{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cantera reactors\n",
    "\n",
    "* [Cantera reactor documentation](https://cantera.org/documentation/docs-2.5/sphinx/html/cython/zerodim.html)\n",
    "\n",
    "* [Cantera examples](https://cantera.org/examples/python/#python-example-reactors)\n",
    "    * These are also available in your cantera installation\n",
    "    * For example, at /usr/local/cantera/lib/python3.7/site-packages/cantera/examples/reactors\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CSTR Example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cantera as ct\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "P  = 101325\n",
    "\n",
    "Tin = 300                        # inlet state\n",
    "Xin = 'CH4:1, O2:2, N2:7.52'\n",
    "\n",
    "T0 = 2000.0                      # initial reactor state\n",
    "X0 = 'O2:0.21, N2:0.79'\n",
    "\n",
    "Tair = 300                       # exhaust/surrounding state\n",
    "Xair = 'O2:1, N2:3.76'\n",
    "\n",
    "Vcstr = 1                        # volume (m3)\n",
    "mdotIn = 1.767                   # kg/2\n",
    "nt = 100                         # number of time steps\n",
    "trun = 0.6                       # run time\n",
    "\n",
    "L_is_isoT = False                # true if isothermal, false if adiabatic\n",
    "\n",
    "#-------------------------------------------------------------------\n",
    "\n",
    "gasI      = ct.Solution('gri30.yaml')  # in\n",
    "gasI.TPX  = Tin, P, Xin\n",
    "\n",
    "gasE     = ct.Solution('gri30.yaml')   # exhaust\n",
    "gasE.TPX = Tair, P, Xair\n",
    "\n",
    "gas0     = ct.Solution('gri30.yaml')   # initial reactor\n",
    "gas0.TPX = T0, P, X0\n",
    "\n",
    "inlet    = ct.Reservoir(gasI)\n",
    "exhaust  = ct.Reservoir(gasE)\n",
    "\n",
    "cstr = ct.IdealGasReactor(gas0, energy='off') if L_is_isoT else ct.IdealGasReactor(gas0)\n",
    "cstr.volume = Vcstr\n",
    "\n",
    "mfcGas       = ct.MassFlowController(inlet, cstr, mdot=mdotIn)\n",
    "valveExhaust = ct.Valve(cstr, exhaust, K=1000.0)\n",
    "rxrNet       = ct.ReactorNet([cstr])\n",
    "\n",
    "pressures   = P*np.ones(nt)\n",
    "mdotExhaust = mdotIn*np.ones(nt)\n",
    "temps       = cstr.T*np.ones(nt)\n",
    "rhos        = cstr.thermo.density*np.ones(nt)\n",
    "y           = np.zeros((nt, gasI.n_species))\n",
    "x           = np.zeros((nt, gasI.n_species))\n",
    "\n",
    "y[0,:] = cstr.Y\n",
    "\n",
    "#---------- run the reactor\n",
    "\n",
    "nsteps = nt-1\n",
    "dt = trun/nsteps\n",
    "\n",
    "time = 0.0\n",
    "for i in range(nsteps) :\n",
    "    time = time + dt\n",
    "    rxrNet.advance(time)\n",
    "\n",
    "    temps[i+1]       = cstr.thermo.T\n",
    "    pressures[i+1]   = cstr.thermo.P\n",
    "    rhos[i+1]        = cstr.thermo.density\n",
    "    mdotExhaust[i+1] = valveExhaust.mass_flow_rate\n",
    "    y[i+1,:]         = cstr.thermo.Y\n",
    "    x[i+1,:]         = cstr.thermo.X\n",
    "    \n",
    "#---------- plot results\n",
    "\n",
    "times = np.linspace(0,trun,nt)\n",
    "\n",
    "plt.rc('font', size=14)\n",
    "\n",
    "plt.figure(figsize=(5,4))\n",
    "plt.plot(times, temps, '-')\n",
    "plt.xlabel('Time (s)')\n",
    "plt.ylabel('T (K)')\n",
    "\n",
    "plt.figure(figsize=(5,4))\n",
    "plt.plot(times,y[:,gasI.species_index(\"O2\")], label=\"O2\")\n",
    "plt.plot(times,y[:,gasI.species_index(\"CO2\")],label=\"CO2\")\n",
    "plt.plot(times,y[:,gasI.species_index(\"H2O\")],label=\"H2O\")\n",
    "plt.plot(times,y[:,gasI.species_index(\"CO\")], label=\"CO\")\n",
    "plt.xlabel('Time (s)')\n",
    "plt.ylabel('Y')\n",
    "plt.legend(frameon=False, loc='upper right');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Examine properties\n",
    "\n",
    "* Python's ```dir``` function is convenient to see a list of reactor properties\n",
    "    * Try out ```dir(cstr)```\n",
    "* ```cstr.thermo``` gives access to the underlying thermo object.\n",
    "    * For example: ```cstr.thermo.species_names``` or other properties that we would often access through ```gas```, as in ```gas.X```.\n",
    "        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['T',\n",
       " 'Y',\n",
       " '__class__',\n",
       " '__copy__',\n",
       " '__delattr__',\n",
       " '__dir__',\n",
       " '__doc__',\n",
       " '__eq__',\n",
       " '__format__',\n",
       " '__ge__',\n",
       " '__getattribute__',\n",
       " '__gt__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__init_subclass__',\n",
       " '__le__',\n",
       " '__lt__',\n",
       " '__ne__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '_add_inlet',\n",
       " '_add_outlet',\n",
       " '_add_wall',\n",
       " 'add_sensitivity_reaction',\n",
       " 'add_sensitivity_species_enthalpy',\n",
       " 'chemistry_enabled',\n",
       " 'component_index',\n",
       " 'component_name',\n",
       " 'density',\n",
       " 'energy_enabled',\n",
       " 'get_state',\n",
       " 'inlets',\n",
       " 'insert',\n",
       " 'kinetics',\n",
       " 'mass',\n",
       " 'n_vars',\n",
       " 'name',\n",
       " 'outlets',\n",
       " 'reactor_type',\n",
       " 'set_advance_limit',\n",
       " 'surfaces',\n",
       " 'syncState',\n",
       " 'thermo',\n",
       " 'type',\n",
       " 'volume',\n",
       " 'walls']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(cstr)\n",
    "# dir(cstr.thermo)\n",
    "# dir(valveExhaust)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
