Python-DT_Slot_3/Ausgleichsbecken/Ausgleichsbecken_test_steady_state.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from Ausgleichsbecken_class_file import Ausgleichsbecken_class\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('Main_Programm.ipynb'))\n",
    "parent = os.path.dirname(current)\n",
    "sys.path.append(parent)\n",
    "from functions.pressure_conversion import pressure_conversion"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# define constants\n",
    "\n",
    "    # for physics\n",
    "g                   = 9.81                                          # [m/s²]    gravitational acceleration \n",
    "rho                 = 1000.                                         # [kg/m³]   density of water \n",
    "pUnit_calc          = 'Pa'                                          # [text]    DO NOT CHANGE! for pressure conversion in print statements and plot labels \n",
    "pUnit_conv          = 'mWS'                                         # [text]    for pressure conversion in print statements and plot labels\n",
    "\n",
    "    # for Turbine\n",
    "Tur_Q_nenn          = 0.85                                          # [m³/s]    nominal flux of turbine \n",
    "Tur_p_nenn          = pressure_conversion(10.6,'bar',pUnit_calc)    # [Pa]      nominal pressure of turbine \n",
    "Tur_closingTime     = 90.                                           # [s]       closing time of turbine\n",
    "\n",
    "    # for PI controller\n",
    "Con_targetLevel     = 8.                                            # [m]\n",
    "Con_K_p             = 0.1                                           # [-]       proportional constant of PI controller\n",
    "Con_T_i             = 10.                                           # [s]       timespan in which a steady state error is corrected by the intergal term\n",
    "Con_deadbandRange   = 0.05                                          # [m]       Deadband range around targetLevel for which the controller does NOT intervene\n",
    "\n",
    "    # for pipeline\n",
    "Pip_length          = (535.+478.)                                   # [m]       length of pipeline\n",
    "Pip_dia             = 0.9                                           # [m]       diameter of pipeline\n",
    "Pip_area            = Pip_dia**2/4*np.pi                            # [m²]      crossectional area of pipeline\n",
    "Pip_head            = 105.                                          # [m]       hydraulic head of pipeline without reservoir\n",
    "Pip_angle           = np.arcsin(Pip_head/Pip_length)                # [rad]     elevation angle of pipeline \n",
    "Pip_n_seg           = 50                                            # [-]       number of pipe segments in discretization\n",
    "Pip_f_D             = 0.014                                         # [-]       Darcy friction factor\n",
    "Pip_pw_vel          = 500.                                          # [m/s]     propagation velocity of the pressure wave (pw) in the given pipeline\n",
    "    # derivatives of the pipeline constants\n",
    "Pip_dx              = Pip_length/Pip_n_seg                          # [m]       length of each pipe segment\n",
    "Pip_dt              = Pip_dx/Pip_pw_vel                             # [s]       timestep according to method of characteristics\n",
    "Pip_nn              = Pip_n_seg+1                                   # [1]       number of nodes\n",
    "Pip_x_vec           = np.arange(0,Pip_nn,1)*Pip_dx                  # [m]       vector holding the distance of each node from the upstream reservoir along the pipeline\n",
    "Pip_h_vec           = np.arange(0,Pip_nn,1)*Pip_head/Pip_n_seg      # [m]       vector holding the vertival distance of each node from the upstream reservoir\n",
    "\n",
    "    # for reservoir\n",
    "Res_area_base       = 74.                                           # [m²]      total base are of the cuboid reservoir   \n",
    "Res_area_out        = Pip_area                                      # [m²]      outflux area of the reservoir, given by pipeline area\n",
    "Res_level_crit_lo   = 0.                                            # [m]       for yet-to-be-implemented warnings\n",
    "Res_level_crit_hi   = np.inf                                        # [m]       for yet-to-be-implemented warnings\n",
    "Res_dt_approx       = 1e-3                                          # [s]       approx. timestep of reservoir time evolution to ensure numerical stability (see Res_nt why approx.)\n",
    "Res_nt              = max(1,int(Pip_dt//Res_dt_approx))             # [1]       number of timesteps of the reservoir time evolution within one timestep of the pipeline\n",
    "Res_dt              = Pip_dt/Res_nt                                 # [s]       harmonised timestep of reservoir time evolution\n",
    "\n",
    "    # for general simulation\n",
    "flux_init           = Tur_Q_nenn/1.1                                # [m³/s]    initial flux through whole system for steady state initialization  \n",
    "level_init          = Con_targetLevel                               # [m]       initial water level in upstream reservoir for steady state initialization\n",
    "simTime_target      = 600.                                          # [s]       target for total simulation time (will vary slightly to fit with Pip_dt)\n",
    "nt                  = int(simTime_target//Pip_dt)                   # [1]       Number of timesteps of the whole system\n",
    "t_vec               = np.arange(0,nt+1,1)*Pip_dt                    # [s]       time vector. At each step of t_vec the system parameters are stored\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The cuboid reservoir has the following attributes: \n",
      "----------------------------- \n",
      "Base area             =       74.0       m² \n",
      "Outflux area          =       0.636      m² \n",
      "Current level         =       -inf       m\n",
      "Critical level low    =       0.0        m \n",
      "Critical level high   =       inf        m \n",
      "Volume in reservoir   =       -inf       m³ \n",
      "Current influx        =       -inf       m³/s \n",
      "Current outflux       =       -inf       m³/s \n",
      "Current outflux vel   =       -inf       m/s \n",
      "Current pipe pressure =       -inf       mWS \n",
      "Simulation timestep   =       0.001013   s \n",
      "Density of liquid     =       1000.0     kg/m³ \n",
      "----------------------------- \n",
      "\n",
      "The cuboid reservoir has the following attributes: \n",
      "----------------------------- \n",
      "Base area             =       74.0       m² \n",
      "Outflux area          =       0.636      m² \n",
      "Current level         =       8.0        m\n",
      "Critical level low    =       0.0        m \n",
      "Critical level high   =       inf        m \n",
      "Volume in reservoir   =       592.0      m³ \n",
      "Current influx        =       0.773      m³/s \n",
      "Current outflux       =       0.773      m³/s \n",
      "Current outflux vel   =       1.215      m/s \n",
      "Current pipe pressure =       7.854      mWS \n",
      "Simulation timestep   =       0.001013   s \n",
      "Density of liquid     =       1000.0     kg/m³ \n",
      "----------------------------- \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# create objects\n",
    "\n",
    "# Upstream reservoir\n",
    "reservoir = Ausgleichsbecken_class(Res_area_base,Res_area_out,Res_dt,pUnit_conv,Res_level_crit_lo,Res_level_crit_hi,rho)\n",
    "# print(reservoir.__init__.__doc__)\n",
    "reservoir.get_info(full=True)\n",
    "reservoir.set_steady_state(flux_init,level_init)\n",
    "reservoir.get_info(full=True)\n",
    "\n",
    "# initialize vectors\n",
    "outflux_vec     = np.zeros_like(t_vec)\n",
    "outflux_vec[0]  = reservoir.get_current_outflux()\n",
    "level_vec       = np.zeros_like(t_vec)\n",
    "level_vec[0]    = reservoir.get_current_level()\n",
    "volume_vec      = np.zeros_like(t_vec)\n",
    "volume_vec[0]   = reservoir.get_current_volume()\n",
    "pressure_vec    = np.zeros_like(t_vec)\n",
    "pressure_vec[0] = reservoir.get_current_pressure()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The current attributes are: \n",
      "----------------------------- \n",
      "Current level         =       8.0        m\n",
      "Current volume        =       592.0      m³ \n",
      "Current influx        =       0.773      m³/s \n",
      "Current outflux       =       0.773      m³/s \n",
      "Current outflux vel   =       1.215      m/s \n",
      "Current pipe pressure =       7.854      mWS \n",
      "----------------------------- \n",
      "\n"
     ]
    }
   ],
   "source": [
    "# time loop\n",
    "for i in range(1,nt+1):\n",
    "    # if i == 500:\n",
    "    #     reservoir.set_influx(0.)\n",
    "    reservoir.set_pressure(pressure_vec[i-1],display_warning=False)\n",
    "    reservoir.set_outflux(outflux_vec[i-1],display_warning=False)\n",
    "    for it_res in range(Res_nt):\n",
    "        reservoir.timestep_reservoir_evolution()   \n",
    "        \n",
    "    outflux_vec[i] = reservoir.get_current_outflux()\n",
    "    level_vec[i]   = reservoir.get_current_level()\n",
    "    pressure_vec[i]   = reservoir.get_current_pressure()\n",
    "\n",
    "reservoir.get_info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3987c69b8123480db34b021e1a5393a0",
       "version_major": 2,
       "version_minor": 0
      },
      "image/png": "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",
      "text/html": [
       "\n",
       "            <div style=\"display: inline-block;\">\n",
       "                <div class=\"jupyter-widgets widget-label\" style=\"text-align: center;\">\n",
       "                    Figure\n",
       "                </div>\n",
       "                <img src='data:image/png;base64,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' width=640.0/>\n",
       "            </div>\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, (ax1, ax2, ax3) = plt.subplots(3, 1)\n",
    "fig1.set_figheight(10)\n",
    "fig1.suptitle('Ausgleichsbecken')\n",
    "\n",
    "ax1.plot(t_vec,level_vec, label='Water level')\n",
    "ax1.set_ylabel(r'$h$ ['+reservoir.level_unit+']')\n",
    "ax1.set_xlabel(r'$t$ ['+reservoir.time_unit+']')\n",
    "ax1.legend()\n",
    "\n",
    "ax2.plot(t_vec,outflux_vec, label='Outflux')\n",
    "ax2.set_ylabel(r'$Q_{out}$ ['+reservoir.flux_unit+']')\n",
    "ax2.set_xlabel(r'$t$ ['+reservoir.time_unit+']')\n",
    "ax2.legend()\n",
    "\n",
    "ax3.plot(t_vec,pressure_conversion(pressure_vec,'Pa',pUnit_conv), label='Pipeline pressure at reservoir')\n",
    "ax3.set_ylabel(r'$p_{pipeline}$ ['+pUnit_conv+']')\n",
    "ax3.set_xlabel(r'$t$ ['+reservoir.time_unit+']')\n",
    "ax3.legend()\n",
    "\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
}