AutoIgnition Delay For Xylene.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### AutoIgnition Delay for 0.625 % R-Xylene in Batch Reactor Using the Chemkin Format File"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Runnning Cantera version: 2.5.1\n"
     ]
    }
   ],
   "source": [
    "from __future__ import division\n",
    "from __future__ import print_function\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import time\n",
    "\n",
    "import cantera as ct\n",
    "print('Runnning Cantera version: ' + ct.__version__)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the Ideal Gas Reactor "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "gas = ct.Solution('xylene.cti')\n",
    "\n",
    "reactorTemperature = 1500 #Kelvin\n",
    "reactorPressure = 8*101325.0 #Pascals\n",
    "\n",
    "gas.TPX = reactorTemperature, reactorPressure , 'RXYLENE: 0.00625, o2 :0.06562, Ar:0.92812'\n",
    "\n",
    "\n",
    "r = ct.IdealGasReactor(contents=gas, name='Batch Reactor')\n",
    "reactorNetwork = ct.ReactorNet([r])\n",
    "\n",
    "\n",
    "stateVariableNames = [r.component_name(item) for item in range(r.n_vars)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Creating the Dataframes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "timeHistory = pd.DataFrame(columns=stateVariableNames)\n",
    "\n",
    "def ignitionDelay(df, species):\n",
    "    return df[species].idxmax()\n",
    "\n",
    "T = np.arange(1428.57, 1691.66,20)\n",
    "\n",
    "estimatedIgnitionDelayTimes = np.ones(len(T))\n",
    "\n",
    "\n",
    "estimatedIgnitionDelayTimes[:6] = 6*[0.01]\n",
    "estimatedIgnitionDelayTimes[-2:] = 1\n",
    "estimatedIgnitionDelayTimes[-1] = 10\n",
    "\n",
    "\n",
    "ignitionDelays = pd.DataFrame(data={'T': T})\n",
    "ignitionDelays['ignDelay'] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Computed Ignition Delay: 1.635e-04 seconds for T=1428.57K. Took 20.12s to compute\n",
      "Computed Ignition Delay: 1.398e-04 seconds for T=1448.57K. Took 40.38s to compute\n",
      "Computed Ignition Delay: 1.197e-04 seconds for T=1468.57K. Took 18.63s to compute\n",
      "Computed Ignition Delay: 1.030e-04 seconds for T=1488.57K. Took 28.91s to compute\n",
      "Computed Ignition Delay: 8.921e-05 seconds for T=1508.57K. Took 26.45s to compute\n",
      "Computed Ignition Delay: 7.743e-05 seconds for T=1528.57K. Took 45.94s to compute\n",
      "Computed Ignition Delay: 6.731e-05 seconds for T=1548.57K. Took 28.21s to compute\n",
      "Computed Ignition Delay: 5.871e-05 seconds for T=1568.57K. Took 28.55s to compute\n",
      "Computed Ignition Delay: 5.150e-05 seconds for T=1588.57K. Took 44.34s to compute\n",
      "Computed Ignition Delay: 4.561e-05 seconds for T=1608.57K. Took 34.32s to compute\n",
      "Computed Ignition Delay: 4.036e-05 seconds for T=1628.57K. Took 35.48s to compute\n",
      "Computed Ignition Delay: 3.563e-05 seconds for T=1648.57K. Took 35.55s to compute\n",
      "Computed Ignition Delay: 3.171e-05 seconds for T=1668.57K. Took 32.82s to compute\n",
      "Computed Ignition Delay: 2.852e-05 seconds for T=1688.57K. Took 27.04s to compute\n"
     ]
    }
   ],
   "source": [
    "for i, temperature in enumerate(T):\n",
    "    reactorTemperature = temperature\n",
    "    reactorPressure = 6*101325.0\n",
    "    gas.TPX = reactorTemperature, reactorPressure,  'RXYLENE: 0.00625, o2 :0.06562, Ar:0.92812'\n",
    "    r = ct.IdealGasReactor(contents=gas, name='Batch Reactor')\n",
    "    reactorNetwork = ct.ReactorNet([r])\n",
    "\n",
    "    timeHistory = pd.DataFrame(columns=timeHistory.columns)\n",
    "\n",
    "    t0 = time.time()\n",
    "\n",
    "    t = 0\n",
    "    counter = 0\n",
    "    while t < estimatedIgnitionDelayTimes[i]:\n",
    "        t = reactorNetwork.step()\n",
    "        if not counter % 20:\n",
    "            timeHistory.loc[t] = r.get_state()\n",
    "        counter += 1\n",
    "\n",
    "    tau = ignitionDelay(timeHistory, 'OH')\n",
    "    t1 = time.time()\n",
    "\n",
    "    print('Computed Ignition Delay: {:.3e} seconds for T={}K. Took {:3.2f}s to compute'.format(tau, temperature, t1-t0))\n",
    "\n",
    "    ignitionDelays.at[i, 'ignDelay'] = tau"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Ploting and DataFraming the Ignition Delay with Temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'For RXylene, For 0.625%')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.semilogy(1000/ignitionDelays['T'], (ignitionDelays['ignDelay']*(1000000)), '+-',color ='r')\n",
    "ax.set_ylabel('Auto-Ignition Delay (micro-s)')\n",
    "ax.set_xlabel(r'$\\frac{1000}{T (K)}$', fontsize=18)\n",
    "ignitionDelays['ignDelay']*(1000000)\n",
    "ax.set_ylim([1, 10000])\n",
    "ax.legend(['R-xylene, Conc 0.625%'])\n",
    "ax.set_title(\"For RXylene, For 0.625%\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>T</th>\n",
       "      <th>ignDelay</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1428.57</td>\n",
       "      <td>0.000164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1448.57</td>\n",
       "      <td>0.000140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1468.57</td>\n",
       "      <td>0.000120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1488.57</td>\n",
       "      <td>0.000103</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1508.57</td>\n",
       "      <td>0.000089</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1528.57</td>\n",
       "      <td>0.000077</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1548.57</td>\n",
       "      <td>0.000067</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1568.57</td>\n",
       "      <td>0.000059</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1588.57</td>\n",
       "      <td>0.000051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1608.57</td>\n",
       "      <td>0.000046</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1628.57</td>\n",
       "      <td>0.000040</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1648.57</td>\n",
       "      <td>0.000036</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>1668.57</td>\n",
       "      <td>0.000032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1688.57</td>\n",
       "      <td>0.000029</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          T  ignDelay\n",
       "0   1428.57  0.000164\n",
       "1   1448.57  0.000140\n",
       "2   1468.57  0.000120\n",
       "3   1488.57  0.000103\n",
       "4   1508.57  0.000089\n",
       "5   1528.57  0.000077\n",
       "6   1548.57  0.000067\n",
       "7   1568.57  0.000059\n",
       "8   1588.57  0.000051\n",
       "9   1608.57  0.000046\n",
       "10  1628.57  0.000040\n",
       "11  1648.57  0.000036\n",
       "12  1668.57  0.000032\n",
       "13  1688.57  0.000029"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ignitionDelays"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}