115,11 @@ "pipe.set_initial_pressure(p_init,initial_pressure_unit,conversion_pressure_unit)\n", "pipe.set_initial_flow_velocity(v_init)\n", "\n", + "\n", + "T1 = Francis_Turbine(Q_nenn,p_nenn)\n", + "T1.set_LA(1.)\n", + "T1.set_closing_time(30)\n", + "\n", "# display the attributes of the created reservoir and pipeline object\n", "# V.get_info(full=True)\n", "# pipe.get_info()" @@ -116,7 +127,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 49, "metadata": {}, "outputs": [], "source": [ @@ -139,21 +150,21 @@ "level_vec = np.full(nt+1,initial_level) # level at the end of each pipeline timestep\n", "level_vec_2 = np.empty([nt_eRK4]) # level throughout each reservoir timestep-used for plotting and overwritten afterwards\n", "\n", - "# set the boudary conditions for the first timestep\n", + "# set the boundary conditions for the first timestep\n", "v_boundary_res[0] = v_old[0]\n", "v_boundary_tur[0] = v_old[-1] \n", - "v_boundary_tur[1:] = 0 # instantaneous closing\n", - "# v_boundary_tur[0:20] = np.linspace(v_old[-1],0,20) # overwrite for finite closing time - linear case\n", - "# const = int(np.min([100,round(nt/1.1)]))\n", - "# v_boundary_tur[0:const] = v_old[1]*np.cos(t_vec[0:const]*2*np.pi/5)**2\n", - "p_boundary_res[0] = p_old[0]\n", - "p_boundary_tur[0] = p_old[-1]\n", + "p_boundary_res[0] = p_old[0]\n", + "p_boundary_tur[0] = p_old[-1]\n", + "\n", + "LA_soll_vec = np.zeros_like(t_vec)\n", + "LA_soll_vec[0] = 1\n", + "LA_soll_vec[1000:] = 1\n", "\n" ] }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 50, "metadata": {}, "outputs": [], "source": [ @@ -186,7 +197,7 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 51, "metadata": {}, "outputs": [], "source": [ @@ -213,6 +224,9 @@ " v_boundary_res[it_pipe] = v_old[1]+1/(rho*c)*(p_boundary_res[it_pipe]-p_old[1])-f_D*dt/(2*D)*abs(v_old[1])*v_old[1] \\\n", " +dt*g*np.sin(alpha)\n", "\n", + " T1.change_LA(LA_soll_vec[it_pipe],dt)\n", + " v_boundary_tur[it_pipe] = 1/A_pipe*T1.get_Q(p_old[-1])\n", + "\n", " # the the boundary conditions in the pipe.object and thereby calculate boundary pressure at turbine\n", " pipe.set_boundary_conditions_next_timestep(v_boundary_res[it_pipe],p_boundary_res[it_pipe],v_boundary_tur[it_pipe])\n", " p_boundary_tur[it_pipe] = pipe.p_boundary_tur\n", @@ -245,7 +259,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 52, "metadata": {}, "outputs": [], "source": [ diff --git a/untertweng.txt b/untertweng.txt index 7e77ac8..1c6adb3 100644 --- a/untertweng.txt +++ b/untertweng.txt @@ -18,3 +18,5 @@ c = 500 m/s Q_0 = 100%*0.75+30%*0.75 Q_extrem = 30%*0.75 + +Q = LA*Q_nenn*sqrt(H/H_n) From 2331c7cc5b53ec00fd4dce11e42298efc81186d7 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Georg=20=C2=B4Brantegger?= Date: Thu, 21 Jul 2022 15:27:15 +0200 Subject: [PATCH 09/12] added set steady state functionality --- Ausgleichsbecken/Ausgleichsbecken_class_file.py | 13 +++++++++++++ Druckrohrleitung/Druckrohrleitung_class_file.py | 16 ++++++++++++---- Turbinen/Turbinen_class_file.py | 8 +++++++- 3 files changed, 32 insertions(+), 5 deletions(-) diff --git a/Ausgleichsbecken/Ausgleichsbecken_class_file.py b/Ausgleichsbecken/Ausgleichsbecken_class_file.py index 85f0c94..b00d957 100644 --- a/Ausgleichsbecken/Ausgleichsbecken_class_file.py +++ b/Ausgleichsbecken/Ausgleichsbecken_class_file.py @@ -33,6 +33,9 @@ class Ausgleichsbecken_class: velocity_unit_print = 'm/s' volume_unit_print = 'm³' + g = 9.81 # m/s² + rho = 1000 # kg/m³ + # init def __init__(self,area,outflux_area,level_min = 0,level_max = np.inf ,timestep = 1): self.area = area # base area of the rectangular structure @@ -67,6 +70,16 @@ class Ausgleichsbecken_class: self.pressure = pressure self.pressure_unit = pressure_unit self.pressure_unit_print = display_pressure_unit + + def set_steady_state(self,ss_influx,ss_level,pressure_unit,display_pressure_unit): + ss_outflux = ss_influx + ss_outflux_vel = ss_outflux/self.area_outflux + ss_pressure = self.rho*self.g*ss_level-ss_outflux_vel**2*self.rho/2 + + self.set_initial_level(ss_level) + self.set_influx(ss_influx) + self.set_outflux(ss_outflux) + self.set_pressure(ss_pressure,pressure_unit,display_pressure_unit) # getter def get_info(self, full = False): new_line = '\n' diff --git a/Druckrohrleitung/Druckrohrleitung_class_file.py b/Druckrohrleitung/Druckrohrleitung_class_file.py index c6dd4bf..fd5a15f 100644 --- a/Druckrohrleitung/Druckrohrleitung_class_file.py +++ b/Druckrohrleitung/Druckrohrleitung_class_file.py @@ -41,7 +41,7 @@ class Druckrohrleitung_class: self.n_seg = number_segments self.angle = pipeline_angle self.f_D = Darcy_friction_factor # = Rohrreibungszahl oder flow coefficient - self.density = 1000 + self.rho = rho self.g = g self.dx = total_length/number_segments @@ -89,8 +89,8 @@ class Druckrohrleitung_class: self.v_old = self.v0.copy() self.v = np.empty_like(self.v_old) - def set_boundary_conditions_next_timestep(self,v_reservoir,p_reservoir,v_turbine,input_unit_pressure = 'Pa'): - rho = self.density + def set_boundary_conditions_next_timestep(self,v_reservoir,p_reservoir,v_turbine): + rho = self.rho c = self.c f_D = self.f_D dt = self.dt @@ -108,6 +108,14 @@ class Druckrohrleitung_class: self.p[0] = self.p_boundary_res.copy() self.p[-1] = self.p_boundary_tur.copy() + + def set_steady_state(self,ss_flux,ss_level_reservoir,pl_vec,h_vec,pressure_unit,display_pressure_unit): + ss_v0 = np.full(self.n_seg+1,ss_flux/(self.dia**2/4*np.pi)) + ss_pressure = (self.rho*self.g*(ss_level_reservoir+h_vec)-ss_v0**2*self.rho/2)-(self.f_D*pl_vec/self.dia*self.rho/2*ss_v0**2) + + self.set_initial_flow_velocity(ss_v0) + self.set_initial_pressure(ss_pressure,pressure_unit,display_pressure_unit) + # getter def get_info(self): new_line = '\n' @@ -150,7 +158,7 @@ class Druckrohrleitung_class: def timestep_characteristic_method(self): #number of nodes nn = self.n_seg+1 - rho = self.density + rho = self.rho c = self.c f_D = self.f_D dt = self.dt diff --git a/Turbinen/Turbinen_class_file.py b/Turbinen/Turbinen_class_file.py index 8290976..fa7e832 100644 --- a/Turbinen/Turbinen_class_file.py +++ b/Turbinen/Turbinen_class_file.py @@ -31,4 +31,10 @@ class Francis_Turbine: LA_diff_max = self.d_LA_max_dt*timestep if abs(LA_diff) > LA_diff_max: LA_diff = np.sign(LA_diff)*LA_diff_max - self.LA = self.LA-LA_diff \ No newline at end of file + self.LA = self.LA-LA_diff + + def set_steady_state(self,ss_flux,ss_pressure): + ss_LA = self.LA_n*ss_flux/self.Q_n*np.sqrt(self.p_n/ss_pressure) + self.set_LA(ss_LA) + if ss_LA < 0 or ss_LA > 1: + print('LA out of range') From 5835c05af99f8564a75eef416f6228d0c7343a2b Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Georg=20=C2=B4Brantegger?= Date: Thu, 21 Jul 2022 15:27:36 +0200 Subject: [PATCH 10/12] first attempt at involving a Pegelregler in the system --- ...gram.ipynb => Ausgleichsbecken_test.ipynb} | 45 +++---- Pegelregler_test.ipynb | 14 +-- Regler/Regler_class_file.py | 20 ++-- Untertweng.ipynb | 68 +++++------ ....ipynb => Untertweng_mit_Pegelregler.ipynb | 110 ++++++++++-------- 5 files changed, 137 insertions(+), 120 deletions(-) rename Ausgleichsbecken/{Main_Program.ipynb => Ausgleichsbecken_test.ipynb} (77%) rename Main_Programm.ipynb => Untertweng_mit_Pegelregler.ipynb (79%) diff --git a/Ausgleichsbecken/Main_Program.ipynb b/Ausgleichsbecken/Ausgleichsbecken_test.ipynb similarity index 77% rename from Ausgleichsbecken/Main_Program.ipynb rename to Ausgleichsbecken/Ausgleichsbecken_test.ipynb index 9ca27cd..eea80ce 100644 --- a/Ausgleichsbecken/Main_Program.ipynb +++ b/Ausgleichsbecken/Ausgleichsbecken_test.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 38, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -21,15 +21,15 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "# define constants\n", - "initial_level = 5. # m\n", - "initial_influx = 0. # m³/s\n", - "initial_outflux = 0. # m³/s\n", - "initial_pipeline_pressure = 5.\n", + "initial_level = 10. # m\n", + "initial_influx = 5. # m³/s\n", + "initial_outflux = 1. # m³/s\n", + "initial_pipeline_pressure = 10.\n", "initial_pressure_unit = 'mWS'\n", "conversion_pressure_unit = 'mWS'\n", "\n", @@ -41,32 +41,33 @@ "\n", "# for while loop\n", "total_min_level = 0.01 # m\n", - "total_max_time = 300 # s" + "total_max_time = 1000 # s" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ "%matplotlib qt\n", "\n", "V = Ausgleichsbecken_class(area_base, area_outflux, critical_level_low, critical_level_high,simulation_timestep)\n", - "V.set_initial_level(initial_level) \n", - "V.set_influx(initial_influx)\n", - "V.set_outflux(initial_outflux)\n", - "\n", - "converted_pressure,_ = pressure_conversion(initial_pipeline_pressure,input_unit = initial_pressure_unit, target_unit = 'Pa')\n", - "V.pressure = converted_pressure\n", + "# V.set_initial_level(initial_level) \n", + "# V.set_influx(initial_influx)\n", + "# V.set_outflux(initial_outflux)\n", + "# converted_pressure,_ = pressure_conversion(initial_pipeline_pressure,input_unit = initial_pressure_unit, target_unit = 'Pa')\n", + "# V.pressure = converted_pressure\n", + "V.set_steady_state(initial_influx,initial_level,initial_pressure_unit,conversion_pressure_unit)\n", "\n", "time_vec = np.arange(0,total_max_time,simulation_timestep)\n", "outflux_vec = np.empty_like(time_vec)\n", - "outflux_vec[0] = initial_outflux\n", + "outflux_vec[0] = V.outflux\n", "level_vec = np.empty_like(time_vec)\n", - "level_vec[0] = initial_level\n", + "level_vec[0] = V.level\n", "\n", - "pressure_vec = np.full_like(time_vec,converted_pressure)*((np.sin(time_vec)+1)*np.exp(-time_vec/50))\n", + "# pressure_vec = np.full_like(time_vec,converted_pressure)*((np.sin(time_vec)+1)*np.exp(-time_vec/50))\n", + "pressure_vec = np.full_like(time_vec,V.pressure)\n", " \n", "i_max = -1\n", "\n", @@ -86,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -112,9 +113,9 @@ "\n", "# plt.subplots_adjust(left=0.2, bottom=0.2)\n", "ax4.set_axis_off()\n", - "cell_text = np.array([[initial_level, V.level_unit], \\\n", + "cell_text = np.array([[level_vec[0], V.level_unit], \\\n", " [initial_influx, V.flux_unit], \\\n", - " [initial_outflux, V.flux_unit], \\\n", + " [outflux_vec[0], V.flux_unit], \\\n", " [simulation_timestep, V.time_unit], \\\n", " [area_base, V.area_unit], \\\n", " [area_outflux, V.area_unit]])\n", @@ -140,7 +141,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.13 ('Georg_DT_Slot3')", + "display_name": "Python 3.8.13 ('DT_Slot_3')", "language": "python", "name": "python3" }, @@ -159,7 +160,7 @@ "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" + "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48" } } }, diff --git a/Pegelregler_test.ipynb b/Pegelregler_test.ipynb index d69ccf4..dd20068 100644 --- a/Pegelregler_test.ipynb +++ b/Pegelregler_test.ipynb @@ -47,8 +47,8 @@ "source": [ "# define controller constants\n", "target_level = 4.5 # m\n", - "Kp = 0.15\n", - "Ti = 20\n", + "Kp = 0.01\n", + "Ti = 120\n", "\n", "deadband_range = 0.1 # m\n", "deadband_lo = target_level-deadband_range\n", @@ -171,18 +171,18 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "b67e1ad42a2e4b9f932b947588eac3e9", + "model_id": "d0ac8027f1fd435da07e6b46adf3a810", "version_major": 2, "version_minor": 0 }, - "image/png": 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", + "image/png": 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", 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\n", " " ], @@ -246,7 +246,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.13 ('Georg_DT_Slot3')", + "display_name": "Python 3.8.13 ('DT_Slot_3')", "language": "python", "name": "python3" }, @@ -265,7 +265,7 @@ "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" + "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48" } } }, diff --git a/Regler/Regler_class_file.py b/Regler/Regler_class_file.py index 7d5cca0..8e5b469 100644 --- a/Regler/Regler_class_file.py +++ b/Regler/Regler_class_file.py @@ -74,15 +74,15 @@ class P_controller_class: class PI_controller_class: - def __init__(self,setpoint,proportionality_constant,Ti, timestep): + def __init__(self,setpoint,deadband,proportionality_constant,Ti, timestep): self.SP = setpoint + self.db = deadband self.Kp = proportionality_constant self.Ti = Ti self.dt = timestep self.error_history = [0] - self.control_variable = 0.0 - self.cv_lower_limit = -1 # default + self.cv_lower_limit = 0 # default self.cv_upper_limit = +1 # default @@ -91,12 +91,12 @@ class PI_controller_class: self.cv_upper_limit = upper_limit def calculate_error(self,process_variable): - self.error = self.SP-process_variable + self.error = process_variable-self.SP self.error_history.append(self.error) - def get_control_variable(self): - # if np.isclose(self.error,0,atol = 0.1): - # self.control_variable = 0 + def get_control_variable(self,process_variable): + + self.calculate_error(process_variable) cv = self.control_variable Kp = self.Kp @@ -105,7 +105,11 @@ class PI_controller_class: e0 = self.error_history[-1] e1 = self.error_history[-2] - new_control = cv+Kp*(e0-e1)+dt/Ti*e0 + if abs(self.error) > self.db: + new_control = cv+Kp*(e0-e1)+dt/Ti*e0 + else: + new_control = cv + if new_control < self.cv_lower_limit: new_control = self.cv_lower_limit diff --git a/Untertweng.ipynb b/Untertweng.ipynb index 5518dec..cd4de2e 100644 --- a/Untertweng.ipynb +++ b/Untertweng.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 46, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -17,7 +17,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -32,38 +32,38 @@ "rho = 1000. # density of water [kg/m³]\n", "\n", "# pipeline\n", - "L = 535.+478. # length of pipeline [m]\n", - "D = 0.9 # pipe diameter [m]\n", + "L = 535.+478. # length of pipeline [m]\n", + "D = 0.9 # pipe diameter [m]\n", "A_pipe = D**2/4*np.pi # pipeline area\n", "h_pipe = 105 # hydraulic head without reservoir [m] \n", "alpha = np.arcsin(h_pipe/L) # Höhenwinkel der Druckrohrleitung \n", - "n = 50 # number of pipe segments in discretization\n", + "n = 50 # number of pipe segments in discretization\n", "# consider replacing Q0 with a vector be be more flexible in initial conditions\n", - "Q0 = Q_nenn # initial flow in whole pipe [m³/s]\n", - "v0 = Q0/A_pipe # initial flow velocity [m/s]\n", + "# Q0 = Q_nenn # initial flow in whole pipe [m³/s]\n", + "# v0 = Q0/A_pipe # initial flow velocity [m/s]\n", "f_D = 0.014 # Darcy friction factor\n", "c = 500. # propagation velocity of the pressure wave [m/s]\n", "# consider prescribing a total simulation time and deducting the number of timesteps from that\n", - "nt = 2000 # number of time steps after initial conditions\n", + "nt = 3000 # number of time steps after initial conditions\n", "\n", "# derivatives of the pipeline constants\n", "dx = L/n # length of each pipe segment\n", "dt = dx/c # timestep according to method of characterisitics\n", "nn = n+1 # number of nodes\n", - "initial_level = 8. # water level in upstream reservoir [m]\n", - "p0 = rho*g*initial_level-v0**2*rho/2\n", + "initial_level = 8. # water level in upstream reservoir [m]\n", + "# p0 = rho*g*initial_level-v0**2*rho/2\n", "pl_vec = np.arange(0,nn*dx,dx) # pl = pipe-length. position of the nodes on the pipeline\n", "t_vec = np.arange(0,nt+1)*dt # time vector\n", "h_vec = np.arange(0,n+1)*h_pipe/n # hydraulic head of pipeline at each node \n", - "v_init = np.full(nn,Q0/(D**2/4*np.pi)) # initial velocity distribution in pipeline\n", - "p_init = (rho*g*(initial_level+h_vec)-v_init**2*rho/2)-(f_D*pl_vec/D*rho/2*v_init**2) # ref Wikipedia: Darcy Weisbach\n", + "# v_init = np.full(nn,Q0/(D**2/4*np.pi)) # initial velocity distribution in pipeline\n", + "# p_init = (rho*g*(initial_level+h_vec)-v_init**2*rho/2)-(f_D*pl_vec/D*rho/2*v_init**2) # ref Wikipedia: Darcy Weisbach\n", "\n", "\n", "# reservoir\n", "# replace influx by vector\n", - "initial_influx = 0. # initial influx of volume to the reservoir [m³/s]\n", - "initial_outflux = Q0 # initial outflux of volume from the reservoir to the pipeline [m³/s]\n", - "initial_pipeline_pressure = p0 # Initial condition for the static pipeline pressure at the reservoir (= hydrostatic pressure - dynamic pressure) \n", + "initial_influx = Q_nenn/1.1 # initial influx of volume to the reservoir [m³/s]\n", + "# initial_outflux = Q0 # initial outflux of volume from the reservoir to the pipeline [m³/s]\n", + "# initial_pipeline_pressure = p0 # Initial condition for the static pipeline pressure at the reservoir (= hydrostatic pressure - dynamic pressure) \n", "initial_pressure_unit = 'Pa' # DO NOT CHANGE! for pressure conversion in print statements and plot labels \n", "conversion_pressure_unit = 'bar' # for pressure conversion in print statements and plot labels\n", "area_base = 74. # total base are of the cuboid reservoir [m²] \n", @@ -97,27 +97,23 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "# create objects\n", "\n", "V = Ausgleichsbecken_class(area_base,area_outflux,critical_level_low,critical_level_high,simulation_timestep)\n", - "V.set_initial_level(initial_level) \n", - "V.set_influx(initial_influx)\n", - "V.set_outflux(initial_outflux)\n", - "V.set_pressure(initial_pipeline_pressure,initial_pressure_unit,conversion_pressure_unit)\n", + "V.set_steady_state(initial_influx,initial_level,initial_pressure_unit,conversion_pressure_unit)\n", "\n", "pipe = Druckrohrleitung_class(L,D,n,alpha,f_D)\n", "pipe.set_pressure_propagation_velocity(c)\n", "pipe.set_number_of_timesteps(nt)\n", - "pipe.set_initial_pressure(p_init,initial_pressure_unit,conversion_pressure_unit)\n", - "pipe.set_initial_flow_velocity(v_init)\n", + "pipe.set_steady_state(initial_influx,V.level,pl_vec,h_vec,initial_pressure_unit,conversion_pressure_unit)\n", "\n", "\n", "T1 = Francis_Turbine(Q_nenn,p_nenn)\n", - "T1.set_LA(1.)\n", + "T1.set_steady_state(initial_influx,pipe.p0[-1])\n", "T1.set_closing_time(30)\n", "\n", "# display the attributes of the created reservoir and pipeline object\n", @@ -127,15 +123,15 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ "# initialization for timeloop\n", "\n", "# prepare the vectors in which the pressure and velocity distribution in the pipeline from the previous timestep are stored\n", - "v_old = v_init.copy()\n", - "p_old = p_init.copy()\n", + "v_old = pipe.v0.copy()\n", + "p_old = pipe.p0.copy()\n", "\n", "# prepare the vectors in which the temporal evolution of the boundary conditions are stored\n", " # keep in mind, that the velocity at the turbine and the pressure at the reservoir are set manually and\n", @@ -147,7 +143,7 @@ "p_boundary_tur = np.empty_like(t_vec)\n", "\n", "# prepare the vectors that store the temporal evolution of the level in the reservoir\n", - "level_vec = np.full(nt+1,initial_level) # level at the end of each pipeline timestep\n", + "level_vec = np.full(nt+1,V.level) # level at the end of each pipeline timestep\n", "level_vec_2 = np.empty([nt_eRK4]) # level throughout each reservoir timestep-used for plotting and overwritten afterwards\n", "\n", "# set the boundary conditions for the first timestep\n", @@ -156,15 +152,15 @@ "p_boundary_res[0] = p_old[0]\n", "p_boundary_tur[0] = p_old[-1]\n", "\n", - "LA_soll_vec = np.zeros_like(t_vec)\n", - "LA_soll_vec[0] = 1\n", - "LA_soll_vec[1000:] = 1\n", + "LA_soll_vec = np.full_like(t_vec,T1.LA)\n", + "LA_soll_vec[1500:]= 0\n", + "\n", "\n" ] }, { "cell_type": "code", - "execution_count": 50, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -197,7 +193,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -247,7 +243,7 @@ " fig1.canvas.draw()\n", " fig1.tight_layout()\n", " fig1.show()\n", - " plt.pause(0.00001) \n", + " plt.pause(0.1) \n", "\n", " # prepare for next loop\n", " p_old = pipe.p_old\n", @@ -259,7 +255,7 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -294,7 +290,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.13 ('Georg_DT_Slot3')", + "display_name": "Python 3.8.13 ('DT_Slot_3')", "language": "python", "name": "python3" }, @@ -313,7 +309,7 @@ "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" + "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48" } } }, diff --git a/Main_Programm.ipynb b/Untertweng_mit_Pegelregler.ipynb similarity index 79% rename from Main_Programm.ipynb rename to Untertweng_mit_Pegelregler.ipynb index 1570767..540e385 100644 --- a/Main_Programm.ipynb +++ b/Untertweng_mit_Pegelregler.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 56, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -11,64 +11,69 @@ "\n", "from functions.pressure_conversion import pressure_conversion\n", "from Ausgleichsbecken.Ausgleichsbecken_class_file import Ausgleichsbecken_class\n", - "from Druckrohrleitung.Druckrohrleitung_class_file import Druckrohrleitung_class" + "from Druckrohrleitung.Druckrohrleitung_class_file import Druckrohrleitung_class\n", + "from Turbinen.Turbinen_class_file import Francis_Turbine\n", + "from Regler.Regler_class_file import PI_controller_class" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ "#define constants\n", "\n", + "#Turbine\n", + "Q_nenn = 0.85\n", + "p_nenn,_ = pressure_conversion(10.6,'bar','Pa')\n", + "\n", "# physics\n", "g = 9.81 # gravitational acceleration [m/s²]\n", "rho = 1000. # density of water [kg/m³]\n", "\n", "# pipeline\n", - "L = 1000. # length of pipeline [m]\n", - "D = 1. # pipe diameter [m]\n", + "L = 535.+478. # length of pipeline [m]\n", + "D = 0.9 # pipe diameter [m]\n", "A_pipe = D**2/4*np.pi # pipeline area\n", - "h_pipe = 200 # hydraulic head without reservoir [m] \n", + "h_pipe = 105 # hydraulic head without reservoir [m] \n", "alpha = np.arcsin(h_pipe/L) # Höhenwinkel der Druckrohrleitung \n", - "n = 50 # number of pipe segments in discretization\n", - "# consider replacing Q0 with a vector be be more flexible in initial conditions\n", - "Q0 = 2. # initial flow in whole pipe [m³/s]\n", - "v0 = Q0/A_pipe # initial flow velocity [m/s]\n", - "f_D = 0.01 # Darcy friction factor\n", - "c = 400. # propagation velocity of the pressure wave [m/s]\n", + "n = 50 # number of pipe segments in discretization\n", + "f_D = 0.014 # Darcy friction factor\n", + "c = 500. # propagation velocity of the pressure wave [m/s]\n", "# consider prescribing a total simulation time and deducting the number of timesteps from that\n", - "nt = 500 # number of time steps after initial conditions\n", + "nt = 1500 # number of time steps after initial conditions\n", "\n", "# derivatives of the pipeline constants\n", "dx = L/n # length of each pipe segment\n", "dt = dx/c # timestep according to method of characterisitics\n", "nn = n+1 # number of nodes\n", - "initial_level = 20. # water level in upstream reservoir [m]\n", - "p0 = rho*g*initial_level-v0**2*rho/2\n", + "initial_level = 8. # water level in upstream reservoir [m]\n", "pl_vec = np.arange(0,nn*dx,dx) # pl = pipe-length. position of the nodes on the pipeline\n", "t_vec = np.arange(0,nt+1)*dt # time vector\n", "h_vec = np.arange(0,n+1)*h_pipe/n # hydraulic head of pipeline at each node \n", - "v_init = np.full(nn,Q0/(D**2/4*np.pi)) # initial velocity distribution in pipeline\n", - "p_init = (rho*g*(initial_level+h_vec)-v_init**2*rho/2)-(f_D*pl_vec/D*rho/2*v_init**2) # ref Wikipedia: Darcy Weisbach\n", - "\n", "\n", "# reservoir\n", "# replace influx by vector\n", - "initial_influx = 0. # initial influx of volume to the reservoir [m³/s]\n", - "initial_outflux = Q0 # initial outflux of volume from the reservoir to the pipeline [m³/s]\n", - "initial_pipeline_pressure = p0 # Initial condition for the static pipeline pressure at the reservoir (= hydrostatic pressure - dynamic pressure) \n", + "initial_influx = Q_nenn/1.1 # initial influx of volume to the reservoir [m³/s]\n", "initial_pressure_unit = 'Pa' # DO NOT CHANGE! for pressure conversion in print statements and plot labels \n", "conversion_pressure_unit = 'bar' # for pressure conversion in print statements and plot labels\n", - "area_base = 20. # total base are of the cuboid reservoir [m²] \n", + "area_base = 74. # total base are of the cuboid reservoir [m²] \n", "area_outflux = A_pipe # outlfux area of the reservoir, given by pipeline area [m²]\n", "critical_level_low = 0. # for yet-to-be-implemented warnings[m]\n", "critical_level_high = np.inf # for yet-to-be-implemented warnings[m]\n", "\n", + "\n", + "# define controller constants\n", + "target_level = initial_level # m\n", + "Kp = 2\n", + "Ti = 10\n", + "deadband_range = 0.05 # m\n", + "\n", "# make sure e-RK4 method of reservoir has a small enough timestep to avoid runaway numerical error\n", "nt_eRK4 = 1000 # number of simulation steps of reservoir in between timesteps of pipeline \n", "simulation_timestep = dt/nt_eRK4\n", + "\n", "\n" ] }, @@ -91,23 +96,26 @@ }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ "# create objects\n", "\n", "V = Ausgleichsbecken_class(area_base,area_outflux,critical_level_low,critical_level_high,simulation_timestep)\n", - "V.set_initial_level(initial_level) \n", - "V.set_influx(initial_influx)\n", - "V.set_outflux(initial_outflux)\n", - "V.set_pressure(initial_pipeline_pressure,initial_pressure_unit,conversion_pressure_unit)\n", + "V.set_steady_state(initial_influx,initial_level,initial_pressure_unit,conversion_pressure_unit)\n", "\n", "pipe = Druckrohrleitung_class(L,D,n,alpha,f_D)\n", "pipe.set_pressure_propagation_velocity(c)\n", "pipe.set_number_of_timesteps(nt)\n", - "pipe.set_initial_pressure(p_init,initial_pressure_unit,conversion_pressure_unit)\n", - "pipe.set_initial_flow_velocity(v_init)\n", + "pipe.set_steady_state(initial_influx,V.level,pl_vec,h_vec,initial_pressure_unit,conversion_pressure_unit)\n", + "\n", + "\n", + "T1 = Francis_Turbine(Q_nenn,p_nenn)\n", + "T1.set_steady_state(initial_influx,pipe.p0[-1])\n", + "T1.set_closing_time(5)\n", + "\n", + "Pegelregler = PI_controller_class(target_level,deadband_range,Kp,Ti,dt)\n", "\n", "# display the attributes of the created reservoir and pipeline object\n", "# V.get_info(full=True)\n", @@ -116,15 +124,15 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ "# initialization for timeloop\n", "\n", "# prepare the vectors in which the pressure and velocity distribution in the pipeline from the previous timestep are stored\n", - "v_old = v_init.copy()\n", - "p_old = p_init.copy()\n", + "v_old = pipe.v0.copy()\n", + "p_old = pipe.p0.copy()\n", "\n", "# prepare the vectors in which the temporal evolution of the boundary conditions are stored\n", " # keep in mind, that the velocity at the turbine and the pressure at the reservoir are set manually and\n", @@ -136,24 +144,25 @@ "p_boundary_tur = np.empty_like(t_vec)\n", "\n", "# prepare the vectors that store the temporal evolution of the level in the reservoir\n", - "level_vec = np.full(nt+1,initial_level) # level at the end of each pipeline timestep\n", + "level_vec = np.full(nt+1,V.level) # level at the end of each pipeline timestep\n", "level_vec_2 = np.empty([nt_eRK4]) # level throughout each reservoir timestep-used for plotting and overwritten afterwards\n", "\n", - "# set the boudary conditions for the first timestep\n", + "# set the boundary conditions for the first timestep\n", "v_boundary_res[0] = v_old[0]\n", "v_boundary_tur[0] = v_old[-1] \n", - "v_boundary_tur[1:] = 0 # instantaneous closing\n", - "# v_boundary_tur[0:20] = np.linspace(v_old[-1],0,20) # overwrite for finite closing time - linear case\n", - "# const = int(np.min([100,round(nt/1.1)]))\n", - "# v_boundary_tur[0:const] = v_old[1]*np.cos(t_vec[0:const]*2*np.pi/5)**2\n", - "p_boundary_res[0] = p_old[0]\n", - "p_boundary_tur[0] = p_old[-1]\n", + "p_boundary_res[0] = p_old[0]\n", + "p_boundary_tur[0] = p_old[-1]\n", + "\n", + "LA_soll_vec = np.full_like(t_vec,T1.LA)\n", + "Pegelregler.control_variable = T1.LA\n", + "\n", + "\n", "\n" ] }, { "cell_type": "code", - "execution_count": 60, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -172,7 +181,7 @@ "lo_00, = axs1[0].plot(pl_vec,pressure_conversion(pipe.p_old,initial_pressure_unit, conversion_pressure_unit)[0],marker='.')\n", "lo_01, = axs1[1].plot(pl_vec,pipe.v_old,marker='.')\n", "axs1[0].autoscale()\n", - "axs1[1].autoscale()\n", + "axs1[1].set_ylim([0,2])\n", "# displaying the reservoir level within each pipeline timestep\n", "# axs1[2].set_title('Level reservoir')\n", "# axs1[2].set_xlabel(r'$t$ [$\\mathrm{s}$]')\n", @@ -186,13 +195,16 @@ }, { "cell_type": "code", - "execution_count": 61, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "# loop through time steps of the pipeline\n", "for it_pipe in range(1,pipe.nt+1):\n", "\n", + " if it_pipe == 150:\n", + " V.influx = 0\n", + "\n", "# for each pipeline timestep, execute nt_eRK4 timesteps of the reservoir code\n", " # set initial conditions for the reservoir time evolution calculted with e-RK4\n", " V.pressure = p_old[0]\n", @@ -213,6 +225,10 @@ " v_boundary_res[it_pipe] = v_old[1]+1/(rho*c)*(p_boundary_res[it_pipe]-p_old[1])-f_D*dt/(2*D)*abs(v_old[1])*v_old[1] \\\n", " +dt*g*np.sin(alpha)\n", "\n", + " LA_soll_vec[it_pipe] = Pegelregler.get_control_variable(V.level)\n", + " T1.change_LA(LA_soll_vec[it_pipe],dt)\n", + " v_boundary_tur[it_pipe] = 1/A_pipe*T1.get_Q(p_old[-1])\n", + "\n", " # the the boundary conditions in the pipe.object and thereby calculate boundary pressure at turbine\n", " pipe.set_boundary_conditions_next_timestep(v_boundary_res[it_pipe],p_boundary_res[it_pipe],v_boundary_tur[it_pipe])\n", " p_boundary_tur[it_pipe] = pipe.p_boundary_tur\n", @@ -233,7 +249,7 @@ " fig1.canvas.draw()\n", " fig1.tight_layout()\n", " fig1.show()\n", - " plt.pause(0.00001) \n", + " plt.pause(0.1) \n", "\n", " # prepare for next loop\n", " p_old = pipe.p_old\n", @@ -245,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 62, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -280,7 +296,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.13 ('Georg_DT_Slot3')", + "display_name": "Python 3.8.13 ('DT_Slot_3')", "language": "python", "name": "python3" }, @@ -299,7 +315,7 @@ "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" + "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48" } } }, From 11cfda4615cc2541b1e714254de8afc940937e8b Mon Sep 17 00:00:00 2001 From: Brantegger Georg Date: Mon, 25 Jul 2022 10:22:22 +0200 Subject: [PATCH 11/12] added PI controller test --- ...ramm.ipynb => Druckrohrleitung_test.ipynb} | 0 Regler/Regler_class_file.py | 1 - Regler/regler_test.ipynb | 517 ++++++++---------- Regler/regler_test_optimierung.ipynb | 386 +++++++++++++ Untertweng_mit_Pegelregler.ipynb | 4 +- 5 files changed, 625 insertions(+), 283 deletions(-) rename Druckrohrleitung/{Main_Programm.ipynb => Druckrohrleitung_test.ipynb} (100%) create mode 100644 Regler/regler_test_optimierung.ipynb diff --git a/Druckrohrleitung/Main_Programm.ipynb b/Druckrohrleitung/Druckrohrleitung_test.ipynb similarity index 100% rename from Druckrohrleitung/Main_Programm.ipynb rename to Druckrohrleitung/Druckrohrleitung_test.ipynb diff --git a/Regler/Regler_class_file.py b/Regler/Regler_class_file.py index 8e5b469..c205257 100644 --- a/Regler/Regler_class_file.py +++ b/Regler/Regler_class_file.py @@ -115,7 +115,6 @@ class PI_controller_class: if new_control > self.cv_upper_limit: new_control = self.cv_upper_limit - self.control_variable = new_control return self.control_variable diff --git a/Regler/regler_test.ipynb b/Regler/regler_test.ipynb index 7bf1ed9..c8c44b1 100644 --- a/Regler/regler_test.ipynb +++ b/Regler/regler_test.ipynb @@ -2,357 +2,314 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "import matplotlib.pyplot as plt\n", - "from Regler_class_file import P_controller_class\n", - "from Regler_class_file import PI_controller_class\n" + "from Regler_class_file import PI_controller_class\n", + "\n", + "#importing Druckrohrleitung\n", + "import sys\n", + "import os\n", + "current = os.path.dirname(os.path.realpath('Main_Programm.ipynb'))\n", + "parent = os.path.dirname(current)\n", + "sys.path.append(parent)\n", + "from functions.pressure_conversion import pressure_conversion\n", + "from Ausgleichsbecken.Ausgleichsbecken_class_file import Ausgleichsbecken_class\n", + "from Turbinen.Turbinen_class_file import Francis_Turbine" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ - "# # %matplotlib widget\n", - "# pi_controller = PI_controller_class(setpoint=100,proportionality_constant=1,Ti = 30,timestep = 0.1)\n", + "#define constants\n", "\n", - "# t_max = 100 #s\n", - "# dt = 0.1 #s\n", - "# nt = int(t_max//dt)\n", - "# t_vec = np.arange(0,nt+1,1)*dt\n", + "#Turbine\n", + "Q_nenn = 0.85\n", + "p_nenn,_ = pressure_conversion(10.6,'bar','Pa')\n", "\n", - "# PV_0 = 50\n", + "# physics\n", + "g = 9.81 # gravitational acceleration [m/s²]\n", + "rho = 1000. # density of water [kg/m³]\n", "\n", - "# PV_vec = np.full_like(t_vec,PV_0)\n", + "# define controller constants\n", + "target_level = 8. # m\n", + "Kp = 0.1\n", + "Ti = 100.\n", + "deadband_range = 0.05 # m\n", "\n", - "# for i in range(1,nt+1):\n", - "# pi_controller.calculate_error(PV_vec[i-1])\n", + "# reservoir\n", + "initial_level = target_level\n", + "initial_influx = Q_nenn/2 # initial influx of volume to the reservoir [m³/s]\n", + "initial_pressure_unit = 'Pa' # DO NOT CHANGE! for pressure conversion in print statements and plot labels \n", + "conversion_pressure_unit = 'bar' # for pressure conversion in print statements and plot labels\n", + "area_base = 74. # total base are of the cuboid reservoir [m²] \n", + "area_outflux = 1. # outflux area of the reservoir, given by pipeline area [m²]\n", + "critical_level_low = 0. # for yet-to-be-implemented warnings[m]\n", + "critical_level_high = np.inf # for yet-to-be-implemented warnings[m]\n", "\n", - "# if i == 500:\n", - "# pi_controller.SP = 0.\n", - "# PV_vec[i] = PV_vec[i-1]+pi_controller.get_control_variable()\n", + "p0 = rho*g*initial_level-0.5*rho*(initial_influx/area_outflux)**2\n", "\n", + "# offset the pressure in front of the turbine to get realisitc fluxes\n", + "h_fict = 100\n", + "offset_pressure = rho*g*h_fict\n", "\n", + "t_max = 1e3 #s\n", + "nt = int(1e6) # number of simulation steps of reservoir in between timesteps of pipeline \n", + "dt = t_max/nt\n", "\n", - "# plt.plot(t_vec,PV_vec,'.')" + "t_vec = np.arange(0,nt+1,1)*dt\n", + "\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ - "SP_0 = 1.1\n", - "SP_1 = 0.24\n", - "PV_0 = 0.5" + "# create objects\n", + "\n", + "V = Ausgleichsbecken_class(area_base,area_outflux,critical_level_low,critical_level_high,dt)\n", + "V.set_steady_state(initial_influx,initial_level,initial_pressure_unit,conversion_pressure_unit)\n", + "\n", + "T1 = Francis_Turbine(Q_nenn,p_nenn)\n", + "T1.set_steady_state(initial_influx,p0+offset_pressure)\n", + "T1.set_closing_time(500)\n", + "\n", + "Pegelregler = PI_controller_class(target_level,deadband_range,Kp,Ti,dt)" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "level_vec = np.full(nt+1,V.level)\n", + "LA_ist_vec = np.full(nt+1,T1.LA)\n", + "LA_soll_vec = np.full(nt+1,T1.LA)\n", + "Q_vec = np.full(nt+1,initial_influx)\n", + "\n", + "Pegelregler.control_variable = T1.LA" + ] + }, + { + "cell_type": "code", + "execution_count": 30, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "0\n" + "0.0\n", + "10.0\n", + "20.0\n", + "30.0\n", + "40.0\n", + "50.0\n", + "60.0\n", + "70.0\n", + "80.0\n", + "90.0\n", + "100.0\n", + "110.0\n", + "120.0\n", + "130.0\n", + "140.0\n", + "150.0\n", + "160.0\n", + "170.0\n", + "180.0\n", + "190.0\n", + "200.0\n", + "210.0\n", + "220.0\n", + "230.0\n", + "240.0\n", + "250.0\n", + "260.0\n", + "270.0\n", + "280.0\n", + "290.0\n", + "300.0\n", + "310.0\n", + "320.0\n", + "330.0\n", + "340.0\n", + "350.0\n", + "360.0\n", + "370.0\n", + "380.0\n", + "390.0\n", + "400.0\n", + "410.0\n", + "420.0\n", + "430.0\n", + "440.0\n", + "450.0\n", + "460.0\n", + "470.0\n", + "480.0\n", + "490.0\n", + "500.0\n", + "510.0\n", + "520.0\n", + "530.0\n", + "540.0\n", + "550.0\n", + "560.0\n", + "570.0\n", + "580.0\n", + "590.0\n", + "600.0\n", + "610.0\n", + "620.0\n", + "630.0\n", + "640.0\n", + "650.0\n", + "660.0\n", + "670.0\n", + "680.0\n", + "690.0\n", + "700.0\n", + "710.0\n", + "720.0\n", + "730.0\n", + "740.0\n", + "750.0\n", + "760.0\n", + "770.0\n", + "780.0\n", + "790.0\n", + "800.0\n", + "810.0\n", + "820.0\n", + "830.0\n", + "840.0\n", + "850.0\n", + "860.0\n", + "870.0\n", + "880.0\n", + "890.0\n", + "900.0\n", + "910.0\n", + "920.0\n", + "930.0\n", + "940.0\n", + "950.0\n", + "960.0\n", + "970.0\n", + "980.0\n", + "990.0\n", + "1000.0\n" ] } ], "source": [ - "n = 10\n", - "Kp_max = 5.\n", - "Ti_max = 5.\n", - "d_Kp = Kp_max/n\n", - "d_Ti = Ti_max/n\n", - "kp_vec = np.arange(1,n+1,1)*d_Kp\n", - "Ti_vec = np.arange(1,n+1,1)*d_Ti\n", + "# time loop\n", "\n", - "XX,YY = np.meshgrid(kp_vec,Ti_vec)\n", + "for i in range(nt+1):\n", "\n", - "ise_mat = np.empty_like(XX)\n", - "iae_mat = np.empty_like(XX)\n", - "itse_mat = np.empty_like(XX)\n", - "itae_mat = np.empty_like(XX)\n", + " if np.mod(i,1e4) == 0:\n", + " print(t_vec[i])\n", "\n", + " if t_vec[i] == 0.4*np.max(t_vec):\n", + " V.influx = 0\n", "\n", - "t_max = 100 #s\n", - "dt = 0.05 #s\n", - "nt = int(t_max//dt)\n", - "t_vec = np.arange(0,nt+1,1)*dt\n", + " p = rho*g*V.level-0.5*rho*(V.outflux_vel)**2\n", "\n", - "for i in range(n):\n", - " if i%10 == 0:\n", - " print(i)\n", - " for j in range(n):\n", - " Kp = XX[i,j]\n", - " Ti = YY[i,j]\n", + " LA_soll = Pegelregler.get_control_variable(V.level)\n", + " T1.change_LA(LA_soll,dt)\n", + " LA_soll_vec[i] = LA_soll\n", + " LA_ist_vec[i] = T1.LA\n", + " Q_vec[i] = T1.get_Q(p+offset_pressure)\n", "\n", - " c = PI_controller_class(SP_0,Kp,Ti,dt)\n", + " V.pressure = p\n", + " V.outflux_vel = 1/V.area_outflux*Q_vec[i]\n", "\n", - " PV_vec = np.full_like(t_vec,PV_0)\n", - "\n", - " for t in range(1,nt+1):\n", - " c.calculate_error(PV_vec[t-1])\n", - "\n", - " if t == 500:\n", - " c.SP = SP_1\n", - " PV_vec[t] = PV_vec[t-1]+c.get_control_variable()\n", - " \n", - " ise_mat[i,j],iae_mat[i,j],itse_mat[i,j],itae_mat[i,j] = np.log(c.get_performance_indicators())\n", - "\n", - "\n" + " V.e_RK_4() \n", + " V.level = V.update_level(V.timestep) \n", + " V.set_volume() \n", + " level_vec[i] = V.level \n", + " \n", + " " ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d9226014321c46daa6791bc1dfd0f06d", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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- "text/html": [ - "\n", - "
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\n", - " Figure\n", - "
\n", - " \n", - "
\n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib widget\n", - "fig1,axs1 = plt.subplots(1,1)\n", - "ise_min = np.min(ise_mat)\n", - "ise_max = np.max(ise_mat)\n", - "mesh1 = axs1.pcolormesh(XX,YY,ise_mat,cmap='RdBu', vmin=ise_min, vmax=ise_max,shading='nearest')\n", - "fig1.colorbar(mesh1)\n", - "fig2,axs2 = plt.subplots(1,1)\n", - "iae_min = np.min(iae_mat)\n", - "iae_max = np.max(iae_mat)\n", - "mesh2 = axs2.pcolormesh(XX,YY,iae_mat,cmap='RdBu', vmin=iae_min, vmax=iae_max,shading='nearest')\n", - "fig2.colorbar(mesh2)\n", - "fig3,axs3 = plt.subplots(1,1)\n", - "itse_min = np.min(itse_mat)\n", - "itse_max = np.max(itse_mat)\n", - "mesh3 = axs3.pcolormesh(XX,YY,itse_mat,cmap='RdBu', vmin=itse_min, vmax=itse_max,shading='nearest')\n", - "fig3.colorbar(mesh3)\n", - "fig4,axs4 = plt.subplots(1,1)\n", - "itae_min = np.min(itae_mat)\n", - "itae_max = np.max(itae_mat)\n", - "mesh4 = axs4.pcolormesh(XX,YY,itae_mat,cmap='RdBu', vmin=itae_min, vmax=itae_max,shading='nearest')\n", - "fig4.colorbar(mesh4)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ - "ise_ind = np.unravel_index(np.argmin(ise_mat,axis=None),ise_mat.shape)\n", - "Kp_ise = XX[ise_ind]\n", - "Ti_ise = YY[ise_ind]\n", - "iae_ind = np.unravel_index(np.argmin(iae_mat,axis=None),iae_mat.shape)\n", - "Kp_iae = XX[iae_ind]\n", - "Ti_iae = YY[iae_ind]\n", - "itse_ind = np.unravel_index(np.argmin(itse_mat,axis=None),itse_mat.shape)\n", - "Kp_itse = XX[itse_ind]\n", - "Ti_itse = YY[itse_ind]\n", - "itae_ind = np.unravel_index(np.argmin(itae_mat,axis=None),itae_mat.shape)\n", - "Kp_itae = XX[itae_ind]\n", - "Ti_itae = YY[itae_ind]\n", - "\n" + "%matplotlib qt5\n", + "# time loop\n", + "\n", + "# create a figure and subplots to display the velocity and pressure distribution across the pipeline in each pipeline step\n", + "fig1,axs1 = plt.subplots(3,1)\n", + "axs1[0].set_title('Level')\n", + "axs1[0].set_xlabel(r'$t$ [$\\mathrm{s}$]')\n", + "axs1[0].set_ylabel(r'$h$ [$\\mathrm{m}$]')\n", + "axs1[0].plot(t_vec,level_vec)\n", + "axs1[0].set_ylim([0.85*initial_level,1.05*initial_level])\n", + "axs1[1].set_title('Flux')\n", + "axs1[1].set_xlabel(r'$t$ [$\\mathrm{s}$]')\n", + "axs1[1].set_ylabel(r'$Q$ [$\\mathrm{m} / \\mathrm{s}^3$]')\n", + "axs1[1].plot(t_vec,Q_vec)\n", + "axs1[1].set_ylim([0,2*initial_influx])\n", + "axs1[2].set_title('LA')\n", + "axs1[2].set_xlabel(r'$t$ [$\\mathrm{s}$]')\n", + "axs1[2].set_ylabel(r'$LA$ [\\%]')\n", + "axs1[2].plot(t_vec,LA_soll_vec)\n", + "axs1[2].plot(t_vec,LA_ist_vec)\n", + "axs1[2].set_ylim([0,1])\n", + "fig1.tight_layout()\n", + "fig1.show()\n", + "plt.pause(1)" ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 8, + "execution_count": 32, "metadata": {}, "output_type": "execute_result" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "05f5663bd9ab4b23a30ec4ee94e41a1e", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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KlCkREdHe3t5nTcBzzjknNm/eHN/85jfjr//6r+OAAw6Ik08+Ob761a+W6yW8yRhAKD/rcAIZlpkAGBGxYMGCWLBgQdHnVqxYscu2Cy+8MC688MISlwoY3nQBA9mTmS7gbNlxL2AtEABAKQiAAEW5AAOySwBMoZ5Jh8YAQgqYBQxkkAAIAJAzAmAaaXGAstMCD2SZAAjQLxdkQPYIgGlkHUAAoIQEQIBiepZhMiQDyCABMIWSZOc6gFoAAYASEAABAHJGAEwxYwAhDXQBA9kjAAIA5IwAmEJJz6Bz9wIGAEpAAAQoyixgILsEwBQqxI5ZwD52AIBSEAABikgMwQAyTABMod4xgGYBQwpoiweyRwAEAMgZATCN3AsYUsD5B2SXAAjQH7OAgQwSAFPozSGAWiAAgMEnAAIU5QIMyC4BMJWsAwipoQsYyCABEAAgZwTANLIOIJSfMbhAhgmAAP3QAQxkkQCYQkloAQQASkcABCiidyH2pLu8BQEoAQEwjXruBGIMEgBQAgIgAEDOCIBpZN0xSBHnI5A9AiAAQM4IgGnUMwbQLGAoH2NwgQwTAAH6UdADDGSQAJhK1gEEAEpHAAQoygUYkF0CYAol1gGE1EjCQtBA9giAAAA5IwCmUWIMIJSdFnggwwRAgP5YmB3IIAEwlbQAAgClIwACFGEhdiDLBMAU6p0FXOZyAADZJAACAOSMAJhKO9v+zEKE8uk5/0wCATJIAAQAyBkBMIUK1gEEAEpIAAQoqucCTBcwkD0CYAq9OQtYCyAAMPgEQACAnBEAU8ksYEgNs4CBDBIAAQByRgBMI2MAofwK3h6B7PIOB9AvXcBA9giAKZQYcwQAlJAACACQMwJgKrkTCKRFQYs8kEECIABAzgiAaZRYBxDKz/kHZJcACNCPxCxgIIMEwFSyDiAAUDoCIEAxhmAAGSYAplFiFjCkhlnAQAYJgAAAOZOpALhs2bJoaGiI2traaGxsjAcffLDf/bu6umLJkiUxZcqUqKmpiXe+851x0003DVFp+9MzC7i8pYA8e3MMrhZAIHuqyl2AwbJy5cpYuHBhLFu2LI477rj49re/HbNnz44nnngiDj300KLHnHHGGfHSSy/FjTfeGH/0R38UGzZsiG3btg1xyQEAhlZmAuA111wT8+bNi/POOy8iIpYuXRo/+tGPYvny5dHc3LzL/vfee2/cf//98cwzz8SBBx4YERGHHXbYUBZ594wBBABKKBNdwFu3bo3W1tZoamrqs72pqSkefvjhosfcc889MWPGjPja174WhxxySBxxxBFx6aWXxmuvvTYURe5XYtA5lF/PLGDnI5BBmWgB3LhxY2zfvj3q6+v7bK+vr4/169cXPeaZZ56Jhx56KGpra+P73/9+bNy4MRYsWBC/+93vdjsOsKurK7q6unq/7uzsHLwXUYR1AAGAUshEC2CPwtvW7UqSZJdtPbq7u6NQKMStt94axx57bJx66qlxzTXXxIoVK3bbCtjc3Bx1dXW9j8mTJw/6a9hZ8hJ9X2DgXIAB2ZWJADhu3LiorKzcpbVvw4YNu7QK9pgwYUIccsghUVdX17tt2rRpkSRJPP/880WPWbx4cXR0dPQ+1q1bN3gvohgL0UIKuCADsicTAbC6ujoaGxujpaWlz/aWlpaYNWtW0WOOO+64ePHFF+OVV17p3fbUU09FRUVFTJo0qegxNTU1MWbMmD6PkjDmCAAooUwEwIiIRYsWxXe+85246aab4sknn4xLLrkk2traYv78+RGxo/Vu7ty5vfufeeaZMXbs2PjMZz4TTzzxRDzwwANx2WWXxbnnnhsjR44s18t4Gy2AUDZa4IEMy8QkkIiIOXPmxKZNm+LKK6+M9vb2mD59eqxatSqmTJkSERHt7e3R1tbWu//o0aOjpaUlLrzwwpgxY0aMHTs2zjjjjLjqqqvK9RLeQgsgpIbTEcigzATAiIgFCxbEggULij63YsWKXbZNnTp1l27jNDELGAAohcx0AWeKMYCQAi7AgOwSANPMGCRIge5yFwBg0AmAqaQFEAAoHQEQACBnBMA0MgYQUqPgfAQySABMNWMAAYDBJwCmkhYHKLuCt0cgu7zDpVhiFjAAUAICYBoZcwQAlJAAmGpaAKHsEusAAtkjAKaRFkAAoIQEwFTTAggADD4BMIW0/0H5mYQFZJkAmGY+gACAEhAA08igc0gBb49AdnmHS7HEGEBIAYMygOwRAFPJBw4AUDoCIEAxGuCBDBMAU6igARDSw7qcQAYJgGlmFjAAUAICYAolYRYwlJ8LMCC7BMBU8wEE5acLGMgeATCNjDkCAEpIAEwzYwChfJx/QIYJgAD90SIPZJAAmGLuBAIAlIIAmEZaHCAFCjv/dD4C2SMAppkxSABACQiAqZTEiy9Xx6v3/Hu8uPrBchcGAMgYATClXv7tqJiwritevvO2chcF8qmnBd6QDCCDqspdAN70xgsvxLbfvxzjnlkbb/y2dsfGe38Sr/3J1yLefVpUveOAGHHIIeUtJAAw7AmAKfKbD5/ylq92tj68XojfLrk5Im6OiIhpv3pyyMsFAGSLLuAUeeKCpti283+k0DsDccff2yp2PA8MjULvMky6gIHsEQBT5IPnfjEqr11c9LnKaxfHB8/94hCXCADIIgEwRQ4adVA0TGiMiIhkZ6tDz98NExrjoFEHla1skDeJZZiADBMAU6Zq7NioHDcu4ojD4vr/UhFxxGFROW5cVI0dW+6iAQAZYRJIyowYPz7+6J9+EhvfeDne9fSdMe5d/y3GjTggKqqry100ACAjBMAUqqiujoOrD44FRy8od1Egx3QBA9mlCxigP0l3uUsAMOgEQACAnBEAAYoxCxjIMAEQACBnBEAAgJwRAAGKcis4ILsEQACAnBEAAQByRgAEKKZnFnCiCxjIHgEQACBnBEAAgJwRAAH6UTALGMggARAAIGcEQIBi3AoOyDABEKA/ZgEDGSQAAgDkjAAIUETB2yOQYd7hAPqlCxjIHgEQACBnBECAIhKTgIEMEwAB+mMWMJBBAiAAQM4IgABF6QMGsksABADIGQEQACBnBECAYgo73h4L1gEEMkgABADImUwFwGXLlkVDQ0PU1tZGY2NjPPjggwM67mc/+1lUVVXF0UcfXdoCAgCkQGYC4MqVK2PhwoWxZMmSWLNmTZxwwgkxe/bsaGtr6/e4jo6OmDt3bnz4wx8eopICw0HvHGDrAAIZlJkAeM0118S8efPivPPOi2nTpsXSpUtj8uTJsXz58n6P++xnPxtnnnlmzJw5c4hKCgBQXpkIgFu3bo3W1tZoamrqs72pqSkefvjh3R538803x3/8x3/EFVdcMaCf09XVFZ2dnX0eAADDTSYC4MaNG2P79u1RX1/fZ3t9fX2sX7++6DFPP/10XH755XHrrbdGVVXVgH5Oc3Nz1NXV9T4mT568z2UHUqrQ0wmsCxjInkwEwB6FQt+V+5Mk2WVbRMT27dvjzDPPjL/927+NI444YsDff/HixdHR0dH7WLdu3T6XGQBgqA2s6Svlxo0bF5WVlbu09m3YsGGXVsGIiM2bN8fq1atjzZo1ccEFF0RERHd3dyRJElVVVXHffffFySefvMtxNTU1UVNTU5oXAaRLkYtHgKzIRAtgdXV1NDY2RktLS5/tLS0tMWvWrF32HzNmTDz22GOxdu3a3sf8+fPj3e9+d6xduzbe//73D1XRgdTTBQxkTyZaACMiFi1aFGeddVbMmDEjZs6cGddff320tbXF/PnzI2JH9+0LL7wQ3/3ud6OioiKmT5/e5/iDDz44amtrd9kOAJA1mQmAc+bMiU2bNsWVV14Z7e3tMX369Fi1alVMmTIlIiLa29v/4JqAAG/SBQxkVyFJrHK6tzo7O6Ouri46OjpizJgx5S4OMIh+fsuX4wO/+V+xesxHYsaiO8tdHGAQ+fzOyBhAAAAGTgAEKMYsYCDDBECAfhklA2SPAAgAkDMCIEBRuoCB7BIAAQByRgAEAMgZARCgmJ5ZwJZKBTJIAAQAyBkBEAAgZwRAgKIKO//UBQxkjwAIAJAzAiBAMW4FB2SYAAjQL13AQPYIgAAAOSMAAhSlCxjILgEQoD8WggYySAAEAMgZARCgiIJZwECGCYAA/bAQNJBFAiAAQM4IgADF6AIGMkwABOiXLmAgewRAAICcEQABitrZBawBEMggARAAIGcEQACAnBEAAYrpnQWsDxjIHgEQACBnBEAAgJwRAAGKKuz8UxcwkD0CIABAzgiAAMW4FRyQYQIgQH8SXcBA9giAAAA5IwACFKULGMguARCgX7qAgewRAAEAckYABCiiYBYwkGECIABAzgiAAAA5IwACFFNwKzgguwRAAICcEQABAHJGAAQoaucsYLeCAzJIAAQAyBkBEAAgZwRAgCIKZgEDGSYAAgDkjAAIAJAzVeUuAEAaJb33As5WF/CLqx+M5y796xj1n5ujkEQUkoiKJKK7EJHsfMlv3zaQffb2uFJ+7+FyXBrLlJrXEhFdZ/1ZHPOFrw7o95uBEwABUuitQa2ie/A+ZCuTiAOK/LyKJHbJum/fNpB99va4Un7v4XJcGsuUhtdSedf/ixAAB50ACFBU4Q/vsofe3voWsfuWkCR2DWqD9SELw8mIV7fGb085Mqo/8F9j9AnHx5iPfrTcRcoEARCgP3u4EHRPyNtvw+ao7O7bIlfRXbz1LUJIg90pRCFee/6NeO3OO6PjzjtjzK+eLHeRMkEABNhLT96yPLquvjYqunffcifYweBIIuLXh9fEtHIXJCMEQIAiCoW+XcDFxuRFRNSUoWyQR4/PnhKTLr2i3MXIDAEQYDeebNsv4ucvxOMrphUdkwcMnQ8d8V/joENmlrsYmSEAAuxG1+P7RU13uUtRWnqnGQ4KEVE7VefvYBIAAd5iy6OPxsur/l/U/XhlbO9Iz1tkKYLaaxFx3bxx0V77eowaMTpqksqIiOgqbI8tb7wSo0aMjlFvVMSWEd29XxfbZ2+PK+X3Hi7HpbFMaXktm0cm0ZVsjS+9/79H4/gZUX3ggfvy687bpOfdDSAFnjvzU0P2swYa6rYUIv7+7JGx/YC6ffqQ3TwyiVcKr8ex44+NBUd+LqbWN8QN1dUlenVAmgmAAG+x5rj6OPpnLw3aKoC7C3lJRNw8Z2y0TRixS0vI5uTV2G/0O2LGgUfHOTM+G1PfMSluE9SAQSQAArzFMV+7LtbecGUctWLNHh1XLOi9WhFx/WfGxeuja2J7VSG21lbEyOpR8YVjl8QRB02Nr48ePTiFBthDAiDAW0wdOzXGv+eT0R79B8Dkbf++4S8OiF/Xb9+l5U4XK5BGAiDA29RMOSwidt99u6UQ0fyX+8WI8fVxReMX44hD3htLteYBw4gACPA2I498b7xr9b/F0y88Fl9uvSo2dG2KsVV1fVr27tKyBwxjFeUuwGBatmxZNDQ0RG1tbTQ2NsaDDz64233vvvvu+MhHPhIHHXRQjBkzJmbOnBk/+tGPhrC0QJpVjR4d0949M1ae+X/jp5/5edx51o/i8tO+GuPrD48K4Q8Y5jITAFeuXBkLFy6MJUuWxJo1a+KEE06I2bNnR1tbW9H9H3jggfjIRz4Sq1atitbW1jjppJPi9NNPjzVr9mzgNwDAcFNIkiQTC8G///3vj2OOOSaWL1/eu23atGnxsY99LJqbmwf0Pd7znvfEnDlz4ktf+tKA9u/s7Iy6urro6OiIMWPG7FW5AYCh5fM7Iy2AW7dujdbW1mhqauqzvampKR5++OEBfY/u7u7YvHlzHNjPSuNdXV3R2dnZ5wEAMNxkIgBu3Lgxtm/fHvX19X2219fXx/r16wf0Pf7+7/8+Xn311TjjjDN2u09zc3PU1dX1PiZPnrxP5QYAKIdMBMAehULftfuTJNllWzG33XZbfPnLX46VK1fGwQcfvNv9Fi9eHB0dHb2PdevW7XOZAQCGWiaWgRk3blxUVlbu0tq3YcOGXVoF327lypUxb968uOOOO+KUU07pd9+ampqoqanZ5/ICAJRTJloAq6uro7GxMVpaWvpsb2lpiVmzZu32uNtuuy3OOeec+N73vhennXZaqYsJAJAKmWgBjIhYtGhRnHXWWTFjxoyYOXNmXH/99dHW1hbz58+PiB3dty+88EJ897vfjYgd4W/u3LnxjW98Iz7wgQ/0th6OHDky6urqyvY6AABKLTMBcM6cObFp06a48soro729PaZPnx6rVq2KKVOmREREe3t7nzUBv/3tb8e2bdvi/PPPj/PPP793+9lnnx0rVqwY6uIDAAyZzKwDWA7WEQKA4cfnd4ZaAMuhJztbDxAAho+ez+08t4EJgPtg8+bNERHWAwSAYWjz5s25HfevC3gfdHd3x4svvhj777//gNYb3BOdnZ0xefLkWLduXW6bp4eCeh4a6nloqOeho66HRqnqOUmS2Lx5c0ycODEqKjKxIMoe0wK4DyoqKmLSpEkl/Rljxozx5jIE1PPQUM9DQz0PHXU9NEpRz3lt+euRz9gLAJBjAiAAQM4IgClVU1MTV1xxhVvPlZh6HhrqeWio56GjroeGei4dk0AAAHJGCyAAQM4IgAAAOSMAAgDkjAAIAJAzAmAKLVu2LBoaGqK2tjYaGxvjwQcfLHeRhrXm5ub4kz/5k9h///3j4IMPjo997GPx61//us8+SZLEl7/85Zg4cWKMHDkyPvShD8Xjjz9ephJnQ3NzcxQKhVi4cGHvNvU8OF544YX49Kc/HWPHjo1Ro0bF0UcfHa2trb3Pq+fBsW3btvjiF78YDQ0NMXLkyDj88MPjyiuvjO7u7t591PWee+CBB+L000+PiRMnRqFQiB/84Ad9nh9InXZ1dcWFF14Y48aNi/322y/+7M/+LJ5//vkhfBUZkJAqt99+ezJixIjkhhtuSJ544onk4osvTvbbb7/kueeeK3fRhq2PfvSjyc0335z88pe/TNauXZucdtppyaGHHpq88sorvftcffXVyf7775/cddddyWOPPZbMmTMnmTBhQtLZ2VnGkg9fjzzySHLYYYclRx55ZHLxxRf3blfP++53v/tdMmXKlOScc85J/vVf/zV59tlnkx//+MfJb37zm9591PPguOqqq5KxY8cm//iP/5g8++yzyR133JGMHj06Wbp0ae8+6nrPrVq1KlmyZEly1113JRGRfP/73+/z/EDqdP78+ckhhxyStLS0JI8++mhy0kknJUcddVSybdu2IX41w5cAmDLHHntsMn/+/D7bpk6dmlx++eVlKlH2bNiwIYmI5P7770+SJEm6u7uT8ePHJ1dffXXvPq+//npSV1eXXHfddeUq5rC1efPm5F3velfS0tKSnHjiib0BUD0Pjs9//vPJ8ccfv9vn1fPgOe2005Jzzz23z7aPf/zjyac//ekkSdT1YHh7ABxInb788svJiBEjkttvv713nxdeeCGpqKhI7r333iEr+3CnCzhFtm7dGq2trdHU1NRne1NTUzz88MNlKlX2dHR0RETEgQceGBERzz77bKxfv75PvdfU1MSJJ56o3vfC+eefH6eddlqccsopfbar58Fxzz33xIwZM+LP//zP4+CDD473ve99ccMNN/Q+r54Hz/HHHx8/+clP4qmnnoqIiF/84hfx0EMPxamnnhoR6roUBlKnra2t8cYbb/TZZ+LEiTF9+nT1vgeqyl0A3rRx48bYvn171NfX99leX18f69evL1OpsiVJkli0aFEcf/zxMX369IiI3rotVu/PPffckJdxOLv99tujtbU1Vq9evctz6nlwPPPMM7F8+fJYtGhRfOELX4hHHnkkLrrooqipqYm5c+eq50H0+c9/Pjo6OmLq1KlRWVkZ27dvj6985SvxyU9+MiL8TpfCQOp0/fr1UV1dHe94xzt22cdn5cAJgClUKBT6fJ0kyS7b2DsXXHBB/Pu//3s89NBDuzyn3vfNunXr4uKLL4777rsvamtrd7ufet433d3dMWPGjPi7v/u7iIh43/veF48//ngsX7485s6d27ufet53K1eujFtuuSW+973vxXve855Yu3ZtLFy4MCZOnBhnn312737qevDtTZ2q9z2jCzhFxo0bF5WVlbtcwWzYsGGXqyH23IUXXhj33HNP/PSnP41Jkyb1bh8/fnxEhHrfR62trbFhw4ZobGyMqqqqqKqqivvvvz+uvfbaqKqq6q1L9bxvJkyYEH/8x3/cZ9u0adOira0tIvw+D6bLLrssLr/88viLv/iLeO973xtnnXVWXHLJJdHc3BwR6roUBlKn48ePj61bt8bvf//73e7DHyYApkh1dXU0NjZGS0tLn+0tLS0xa9asMpVq+EuSJC644IK4++6745/+6Z+ioaGhz/MNDQ0xfvz4PvW+devWuP/++9X7Hvjwhz8cjz32WKxdu7b3MWPGjPjUpz4Va9eujcMPP1w9D4Ljjjtul2WMnnrqqZgyZUpE+H0eTFu2bImKir4fk5WVlb3LwKjrwTeQOm1sbIwRI0b02ae9vT1++ctfqvc9UbbpJxTVswzMjTfemDzxxBPJwoULk/322y/57W9/W+6iDVuf+9znkrq6uuSf//mfk/b29t7Hli1beve5+uqrk7q6uuTuu+9OHnvsseSTn/ykpRwGwVtnASeJeh4MjzzySFJVVZV85StfSZ5++unk1ltvTUaNGpXccsstvfuo58Fx9tlnJ4ccckjvMjB33313Mm7cuORv/uZvevdR13tu8+bNyZo1a5I1a9YkEZFcc801yZo1a3qXOxtInc6fPz+ZNGlS8uMf/zh59NFHk5NPPtkyMHtIAEyhb33rW8mUKVOS6urq5JhjjuldroS9ExFFHzfffHPvPt3d3ckVV1yRjB8/PqmpqUk++MEPJo899lj5Cp0Rbw+A6nlw/PCHP0ymT5+e1NTUJFOnTk2uv/76Ps+r58HR2dmZXHzxxcmhhx6a1NbWJocffniyZMmSpKurq3cfdb3nfvrTnxZ9Tz777LOTJBlYnb722mvJBRdckBx44IHJyJEjkz/90z9N2trayvBqhq9CkiRJedoeAQAoB2MAAQByRgAEAMgZARAAIGcEQACAnBEAAQByRgAEAMgZARAAIGcEQACAnBEAAQByRgAEAMgZARAAIGcEQACAnBEAAQByRgAEAMgZARAAIGcEQACAnBEAAQByRgAEAMgZARAAIGcEQACAnBEAAQByRgAEAMgZARAAIGcEQACAnBEAAQByRgAEAMgZARAAIGcEQACAnBEAAQByRgAEAMgZARAAIGf+P28i46POjEexAAAAAElFTkSuQmCC", - "text/html": [ - "\n", - "
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c_itse.calculate_error(PV_vec_itse[i-1])\n", - " c_itae.calculate_error(PV_vec_itae[i-1])\n", - "\n", - " if i == 500:\n", - " c_ise.SP = SP_1\n", - " c_iae.SP = SP_1\n", - " c_itse.SP = SP_1\n", - " c_itae.SP = SP_1\n", - "\n", - " PV_vec_ise[i] = PV_vec_ise[i-1]+c_ise.get_control_variable()\n", - " PV_vec_iae[i] = PV_vec_iae[i-1]+c_iae.get_control_variable()\n", - " PV_vec_itse[i] = PV_vec_itse[i-1]+c_itse.get_control_variable()\n", - " PV_vec_itae[i] = PV_vec_itae[i-1]+c_itae.get_control_variable()\n", - "\n", - "fig5 = plt.figure()\n", - "plt.plot(t_vec,PV_vec_ise,label='ise')\n", - "plt.plot(t_vec,PV_vec_iae,'.-',label='iae')\n", - "plt.plot(t_vec,PV_vec_itse,'+',label='itse')\n", - "plt.plot(t_vec,PV_vec_itae,'*',label='itae')" + "fig2 = plt.figure()\n", + "plt.plot(t_vec,Pegelregler.error_history[1:])" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[8. 8. 8. ... 7.49916924 7.49916924 7.49916924]\n" + ] + } + ], + "source": [ + "print(level_vec[:])" ] } ], diff --git a/Regler/regler_test_optimierung.ipynb b/Regler/regler_test_optimierung.ipynb new file mode 100644 index 0000000..7bf1ed9 --- /dev/null +++ b/Regler/regler_test_optimierung.ipynb @@ -0,0 +1,386 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "from Regler_class_file import P_controller_class\n", + "from Regler_class_file import PI_controller_class\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "# # %matplotlib widget\n", + "# pi_controller = PI_controller_class(setpoint=100,proportionality_constant=1,Ti = 30,timestep = 0.1)\n", + "\n", + "# t_max = 100 #s\n", + "# dt = 0.1 #s\n", + "# nt = int(t_max//dt)\n", + "# t_vec = np.arange(0,nt+1,1)*dt\n", + "\n", + "# PV_0 = 50\n", + "\n", + "# PV_vec = np.full_like(t_vec,PV_0)\n", + "\n", + "# for i in range(1,nt+1):\n", + "# pi_controller.calculate_error(PV_vec[i-1])\n", + "\n", + "# if i == 500:\n", + "# pi_controller.SP = 0.\n", + "# PV_vec[i] = PV_vec[i-1]+pi_controller.get_control_variable()\n", + "\n", + "\n", + "\n", + "# plt.plot(t_vec,PV_vec,'.')" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "SP_0 = 1.1\n", + "SP_1 = 0.24\n", + "PV_0 = 0.5" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "source": [ + "n = 10\n", + "Kp_max = 5.\n", + "Ti_max = 5.\n", + "d_Kp = Kp_max/n\n", + "d_Ti = Ti_max/n\n", + "kp_vec = np.arange(1,n+1,1)*d_Kp\n", + "Ti_vec = np.arange(1,n+1,1)*d_Ti\n", + "\n", + "XX,YY = np.meshgrid(kp_vec,Ti_vec)\n", + "\n", + "ise_mat = np.empty_like(XX)\n", + "iae_mat = np.empty_like(XX)\n", + "itse_mat = np.empty_like(XX)\n", + "itae_mat = np.empty_like(XX)\n", + "\n", + "\n", + "t_max = 100 #s\n", + "dt = 0.05 #s\n", + "nt = int(t_max//dt)\n", + "t_vec = np.arange(0,nt+1,1)*dt\n", + "\n", + "for i in range(n):\n", + " if i%10 == 0:\n", + " print(i)\n", + " for j in range(n):\n", + " Kp = XX[i,j]\n", + " Ti = YY[i,j]\n", + "\n", + " c = PI_controller_class(SP_0,Kp,Ti,dt)\n", + "\n", + " PV_vec = np.full_like(t_vec,PV_0)\n", + "\n", + " for t in range(1,nt+1):\n", + " c.calculate_error(PV_vec[t-1])\n", + "\n", + " if t == 500:\n", + " c.SP = SP_1\n", + " PV_vec[t] = PV_vec[t-1]+c.get_control_variable()\n", + " \n", + " ise_mat[i,j],iae_mat[i,j],itse_mat[i,j],itae_mat[i,j] = np.log(c.get_performance_indicators())\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + 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plt.subplots(1,1)\n", + "itae_min = np.min(itae_mat)\n", + "itae_max = np.max(itae_mat)\n", + "mesh4 = axs4.pcolormesh(XX,YY,itae_mat,cmap='RdBu', vmin=itae_min, vmax=itae_max,shading='nearest')\n", + "fig4.colorbar(mesh4)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "ise_ind = np.unravel_index(np.argmin(ise_mat,axis=None),ise_mat.shape)\n", + "Kp_ise = XX[ise_ind]\n", + "Ti_ise = YY[ise_ind]\n", + "iae_ind = np.unravel_index(np.argmin(iae_mat,axis=None),iae_mat.shape)\n", + "Kp_iae = XX[iae_ind]\n", + "Ti_iae = YY[iae_ind]\n", + "itse_ind = np.unravel_index(np.argmin(itse_mat,axis=None),itse_mat.shape)\n", + "Kp_itse = XX[itse_ind]\n", + "Ti_itse = YY[itse_ind]\n", + "itae_ind = np.unravel_index(np.argmin(itae_mat,axis=None),itae_mat.shape)\n", + "Kp_itae = XX[itae_ind]\n", + "Ti_itae = YY[itae_ind]\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "05f5663bd9ab4b23a30ec4ee94e41a1e", + "version_major": 2, + "version_minor": 0 + }, + "image/png": 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", + "text/html": [ + "\n", + "
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\n", + " Figure\n", + "
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c_itse.calculate_error(PV_vec_itse[i-1])\n", + " c_itae.calculate_error(PV_vec_itae[i-1])\n", + "\n", + " if i == 500:\n", + " c_ise.SP = SP_1\n", + " c_iae.SP = SP_1\n", + " c_itse.SP = SP_1\n", + " c_itae.SP = SP_1\n", + "\n", + " PV_vec_ise[i] = PV_vec_ise[i-1]+c_ise.get_control_variable()\n", + " PV_vec_iae[i] = PV_vec_iae[i-1]+c_iae.get_control_variable()\n", + " PV_vec_itse[i] = PV_vec_itse[i-1]+c_itse.get_control_variable()\n", + " PV_vec_itae[i] = PV_vec_itae[i-1]+c_itae.get_control_variable()\n", + "\n", + "fig5 = plt.figure()\n", + "plt.plot(t_vec,PV_vec_ise,label='ise')\n", + "plt.plot(t_vec,PV_vec_iae,'.-',label='iae')\n", + "plt.plot(t_vec,PV_vec_itse,'+',label='itse')\n", + "plt.plot(t_vec,PV_vec_itae,'*',label='itae')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3.8.13 ('Georg_DT_Slot3')", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/Untertweng_mit_Pegelregler.ipynb b/Untertweng_mit_Pegelregler.ipynb index 540e385..0d5d5e7 100644 --- a/Untertweng_mit_Pegelregler.ipynb +++ b/Untertweng_mit_Pegelregler.ipynb @@ -296,7 +296,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3.8.13 ('DT_Slot_3')", + "display_name": "Python 3.8.13 ('Georg_DT_Slot3')", "language": "python", "name": "python3" }, @@ -315,7 +315,7 @@ "orig_nbformat": 4, "vscode": { "interpreter": { - "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48" + "hash": "84fb123bdc47ab647d3782661abcbe80fbb79236dd2f8adf4cef30e8755eb2cd" } } }, From 9204729d0bfc9dec5a0283c4b50a321f4fe3b1d2 Mon Sep 17 00:00:00 2001 From: Brantegger Georg Date: Mon, 25 Jul 2022 10:25:06 +0200 Subject: [PATCH 12/12] pre-merge commit --- Pegelregler_test.ipynb | 274 --------------------------------------- Regler/regler_test.ipynb | 22 ++-- 2 files changed, 11 insertions(+), 285 deletions(-) delete mode 100644 Pegelregler_test.ipynb diff --git a/Pegelregler_test.ipynb b/Pegelregler_test.ipynb deleted file mode 100644 index dd20068..0000000 --- a/Pegelregler_test.ipynb +++ /dev/null @@ -1,274 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "from functions.pressure_conversion import pressure_conversion\n", - "from Ausgleichsbecken.Ausgleichsbecken_class_file import Ausgleichsbecken_class\n", - "from Regler.Regler_class_file import PI_controller_class" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "# define reservoir constants\n", - "initial_level = 5. # m\n", - "initial_influx = 1. # m³/s\n", - "initial_outflux = 0. # m³/s\n", - "initial_pipeline_pressure = 5.\n", - "initial_pressure_unit = 'mWS'\n", - "conversion_pressure_unit = 'mWS'\n", - "\n", - "area_base = 1. # m²\n", - "area_outflux = 0.5 # m²\n", - "critical_level_low = 0. # m\n", - "critical_level_high = 10. # m\n", - "simulation_timestep = 0.001 # s\n", - "\n", - "# for while loop\n", - "total_min_level = 0.01 # m\n", - "total_max_time = 200 # s" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# define controller constants\n", - "target_level = 4.5 # m\n", - "Kp = 0.01\n", - "Ti = 120\n", - "\n", - "deadband_range = 0.1 # m\n", - "deadband_lo = target_level-deadband_range\n", - "deadband_hi = target_level+deadband_range\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "# define pressure modulation function\n", - "d_ppfun_max = +0.001\n", - "d_pp_fun_max_unit = 'mWS'\n", - "d_ppfun_min = -0.001\n", - "d_pp_fun_min_unit = 'mWS'\n", - "\n", - "d_pp_max,_ = pressure_conversion(d_ppfun_max,d_pp_fun_max_unit,'Pa')\n", - "d_pp_min,_ = pressure_conversion(d_ppfun_min,d_pp_fun_min_unit,'Pa')\n", - "\n", - "pp_fun_max = +1.1*target_level\n", - "pp_fun_max_unit = 'mWS'\n", - "pp_fun_min = +0.5*target_level\n", - "pp_fun_min_unit = 'mWS'\n", - "\n", - "pp_max,_ = pressure_conversion(pp_fun_max,pp_fun_max_unit,'Pa')\n", - "pp_min,_ = pressure_conversion(pp_fun_min,pp_fun_min_unit,'Pa')\n", - "\n", - "\n", - "def pipe_pressure_fun(p,control_variable,d_pp_max=d_pp_max,d_pp_min=d_pp_min):\n", - " cv = control_variable\n", - " if cv >= 0:\n", - " return_val = p+cv*d_pp_max\n", - " else:\n", - " return_val = p-cv*d_pp_min\n", - "\n", - " if return_val > pp_max:\n", - " return_val = pp_max\n", - " elif return_val < pp_min:\n", - " return_val = pp_min\n", - " \n", - " return return_val\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "c = PI_controller_class(target_level,Kp,Ti,simulation_timestep)" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "V = Ausgleichsbecken_class(area_base, area_outflux, critical_level_low, critical_level_high,simulation_timestep)\n", - "V.set_initial_level(initial_level) \n", - "V.set_influx(initial_influx)\n", - "V.set_outflux(initial_outflux)\n", - "\n", - "converted_pressure,_ = pressure_conversion(initial_pipeline_pressure,input_unit = initial_pressure_unit, target_unit = 'Pa')\n", - "V.pressure = converted_pressure\n", - "\n", - "time_vec = np.arange(0,total_max_time,simulation_timestep)\n", - "outflux_vec = np.empty_like(time_vec)\n", - "outflux_vec[0] = initial_outflux\n", - "level_vec = np.empty_like(time_vec)\n", - "level_vec[0] = initial_level" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "i_max = -1\n", - "\n", - "pressure_vec = np.full_like(time_vec,V.pressure)\n", - "\n", - "for i in range(np.size(time_vec)-1):\n", - " # update to include p_halfstep\n", - "\n", - " if time_vec[i] >= 25 and time_vec[i] < 35:\n", - " V.influx = 1+(4-1)/10*(time_vec[i]-25)\n", - "\n", - " if time_vec[i] >= 70 and time_vec[i] < 100:\n", - " V.influx = 4-(4-0.2)/30*(time_vec[i]-70)\n", - "\n", - "\n", - " c.calculate_error(V.level)\n", - "\n", - " if abs(c.error) > deadband_range:\n", - " cv = c.get_control_variable()\n", - " V.pressure = pipe_pressure_fun(V.pressure,cv)\n", - "\n", - " pressure_vec[i+1] = V.pressure\n", - " V.e_RK_4()\n", - " V.level = V.update_level(V.timestep)\n", - " V.set_volume()\n", - " outflux_vec[i+1] = V.outflux\n", - " level_vec[i+1] = V.level\n", - " if V.level < total_min_level:\n", - " i_max = i\n", - " break\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "d0ac8027f1fd435da07e6b46adf3a810", - "version_major": 2, - "version_minor": 0 - }, - "image/png": 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", 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\n", - " " - ], - "text/plain": [ - "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "%matplotlib widget\n", - "\n", - "fig1, (ax1, ax2, ax3, ax4) = plt.subplots(4, 1)\n", - "fig1.set_figheight(10)\n", - "fig1.suptitle('Ausgleichsbecken')\n", - "\n", - "ax1.plot(time_vec[:i_max],level_vec[:i_max], label='Water level')\n", - "ax1.set_ylabel(r'$h$ ['+V.level_unit+']')\n", - "ax1.set_xlabel(r'$t$ ['+V.time_unit+']')\n", - "ax1.legend()\n", - "\n", - "ax2.plot(time_vec[:i_max],outflux_vec[:i_max], label='Outflux')\n", - "ax2.set_ylabel(r'$Q_{out}$ ['+V.flux_unit+']')\n", - "ax2.set_xlabel(r'$t$ ['+V.time_unit+']')\n", - "ax2.legend()\n", - "\n", - "ax3.plot(time_vec[:i_max],pressure_conversion(pressure_vec[:i_max],'Pa',conversion_pressure_unit)[0], label='Pipeline pressure at reservoir')\n", - "ax3.set_ylabel(r'$p_{pipeline}$ ['+conversion_pressure_unit+']')\n", - "ax3.set_xlabel(r'$t$ ['+V.time_unit+']')\n", - "ax3.legend()\n", - "\n", - "# plt.subplots_adjust(left=0.2, bottom=0.2)\n", - "ax4.set_axis_off()\n", - "cell_text = np.array([[initial_level, V.level_unit], \\\n", - " [initial_influx, V.flux_unit], \\\n", - " [initial_outflux, V.flux_unit], \\\n", - " [simulation_timestep, V.time_unit], \\\n", - " [area_base, V.area_unit], \\\n", - " [area_outflux, V.area_unit]])\n", - "\n", - "row_labels =['initial_level', \\\n", - " 'initial_influx', \\\n", - " 'initial_outflux', \\\n", - " 'simulation_timestep', \\\n", - " 'area_base', \\\n", - " 'area_outflux']\n", - "\n", - "plt.table(cellText=cell_text, \\\n", - " cellLoc='center', \\\n", - " colWidths=[0.3,0.1,0.3], \\\n", - " rowLabels=row_labels, \\\n", - " loc = 1, \\\n", - " rowLoc='left', \\\n", - " fontsize = 15.)\n", - "\n", - "fig1.tight_layout() " - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3.8.13 ('DT_Slot_3')", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.8.13" - }, - "orig_nbformat": 4, - "vscode": { - "interpreter": { - "hash": "4a28055eb8a3160fa4c7e4fca69770c4e0a1add985300856aa3fcf4ce32a2c48" - } - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Regler/regler_test.ipynb b/Regler/regler_test.ipynb index c8c44b1..bad0931 100644 --- a/Regler/regler_test.ipynb +++ b/Regler/regler_test.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 26, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -23,7 +23,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -69,7 +69,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -87,7 +87,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -101,7 +101,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 38, "metadata": {}, "outputs": [ { @@ -244,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -276,16 +276,16 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[]" ] }, - "execution_count": 32, + "execution_count": 40, "metadata": {}, "output_type": "execute_result" } @@ -297,14 +297,14 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 41, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "[8. 8. 8. ... 7.49916924 7.49916924 7.49916924]\n" + "[8. 8. 8. ... 7.21126138 7.21126138 7.21126138]\n" ] } ],