{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import sys\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from Ausgleichsbecken_class_file import Ausgleichsbecken_class\n",
    "\n",
    "#importing pressure conversion function\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": 23,
   "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          = 1000.                                         # [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      = 1800.                                          # [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": 24,
   "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   =       0.001      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   =       0.001      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",
    "influx_vec      = np.full_like(t_vec,flux_init)\n",
    "influx_vec[np.argmin(np.abs(t_vec-1200.)):] = 0.\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.full_like(t_vec,reservoir.get_current_pressure())\n",
    "pressure_vec[np.argmin(np.abs(t_vec-1200.)):] = 0."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "ename": "Exception",
     "evalue": "Reservoir ran emtpy",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mException\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32my:\\KELAG\\KS\\KS-PW\\04 Digitalisierung\\KSPWDEV Server\\Digital Trainee Projekt\\DT_Slot_3_Project_Repo\\Ausgleichsbecken\\Ausgleichsbecken_test_steady_state.ipynb Cell 4\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      <a href='vscode-notebook-cell:/y%3A/KELAG/KS/KS-PW/04%20Digitalisierung/KSPWDEV%20Server/Digital%20Trainee%20Projekt/DT_Slot_3_Project_Repo/Ausgleichsbecken/Ausgleichsbecken_test_steady_state.ipynb#W3sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m reservoir\u001b[39m.\u001b[39mset_outflux(outflux_vec[i\u001b[39m-\u001b[39m\u001b[39m1\u001b[39m],display_warning\u001b[39m=\u001b[39m\u001b[39mFalse\u001b[39;00m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/y%3A/KELAG/KS/KS-PW/04%20Digitalisierung/KSPWDEV%20Server/Digital%20Trainee%20Projekt/DT_Slot_3_Project_Repo/Ausgleichsbecken/Ausgleichsbecken_test_steady_state.ipynb#W3sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m \u001b[39mfor\u001b[39;00m it_res \u001b[39min\u001b[39;00m \u001b[39mrange\u001b[39m(Res_nt):\n\u001b[1;32m----> <a href='vscode-notebook-cell:/y%3A/KELAG/KS/KS-PW/04%20Digitalisierung/KSPWDEV%20Server/Digital%20Trainee%20Projekt/DT_Slot_3_Project_Repo/Ausgleichsbecken/Ausgleichsbecken_test_steady_state.ipynb#W3sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m     reservoir\u001b[39m.\u001b[39;49mtimestep_reservoir_evolution()   \n\u001b[0;32m      <a href='vscode-notebook-cell:/y%3A/KELAG/KS/KS-PW/04%20Digitalisierung/KSPWDEV%20Server/Digital%20Trainee%20Projekt/DT_Slot_3_Project_Repo/Ausgleichsbecken/Ausgleichsbecken_test_steady_state.ipynb#W3sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m outflux_vec[i] \u001b[39m=\u001b[39m reservoir\u001b[39m.\u001b[39mget_current_outflux()\n\u001b[0;32m     <a href='vscode-notebook-cell:/y%3A/KELAG/KS/KS-PW/04%20Digitalisierung/KSPWDEV%20Server/Digital%20Trainee%20Projekt/DT_Slot_3_Project_Repo/Ausgleichsbecken/Ausgleichsbecken_test_steady_state.ipynb#W3sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m level_vec[i]   \u001b[39m=\u001b[39m reservoir\u001b[39m.\u001b[39mget_current_level()\n",
      "File \u001b[1;32my:\\KELAG\\KS\\KS-PW\\04 Digitalisierung\\KSPWDEV Server\\Digital Trainee Projekt\\DT_Slot_3_Project_Repo\\Ausgleichsbecken\\Ausgleichsbecken_class_file.py:269\u001b[0m, in \u001b[0;36mAusgleichsbecken_class.timestep_reservoir_evolution\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    267\u001b[0m yn      \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39moutflux\u001b[39m/\u001b[39mA_a \u001b[39m# outflux velocity\u001b[39;00m\n\u001b[0;32m    268\u001b[0m h       \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mlevel\n\u001b[1;32m--> 269\u001b[0m h_hs    \u001b[39m=\u001b[39m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mupdate_level(dt\u001b[39m/\u001b[39;49m\u001b[39m2\u001b[39;49m)\n\u001b[0;32m    270\u001b[0m p       \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpressure\n\u001b[0;32m    271\u001b[0m p_hs    \u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpressure \u001b[39m+\u001b[39m rho\u001b[39m*\u001b[39mg\u001b[39m*\u001b[39m(h_hs\u001b[39m-\u001b[39mh)\n",
      "File \u001b[1;32my:\\KELAG\\KS\\KS-PW\\04 Digitalisierung\\KSPWDEV Server\\Digital Trainee Projekt\\DT_Slot_3_Project_Repo\\Ausgleichsbecken\\Ausgleichsbecken_class_file.py:229\u001b[0m, in \u001b[0;36mAusgleichsbecken_class.update_level\u001b[1;34m(self, timestep, set_flag)\u001b[0m\n\u001b[0;32m    227\u001b[0m \u001b[39m# raise exception error if level in reservoir falls below 0.01 ######################### has to be commented out if used in loop\u001b[39;00m\n\u001b[0;32m    228\u001b[0m \u001b[39mif\u001b[39;00m level_new \u001b[39m<\u001b[39m \u001b[39m0.01\u001b[39m:\n\u001b[1;32m--> 229\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mException\u001b[39;00m(\u001b[39m'\u001b[39m\u001b[39mReservoir ran emtpy\u001b[39m\u001b[39m'\u001b[39m)\n\u001b[0;32m    230\u001b[0m \u001b[39m# set flag is necessary because update_level() is used to get a halfstep value in the time evoultion\u001b[39;00m\n\u001b[0;32m    231\u001b[0m \u001b[39mif\u001b[39;00m set_flag \u001b[39m==\u001b[39m \u001b[39mTrue\u001b[39;00m:\n",
      "\u001b[1;31mException\u001b[0m: Reservoir ran emtpy"
     ]
    }
   ],
   "source": [
    "# time loop\n",
    "for i in range(1,nt+1):\n",
    "    reservoir.set_influx(influx_vec[i])\n",
    "    reservoir.set_pressure(pressure_vec[i],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": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "model_id": "17386e67a1764773b422e03d763db868",
       "version_major": 2,
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      "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 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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[:i],level_vec[:i], 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[:i],outflux_vec[:i], 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[:i],pressure_conversion(pressure_vec[:i],'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
}