Notes_IgDly_Example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all the typical toolboxes...\n",
    "from scipy.optimize import fsolve\n",
    "from scipy.optimize import least_squares\n",
    "from scipy.interpolate import interp1d\n",
    "from numpy import*\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import cantera as ct\n",
    "\n",
    "import time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Ignition delay computation\n",
    "In this example, we will illustrate how to setup and use a constant volume batch reactor. This reactor will then be used to compute the ignition delay of a gas at any temperature and pressure.\n",
    "\n",
    "The reactor (system) is simply an insulated box.\n",
    "\n",
    "The example here is based on the *official* example from here: https://www.cantera.org/examples/jupyter/reactors/batch_reactor_ignition_delay_NTC.ipynb.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Define igintion delay functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ignitionDelay(fuel,phi,rxT,rxP,ig_est):\n",
    "    \"\"\"\n",
    "    This function computes the ignition delay for a fuel/condition \n",
    "    from the occurence of the peak in species' concentration.\n",
    "    \n",
    "    This one outputs both ignition delay and the timehistory.\n",
    "    \"\"\"\n",
    "    # # Create our \"gas\" object from a mechanism\n",
    "    # gas = ct.Solution(mech)\n",
    "    gas.TP = rxT, rxP\n",
    "    gas.set_equivalence_ratio(phi=phi, fuel=fuel, oxidizer={'o2':1.0, 'n2':3.76})\n",
    "    # gas()\n",
    "    \n",
    "    # Create a batch reactor object and add it to a reactor network\n",
    "    # In this example, the batch reactor will be the only reactor\n",
    "    # in the network\n",
    "    r = ct.IdealGasReactor(contents=gas, name='Batch Reactor')\n",
    "    reactorNetwork = ct.ReactorNet([r])\n",
    "    \n",
    "    # now compile a list of all variables for which we will store data\n",
    "    stateVariableNames = [r.component_name(item) for item in range(r.n_vars)]\n",
    "    # use the above list to create a DataFrame\n",
    "    timeHistory = pd.DataFrame(columns=stateVariableNames)\n",
    "    \n",
    "    # set initial time to zero\n",
    "    t = 0\n",
    "    \n",
    "    # Tic\n",
    "    t0 = time.time()\n",
    "    \n",
    "    counter = 1;\n",
    "    while(t < ig_est):\n",
    "        t = reactorNetwork.step()\n",
    "        if (counter%10 ==0):\n",
    "            # We will save every 10th value. Otherwise, this takes too long\n",
    "            # Note that the species concentrations are mass fractions\n",
    "            timeHistory.loc[t] = reactorNetwork.get_state()\n",
    "        counter+=1\n",
    "    tau = timeHistory['oh'].idxmax()\n",
    "    # # Calculating the instananeous dT/dt\n",
    "    # timeHistory['time'] = timeHistory.index\n",
    "    # timeHistory['dTdt'] = timeHistory['temperature'].diff()/timeHistory['time'].diff()\n",
    "    # tau = timeHistory['dTdt'].idxmax()\n",
    "    \n",
    "    #Toc\n",
    "    t1 = time.time()\n",
    "    \n",
    "    print(f'Ignition delay at {rxT:0.0f} was {tau:0.3e}s and took {t1-t0:0.3e}s')    \n",
    "    # If you want to save all the data - molefractions, temperature, pressure, etc\n",
    "    # uncomment the next line\n",
    "    # timeHistory.to_csv(\"time_history.csv\")\n",
    "    \n",
    "    return tau, timeHistory\n",
    "\n",
    "def ignitionDelay_tonly(fuel,phi,rxT,rxP,ig_est):\n",
    "    \"\"\"\n",
    "    This function computes the ignition delay for a fuel/condition \n",
    "    from the occurence of the peak in species' concentration.\n",
    "    \n",
    "    This one only outputs the ignition delay.\n",
    "    \"\"\"\n",
    "    # Create our \"gas\" object from a mechanism\n",
    "    # gas = ct.Solution(mech)\n",
    "    gas.TP = rxT, rxP\n",
    "    gas.set_equivalence_ratio(phi=phi, fuel=fuel, oxidizer={'o2':1.0, 'n2':3.76})\n",
    "    # gas()\n",
    "    \n",
    "    # Create a batch reactor object and add it to a reactor network\n",
    "    # In this example, the batch reactor will be the only reactor\n",
    "    # in the network\n",
    "    r = ct.IdealGasReactor(contents=gas, name='Batch Reactor')\n",
    "    reactorNetwork = ct.ReactorNet([r])\n",
    "    \n",
    "    # now compile a list of all variables for which we will store data\n",
    "    stateVariableNames = [r.component_name(item) for item in range(r.n_vars)]\n",
    "    # use the above list to create a DataFrame\n",
    "    timeHistory = pd.DataFrame(columns=stateVariableNames)\n",
    "    \n",
    "    # set initial time to zero\n",
    "    t = 0\n",
    "    \n",
    "    # Tic\n",
    "    t0 = time.time()\n",
    "    \n",
    "    counter = 1;\n",
    "    while(t < ig_est):\n",
    "        t = reactorNetwork.step()\n",
    "        if (counter%10 ==0):\n",
    "            # We will save only every 10th value. Otherwise, this takes too long\n",
    "            # Note that the species concentrations are mass fractions\n",
    "            timeHistory.loc[t] = reactorNetwork.get_state()\n",
    "        counter+=1\n",
    "    tau = timeHistory['oh'].idxmax()\n",
    "    # # Calculating the instananeous dT/dt\n",
    "    # timeHistory['time'] = timeHistory.index\n",
    "    # timeHistory['dTdt'] = timeHistory['temperature'].diff()/timeHistory['time'].diff()\n",
    "    # tau = timeHistory['dTdt'].idxmax()\n",
    "    \n",
    "    #Toc\n",
    "    t1 = time.time()\n",
    "    \n",
    "    # print('Computed Ignition Delay: {:.3e} seconds. Took {:3.2f}s to compute'.format(tau, t1-t0))    \n",
    "    print(f'Ignition delay at {rxT:0.0f} was {tau:0.3e}s and took {t1-t0:0.3e}s')    \n",
    "    return tau"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Define the gas\n",
    "In this example we will choose n-heptane as the gas. For a representative kinetic model, we use the 160 species [mechanism](https://combustion.llnl.gov/archived-mechanisms/alkanes/heptane-reduced-mechanism) by [Seier et al. 2000, Proc. Comb. Inst](http://dx.doi.org/10.1016/S0082-0784&#40;00&#41;80610-4). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CanteraWarning: NasaPoly2::validate: \n",
      "For species c7h15o-1, discontinuity in h/RT detected at Tmid = 1391.0\n",
      "\tValue computed using low-temperature polynomial:  21.83428060093267\n",
      "\tValue computed using high-temperature polynomial: 21.76699500201913\n",
      "\n",
      "Ignition delay at 600 was 2.728e-01s and took 5.651e+00s\n"
     ]
    }
   ],
   "source": [
    "# Define the fuel and conditions\n",
    "mech = 'nc7red.yaml'\n",
    "gas = ct.Solution(mech)\n",
    "fuel = 'nc7h16'\n",
    "phi = 1\n",
    "rxT = 600 # Kelvin\n",
    "rxP = 50*101325 # Pa\n",
    "est_ign_dly = 1 # estimate in [s]\n",
    "\n",
    "# Calculate \n",
    "tau, timeHistory = ignitionDelay(fuel,phi,rxT,rxP,est_ign_dly)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Plot the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "plt.plot(timeHistory.index, timeHistory['temperature'],'-o')\n",
    "# plt.plot(timeHistory.index, timeHistory['O2'],'-o')\n",
    "plt.xlabel('Time (s)')\n",
    "plt.ylabel('Temperature (K)')\n",
    "# plt.ylabel('$Y_{O2}$')\n",
    "\n",
    "plt.xlim([0,tau*1.3])\n",
    "plt.ylim([400,3000])\n",
    "plt.arrow(0, 2000, tau, 0, width=1, head_width=5,\n",
    "          head_length=0.002, length_includes_head=True, color='r', shape='full')\n",
    "plt.annotate(r'$Ignition Delay: \\tau_{ign}$', xy=(10,1800), xytext=(10,1800), fontsize=16);\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax1 = plt.subplots()\n",
    "ax1.plot(timeHistory.index, timeHistory['o2'],'-o',label='O2')\n",
    "ax1.plot(timeHistory.index, timeHistory[fuel],'-s',label='fuel')\n",
    "ax1.set_xlabel('Time (s)')\n",
    "ax1.set_ylabel('Mass Fraction O2 and Fuel')\n",
    "ax1.set_ylim([0,0.25])\n",
    "plt.arrow(0, 0.12, tau, 0, width=0.001, head_width=0.001,\n",
    "          head_length=0.002, length_includes_head=True, color='r', shape='full')\n",
    "plt.annotate(r'$Ignition Delay: \\tau_{ign}$', xy=(0.05,0.13), xytext=(0.05, 0.13), fontsize=16)\n",
    "#\n",
    "ax2 = ax1.twinx()\n",
    "ax2.plot(timeHistory.index, timeHistory['oh'],'-g^',label='OH')\n",
    "ax2.set_ylabel('Mass Fraction OH')\n",
    "#\n",
    "plt.xlim([0,tau*1.3])\n",
    "# combine the legend\n",
    "lines_1, labels_1 = ax1.get_legend_handles_labels()\n",
    "lines_2, labels_2 = ax2.get_legend_handles_labels()\n",
    "lines = lines_1 + lines_2\n",
    "labels = labels_1 + labels_2\n",
    "ax1.legend(lines, labels, loc='upper right')\n",
    "# show plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Illustration : Ignition Delay vs. Inverse Temperature"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ignition delay at 1500 was 4.893e-06s and took 2.803e+00s\n",
      "Ignition delay at 1400 was 1.182e-05s and took 3.132e+00s\n",
      "Ignition delay at 1300 was 3.023e-05s and took 3.067e+00s\n",
      "Ignition delay at 1200 was 8.079e-05s and took 3.188e+00s\n",
      "Ignition delay at 1100 was 2.459e-04s and took 3.321e+00s\n",
      "Ignition delay at 1050 was 4.288e-04s and took 3.418e+00s\n",
      "Ignition delay at 1000 was 5.677e-04s and took 3.246e+00s\n",
      "Ignition delay at 950 was 4.444e-04s and took 3.878e+00s\n",
      "Ignition delay at 900 was 3.413e-04s and took 4.270e+00s\n",
      "Ignition delay at 850 was 3.867e-04s and took 4.409e+00s\n",
      "Ignition delay at 800 was 7.034e-04s and took 4.642e+00s\n",
      "Ignition delay at 750 was 1.897e-03s and took 4.523e+00s\n",
      "Ignition delay at 700 was 7.060e-03s and took 4.688e+00s\n"
     ]
    }
   ],
   "source": [
    "# Make a list of all the temperatures we would like to run simulations at\n",
    "T = hstack((arange(1500, 1050, -100), arange(1050, 650, -50)))\n",
    "\n",
    "est_ig_dly = ones_like(T, dtype=float)\n",
    "# Make time adjustments for the highest and lowest temperatures. \n",
    "# This we do empirically...\n",
    "est_ig_dly[:10] = .001\n",
    "est_ig_dly[-10:6] = 0.01\n",
    "est_ig_dly[-6:-3] = 0.1\n",
    "est_ig_dly[-2:] = 1\n",
    "\n",
    "# Now create a dataFrame out of these\n",
    "ignitionDelays = pd.DataFrame(data={'T':T})\n",
    "ignitionDelays['ignDelay'] = nan\n",
    "\n",
    "tau = {}\n",
    "\n",
    "# Now, run the ignition delay calc for all the temps...\n",
    "for i, temperature in enumerate(T):\n",
    "    tau = ignitionDelay_tonly(fuel,phi,T[i],rxP,est_ig_dly[i])\n",
    "    ignitionDelays.at[i,'ignDelay'] = tau"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Figure: ignition delay ($\\tau$) vs. the inverse of temperature ($\\frac{1000}{T}$)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(1000/ignitionDelays['T'], ignitionDelays['ignDelay'],'o-')\n",
    "plt.yscale('log')\n",
    "ax.set_ylabel(r'Ign Dly (s)')\n",
    "ax.set_xlabel(r'$\\frac{1000}{T (K)}$', fontsize=12)\n",
    "\n",
    "# Add a second axis on top to plot the temperature for better readability\n",
    "ax2 = ax.twiny()\n",
    "ticks = ax.get_xticks()\n",
    "ax2.set_xticks(ticks)\n",
    "ax2.set_xticklabels((1000/ticks).round(1))\n",
    "ax2.set_xlim(ax.get_xlim())\n",
    "ax2.set_xlabel(r'Temperature: $T(K)$');"
   ]
  }
 ],
 "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": "3.9.16"
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
    "lenType": 16,
    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}