{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IV - 1D computations"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two following links might help you during the computation of 1D flames :<br>\n",
    "https://cantera.org/documentation/docs-3.0/sphinx/html/matlab/one-dim.html <br>\n",
    "https://cantera.org/science/flames.html\n",
    "<br>\n",
    "The first one is about the function that can be used for computing a one dimensional flame and the second one explains the equations that are computed.<br>\n",
    "The main difference between Cantera and a solver like AVBP is that equations are already one dimensional in Cantera."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. ADIABATIC FLAME - A freely-propagating, premixed flat flame "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p class=\"bg-primary\" style=\"padding:1em\"> This script will show you the creation of a premixed flame. First the initial solution is created and then, the calculation is performed before plotting the interesting results. </p> "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cantera as ct\n",
    "import numpy as np\n",
    "from matplotlib.pylab import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.1 Initial solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Import solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "gas = ct.Solution('gri30.yaml')                  # Import gas phases with mixture transport model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set the parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# General\n",
    "p = 1e5                                         # pressure\n",
    "tin = 600.0                                     # unburned gas temperature\n",
    "phi = 0.8                                       # equivalence ratio\n",
    "\n",
    "fuel = {'CH4': 1}                               # Methane composition\n",
    "oxidizer = {'O2': 1, 'N2': 3.76}                # Oxygen composition"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Set gas state to that of the unburned gas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "gas.TP = tin, p\n",
    "gas.set_equivalence_ratio(phi, fuel, oxidizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Create the flame and set inlet conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = ct.FreeFlame(gas, width=0.02)   # Create the free laminar premixed flame specifying the width of the grid\n",
    "f.inlet.X = gas.X                   # Inlet condition on mass fraction\n",
    "f.inlet.T = gas.T                   # Inlet condition on temperature"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.2 Program starts here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p class=\"bg-primary\" style=\"padding:1em\"> The idea here is to solve four flames to make the calculation converge. The first flame will be solved without equation energy whereas the others will continue the computation by enabling the calculation using the equation energy and by adding more point to the flame front. </p> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### First flame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 2 timesteps     2.813e-06      2.407\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 5 timesteps     1.424e-05      1.604\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0003649      1.074\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 20 timesteps      0.006658    -0.5691\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [9] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "grid refinement disabled.\n"
     ]
    }
   ],
   "source": [
    "f.energy_enabled = False                       # No energy for starters\n",
    "\n",
    "tol_ss = [1.0e-5, 1.0e-9]  # tolerance [rtol atol] for steady-state problem\n",
    "tol_ts = [1.0e-5, 1.0e-9]  # tolerance [rtol atol] for time stepping\n",
    "\n",
    "f.flame.set_steady_tolerances(default=tol_ss)\n",
    "f.flame.set_transient_tolerances(default=tol_ts)\n",
    "\n",
    "# Set calculation parameters\n",
    "f.set_max_jac_age(50, 50)                           # Maximum number of times the Jacobian will be used before it \n",
    "                                                    # must be re-evaluated\n",
    "f.set_time_step(1.0e-5, [2, 5, 10, 20, 80])         # Set time steps (in seconds) whenever Newton convergence fails \n",
    "f.set_refine_criteria(ratio=10.0, slope=1, curve=1) # Refinement criteria\n",
    "\n",
    "# Calculation\n",
    "loglevel = 1                                        # amount of diagnostic output (0 to 5)\n",
    "refine_grid = 'disabled'                            # 'refine' or 'remesh' to enable refinement\n",
    "                                                    # 'disabled' to disable\n",
    "\n",
    "f.solve(loglevel, refine_grid)                                      # solve the flame on the grid\n",
    "f.save('4-Output/ch4_adiabatic.yaml', 'no energy','solution with no energy')  # save solution"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Second flame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "f.energy_enabled = True                                 # Energy equation enabled\n",
    "refine_grid = 'refine'                                 # Calculation and save of the results\n",
    "\n",
    "f.set_refine_criteria(ratio=5.0, slope=0.5, curve=0.5)  # Refinement criteria when energy equation is enabled\n",
    "\n",
    "f.solve(loglevel, refine_grid)\n",
    "f.save('4-Output/ch4_adiabatic.yaml', 'energy', 'solution with the energy equation enabled')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mixture-averaged flamespeed = 1.146134 m/s\n"
     ]
    }
   ],
   "source": [
    "print('mixture-averaged flamespeed = {0:7f} m/s'.format(f.velocity[0]))  # m/s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Third flame and so on ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "f.set_refine_criteria(ratio=2.0, slope=0.05, curve=0.05) # Refinement criteria should be changed ...\n",
    "\n",
    "f.solve(loglevel, refine_grid)                           \n",
    "f.save('4-Output/ch4_adiabatic.yaml', 'energy continuation','solution with the energy equation enabled continuation')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mixture-averaged flamespeed continuation = 1.076464 m/s\n",
      "mixture-averaged final T = 2201.307804 K\n"
     ]
    }
   ],
   "source": [
    "points = f.flame.n_points\n",
    "print('mixture-averaged flamespeed continuation = {0:7f} m/s'.format(f.velocity[0]))  # m/s\n",
    "print('mixture-averaged final T = {0:7f} K'.format(f.T[points - 1]))  # K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Fourth flame and so on ..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%capture\n",
    "\n",
    "f.transport_model = 'Multi'      # Switch transport model\n",
    "\n",
    "f.solve(loglevel, refine_grid)\n",
    "\n",
    "f.save('4-Output/ch4_adiabatic.yaml', 'energy_multi',\n",
    "       'solution with the multicomponent transport and energy equation enabled')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "multicomponent flamespeed = 1.081193 m/s\n",
      "multicomponent final T = 2201.873468 K\n"
     ]
    }
   ],
   "source": [
    "points = f.flame.n_points\n",
    "print('multicomponent flamespeed = {0:7f} m/s'.format(f.velocity[0]))  # m/s\n",
    "print('multicomponent final T = {0:7f} K'.format(f.T[points - 1]))  # K"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You may notice that there are small differences on the flamespeed and on the final temperature between the two transport models, as it was predictable."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.3 Manipulate the results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Save your results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "ename": "CanteraError",
     "evalue": "\n*******************************************************************************\nCanteraError thrown by SolutionArray::writeEntry:\nFile '4-Output/ch4_adiabatic.csv' already exists; use option 'overwrite' to replace CSV file.\n*******************************************************************************\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mCanteraError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[15], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m4-Output/ch4_adiabatic.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m  \u001b[38;5;66;03m# Write the velocity, temperature, density, and mole fractions \u001b[39;00m\n\u001b[1;32m      2\u001b[0m                                                    \u001b[38;5;66;03m# to a CSV file\u001b[39;00m\n",
      "File \u001b[0;32mbuild/python/cantera/_onedim.pyx:2230\u001b[0m, in \u001b[0;36mcantera._onedim.Sim1D.save\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mCanteraError\u001b[0m: \n*******************************************************************************\nCanteraError thrown by SolutionArray::writeEntry:\nFile '4-Output/ch4_adiabatic.csv' already exists; use option 'overwrite' to replace CSV file.\n*******************************************************************************\n"
     ]
    }
   ],
   "source": [
    "f.save('4-Output/ch4_adiabatic.csv')  # Write the velocity, temperature, density, and mole fractions \n",
    "                                                   # to a CSV file"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot your results (temperature, density, velocity, ...)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "rcParams['figure.figsize'] = (14, 10)\n",
    "\n",
    "# Get the different arrays\n",
    "z = f.flame.grid\n",
    "T = f.T\n",
    "u = f.velocity\n",
    "ifuel = gas.species_index('CH4')\n",
    "\n",
    "fig=figure(1)\n",
    "\n",
    "# create first subplot - adiabatic flame temperature\n",
    "a=fig.add_subplot(221)\n",
    "a.plot(z,T)\n",
    "title(r'$T_{adiabatic}$ vs. Position', fontsize=15)\n",
    "xlabel(r'Position [m]')\n",
    "ylabel(\"Adiabatic Flame Temperature [K]\")\n",
    "a.xaxis.set_major_locator(MaxNLocator(10)) # this controls the number of tick marks on the axis\n",
    "\n",
    "# create second subplot - velocity\n",
    "b=fig.add_subplot(222)\n",
    "b.plot(z,u)\n",
    "title(r'Velocity vs. Position', fontsize=15)\n",
    "xlabel(r'Position [m]')\n",
    "ylabel(\"velocity [m/s]\")\n",
    "b.xaxis.set_major_locator(MaxNLocator(10)) \n",
    "\n",
    "# create third subplot - rho\n",
    "c=fig.add_subplot(223)\n",
    "p = zeros(f.flame.n_points,'d')\n",
    "for n in range(f.flame.n_points):\n",
    "    f.set_gas_state(n)\n",
    "    p[n]= gas.density_mass\n",
    "c.plot(z,p)\n",
    "title(r'Rho vs. Position', fontsize=15)\n",
    "xlabel(r'Position [m]')\n",
    "ylabel(\"Rho [$kg/m^3$]\")\n",
    "c.xaxis.set_major_locator(MaxNLocator(10)) \n",
    "\n",
    "\n",
    "# create fourth subplot - specie CH4\n",
    "d=fig.add_subplot(224)\n",
    "ch4 = zeros(f.flame.n_points,'d')\n",
    "for n in range(f.flame.n_points):\n",
    "    f.set_gas_state(n)\n",
    "    ch4[n]= gas.Y[ifuel]\n",
    "d.plot(z,ch4)\n",
    "title(r'CH4 vs. Position', fontsize=15)\n",
    "xlabel(r'Position [m]')\n",
    "ylabel(\"CH4 Mole Fraction\")\n",
    "d.xaxis.set_major_locator(MaxNLocator(10))\n",
    "\n",
    "# Set title\n",
    "fig.text(0.5,0.95,r'Adiabatic $CH_{4}$ + Air Free Flame at Phi = 0.8 Ti = 600K and P = 1atm',fontsize=22,horizontalalignment='center')\n",
    "\n",
    "subplots_adjust(left=0.08, right=0.96, wspace=0.25, hspace=0.25)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger \"><b> The plots here describe the flame front. The evolution of the variables and the different values seem coherent with the simulation.  </b></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Compute your own premixed flame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Please go on the website :<br>\n",
    "https://chemistry.cerfacs.fr/en/home/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You will find the reference website of cerfacs as far as chemistry is concerned. If you go to **Chemical Database** and **Data table**, you will notice that several mechanisms can be used for your computation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger \"><b> The task now will be to :<br>\n",
    "- compute two premixed flames : one including the gri30.yaml and the other the Lu.yaml (under methane/skeletal on the website).<br>\n",
    "- the flames will be fuel/air at an equivalence ratio of 1.1, a cold gases temperature of 400K and a pressure of 2 bars.<br>\n",
    "- Try to post-process your data by plotting the flame speed of both flames.<br>\n",
    "The solution you are supposed to get is shown below.<br>\n",
    "NB : the transport used should be a mix one.<b></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Here is where you should code your premixed flame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 2 timesteps     1.406e-06      2.347\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 5 timesteps     1.068e-05      1.872\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0002737      1.548\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 20 timesteps        0.4045     -5.575\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [9] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "grid refinement disabled.\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 2 timesteps      2.25e-05      5.733\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 5 timesteps     2.848e-05      6.166\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     1.014e-05      6.702\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 20 timesteps       0.01498      2.822\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [9] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 1 2 3 4 \n",
      "    to resolve C C2H2 C2H3 C2H4 C2H5 C2H6 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CN CO CO2 H H2 H2O H2O2 HCCO HCN HCNO HCO HNCO HNO HO2 HOCN N N2 N2O NCO NH NH2 NH3 NO NO2 O O2 OH T velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 2 timesteps       1.5e-05       5.64\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 5 timesteps     5.695e-05      5.869\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps      1.52e-05      6.197\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [13] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 3 4 5 6 7 \n",
      "    to resolve C C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CN CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HNO HO2 HOCN N N2 N2O NCO NH NH2 NH3 NO NO2 O O2 OH T velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 2 timesteps       1.5e-05      5.658\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 5 timesteps     7.594e-05       5.33\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps      1.52e-05      6.457\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 20 timesteps       0.00749      1.457\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [18] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 5 6 7 8 9 \n",
      "    to resolve C C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CN CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNN HCNO HCO HNCO HNO HO2 HOCN N N2 N2O NCO NH NH2 NH3 NO NO2 O O2 OH T velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [23] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 7 8 9 10 11 12 \n",
      "    to resolve C C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CN CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HNO HO2 HOCN N N2 N2O NCO NH NH2 NH3 NO2 O O2 OH T velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 2 timesteps      2.25e-05      6.398\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [29] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 9 10 11 12 13 14 15 \n",
      "    to resolve C C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N N2 NCO NH NH2 NO2 O O2 OH T velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [36] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 11 12 14 15 16 17 18 19 \n",
      "    to resolve C C2H C2H2 C2H3 C2H4 C2H5 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2OH CH3 CH3CHO CH3O HCCO HCCOH HCN HCNO HCO N \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [44] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "no new points needed in flame\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [44] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 0 2 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 \n",
      "    to resolve C C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N N2 NCO NH2 NH3 NO NO2 O O2 OH T point 0 point 15 point 2 point 40 velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [83] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 2 6 10 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 65 66 69 70 71 72 75 77 79 80 \n",
      "    to resolve C C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N N2 NCO NO NO2 O O2 OH T point 2 point 6 point 77 velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [144] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 1 19 23 24 25 26 27 29 30 31 32 33 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 142 \n",
      "    to resolve C C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO H H2 H2O2 HCCO HCCOH HCN HCO HNCO HO2 N2 NO NO2 O OH point 1 point 142 point 27 point 47 point 96 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [223] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 35 46 65 124 125 \n",
      "    to resolve C2H3 CH3O point 46 point 65 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [228] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 126 131 \n",
      "    to resolve point 126 point 131 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [230] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "no new points needed in flame\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 2 timesteps     1.406e-06      2.361\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 5 timesteps     1.068e-05       1.88\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0002737      1.539\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 20 timesteps        0.2696     -6.247\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [9] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "grid refinement disabled.\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 2 timesteps      2.25e-05      5.773\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 5 timesteps     1.424e-05       6.83\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     7.602e-06      6.752\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 20 timesteps       0.01124      3.298\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [9] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 1 2 3 4 \n",
      "    to resolve C C2H2 C2H3 C2H4 C2H5 C2H6 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCO HO2 N2 O O2 OH T velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 2 timesteps       1.5e-05      5.693\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 5 timesteps     5.695e-05      6.044\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     2.027e-05      5.822\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [13] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 3 4 5 6 7 \n",
      "    to resolve C C2H2 C2H3 C2H4 C2H5 C2H6 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCO HO2 N2 O O2 OH T velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 2 timesteps       1.5e-05       5.66\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 5 timesteps     7.594e-05      5.374\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps      1.14e-05      6.763\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 20 timesteps       0.01124       1.04\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [18] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 5 6 7 8 9 \n",
      "    to resolve C C2H2 C2H3 C2H4 C2H5 C2H6 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCO HO2 N2 O O2 OH T velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [23] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 8 9 10 11 12 \n",
      "    to resolve C C2H2 C2H3 C2H4 C2H5 C2H6 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCO HO2 N2 O O2 OH T velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 2 timesteps      2.25e-05      6.412\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [28] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 8 9 10 11 12 13 14 \n",
      "    to resolve C C2H2 C2H3 C2H4 C2H5 C2H6 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3O CH3OH CH4 CO H H2 H2O H2O2 HCCO HCO HO2 N2 O O2 OH T velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [35] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 10 11 14 15 16 17 18 \n",
      "    to resolve C C2H2 C2H3 C2H4 C2H5 CH CH2 CH2(S) CH2CHO CH2CO CH2OH CH3 CH3O HCCO HCO \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [42] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "no new points needed in flame\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [42] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 0 2 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 \n",
      "    to resolve C C2H2 C2H3 C2H4 C2H5 C2H6 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCO HO2 N2 O O2 OH T point 0 point 14 point 2 point 38 point 7 velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [79] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 2 6 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 61 62 65 66 67 68 71 73 \n",
      "    to resolve C C2H2 C2H3 C2H4 C2H5 C2H6 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCO HO2 N2 O O2 OH T point 12 point 2 point 6 point 73 velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [136] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 1 16 17 18 21 22 23 24 25 26 27 28 29 30 31 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 \n",
      "    to resolve C C2H2 C2H3 C2H4 C2H5 C2H6 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3O CH3OH CH4 CO H H2 H2O H2O2 HCCO HCO HO2 N2 O OH point 1 point 17 point 25 point 49 point 90 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [213] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 20 47 66 73 74 75 76 77 78 79 80 81 82 116 117 118 \n",
      "    to resolve C2H3 C2H5 C2H6 CH2O CH3O CH3OH H2O2 point 20 point 47 point 66 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [229] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 21 75 93 135 \n",
      "    to resolve point 135 point 21 point 75 point 93 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [233] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 75 \n",
      "    to resolve point 75 \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [234] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "no new points needed in flame\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import cantera as ct\n",
    "import numpy as np\n",
    "from matplotlib.pylab import *\n",
    "\n",
    "names = ['gri30.yaml', 'Mechanisms/Lu.yaml']\n",
    "fuels = ['CH4','CH4']\n",
    "fig=figure(1)\n",
    "for i,n in enumerate(names):\n",
    "    gas = ct.Solution(n)                            # Import gas phases with mixture transport model\n",
    "\n",
    "    p = 2e5                                         # pressure\n",
    "    tin = 400.0                                     # unburned gas temperature\n",
    "    phi = 1.1                                       # equivalence ratio\n",
    "\n",
    "    fuel = {fuels[i]: 1}                            # Fuel composition\n",
    "    oxidizer = {'O2': 1, 'N2': 3.76}                # Oxygen composition\n",
    "    \n",
    "    gas.TP = tin, p\n",
    "    gas.set_equivalence_ratio(phi, fuel, oxidizer)\n",
    "    \n",
    "    f = ct.FreeFlame(gas, width=0.02)   # Create the free laminar premixed flame specifying the width of the grid\n",
    "    f.inlet.X = gas.X                   # Inlet condition on mass fraction\n",
    "    f.inlet.T = gas.T                   # Inlet condition on temperature\n",
    "    \n",
    "    f.energy_enabled = False                       # No energy for starters\n",
    "\n",
    "    tol_ss = [1.0e-5, 1.0e-9]  # tolerance [rtol atol] for steady-state problem\n",
    "    tol_ts = [1.0e-5, 1.0e-9]  # tolerance [rtol atol] for time stepping\n",
    "\n",
    "    f.flame.set_steady_tolerances(default=tol_ss)\n",
    "    f.flame.set_transient_tolerances(default=tol_ts)\n",
    "\n",
    "    # Set calculation parameters\n",
    "    f.set_max_jac_age(50, 50)                           # Maximum number of times the Jacobian will be used before it \n",
    "                                                        # must be re-evaluated\n",
    "    f.set_time_step(1.0e-5, [2, 5, 10, 20, 80])         # Set time steps (in seconds) whenever Newton convergence fails \n",
    "    f.set_refine_criteria(ratio=10.0, slope=1, curve=1) # Refinement criteria\n",
    "\n",
    "    # Calculation\n",
    "    loglevel = 1                                        # amount of diagnostic output (0 to 5)\n",
    "    refine_grid = 'disabled'                            # 'refine' or 'remesh' to enable refinement\n",
    "                                                    # 'disabled' to disable\n",
    "\n",
    "    f.solve(loglevel, refine_grid)                                      # solve the flame on the grid\n",
    "\n",
    "    f.energy_enabled = True                                 # Energy equation enabled\n",
    "    refine_grid = 'refine'                                 # Calculation and save of the results\n",
    "\n",
    "    f.set_refine_criteria(ratio=5.0, slope=0.5, curve=0.5)  # Refinement criteria when energy equation is enabled\n",
    "\n",
    "    f.solve(loglevel, refine_grid)\n",
    "\n",
    "    f.set_refine_criteria(ratio=2.0, slope=0.05, curve=0.05) # Refinement criteria should be changed ...\n",
    "    f.solve(loglevel, refine_grid)                           \n",
    "\n",
    "\n",
    "    rcParams['figure.figsize'] = (14, 10)\n",
    "\n",
    "    # Get the different arrays\n",
    "    if n == 'gri30.yaml':\n",
    "        z_gri3 = f.flame.grid\n",
    "        u_gri3 = f.velocity\n",
    "    else:\n",
    "        z_lu = f.flame.grid\n",
    "        u_lu = f.velocity\n",
    "\n",
    "# create second subplot - velocity\n",
    "b=fig.add_subplot(111)\n",
    "b.plot(z_gri3,u_gri3)\n",
    "b.plot(z_lu, u_lu)\n",
    "title(r'Velocity vs. Position', fontsize=25)\n",
    "xlabel(r'Position [m]', fontsize=15)\n",
    "ylabel(\"velocity [m/s]\", fontsize=15)\n",
    "b.xaxis.set_major_locator(MaxNLocator(10)) \n",
    "plt.show()\n",
    "fig.savefig('4-Output/Comparison.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This introduces the notion of types of mechanisms. To represent your chemistry, you will have :\n",
    "- **detailed mechanisms** such as the gri30 which are very complete but also containing a lot of species (so hard to integrate into solvers such as AVBP).\n",
    "- **global mechanisms** that are very simple (usually 6 species and 2 equations) but that may be inaccurate.\n",
    "- **ARC mechanisms** that are an inbetween solution (among others) and that is chosen to be used at CERFACS. The Lu mechanism used here is an intermediate step to access to an ARC mechanism and thus shows some differences with the detailed, as you might see on the graph.<br>\n",
    "\n",
    "Nevertheless, be always careful when you use this or this detailed mechanism as a reference for your computations. Indeed, it might be accurate with a certain error bar or developed for special conditions that are not yours."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. A burner-stabilized rich premixed methane-oxygen flame at low pressure"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p class=\"bg-primary\" style=\"padding:1em\"> This script will show you the creation of a burner. This is basically the same thing as a premixed flame, except that the flame is stabilized on a burner and not freely proapagating. </p> "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](Images/burnervsfree.pdf)\n",
    "From R.J. Kee, M.E. Coltrin and P. Glarborg *Chemically Reacting Flow*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger \"><b> Try to set a solution from :<br>\n",
    "- the gri30 mechanism (CH4/air flame) <br>\n",
    "- with a pressure of 1 bar, a temperature of 373 K, an equivalence ratio of 1.3\n",
    "<b></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import gas phases with mixture transport model\n",
    "gas = ct.Solution('gri30.yaml')\n",
    "\n",
    "# Parameter values :\n",
    "# General\n",
    "p = 1e5  # pressure\n",
    "tin = 373  # unburned gas temperature\n",
    "phi = 1.3\n",
    "\n",
    "fuel = 'CH4: 1'\n",
    "oxidizer = 'O2:1.0, N2:3.76'\n",
    "\n",
    "# Set gas state to that of the unburned gas\n",
    "gas.TP = tin, p\n",
    "gas.set_equivalence_ratio(phi, fuel, oxidizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger \"><b> Try to construct a BurnerFlame of width 2 cm and implement the limit conditions (f.burner instead of f.inlet for this case).\n",
    "<b></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "f = ct.BurnerFlame(gas, width=0.2)\n",
    "\n",
    "f.burner.T =  gas.T\n",
    "f.burner.X =  gas.X              # Conditions\n",
    "\n",
    "mdot = 0.04\n",
    "f.burner.mdot = mdot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, the strategy is, as above, a four step calculation. For the purpose of this training, we have commented lines so that the result is only done in one calculation.<br>\n",
    "As you might notice, the calculation works and it converges, but it takes more time than doing it by running four calculations.<br>\n",
    "Therefore, the choice is entirely yours and dependent on the fuel you are using (heptane will take more time to converge than methane for instance).<br><br>\n",
    "It is possible that your flame does not converge in some simulations (sometimes, the Newton solver used to compute the flame is crashing if the chemistry is too sharp - meaning that the chemical timesteps are very small - or for other reasons). You have several parameters that you can play on to make it work :\n",
    "- increase or decrease the tolerances for steady-state and time stepping (whether absolute or relative).\n",
    "- increase your ratio in the refine criteria.\n",
    "- take a slower time step with more steps.<br>\n",
    "\n",
    "NB : If you do not want the solver to plot the lines of computing, just uncomment the **%%capture** command.<br>\n",
    "NB2 : Note that two functions might be useful for your future developments :\n",
    "- if you want to **save** a solution in a xml file, you can use the <code>save()</code> function on the freeflame object.\n",
    "- if you want to **restore** a solution from a xml file, you can use the <code>restore()</code> function. This is very useful if you want to analyse old results or if you want a flame to converge without starting from a linear initialization.\n",
    "\n",
    "NB3 : To specify the grid refinement criteria, it is possible to set the function <code>set_refine_criteria</code> with correct values. This enables to have more control on the way the solver refine the curve. The different fields definitions are :<br>\n",
    "- **ratio** : additional points will be added if the ratio of the spacing on either side of a grid point exceeds\n",
    "this value\n",
    "- **slope** : maximum difference in value between two adjacent points, scaled by the maximum difference in the profile (0.0 < slope < 1.0). Adds points in regions of high slope.\n",
    "- **curve** : maximum difference in slope between two adjacent intervals, scaled by the maximum difference in the profile (0.0 < curve < 1.0). Adds points in regions of high curvature.\n",
    "- **prune** : if the slope or curve criteria are satisfied to the level of prune, the grid point is assumed not to be needed and is removed. Set prune significantly smaller than slope and curve. Set to zero to disable pruning the grid.<br>\n",
    "For a first computation, it is best to put the ratio value to a sufficiently high value; and to let the\n",
    "'slope' and 'curve' values to 1. Use 'prune' if you see that Cantera tries to refine your grid too much,\n",
    "and that the number of points starts to exceed a reasonable value (approximately 200 points is enough)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 1 timesteps       1.5e-05      5.339\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 2 timesteps     3.375e-05      4.647\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 5 timesteps     0.0002563      3.665\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0002309      5.144\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 20 timesteps       0.00316      4.307\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 20 timesteps          0.41       1.38\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [7] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 0 1 2 3 4 \n",
      "    to resolve C2H C2H2 C2H3 C2H4 C2H5 C2H6 CH CH2 CH2(S) CH2CO CH2O CH2OH CH3 CH3OH CH4 CO CO2 H H2 H2O HCCO HCCOH HCN HCNO HCO HNCO HNO HO2 HOCN N2 N2O NH NH2 NH3 NO O O2 OH T velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 1 timesteps       1.5e-05      5.049\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 2 timesteps     3.375e-05      4.207\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 5 timesteps     0.0002563      3.642\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps      0.009853      3.659\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 20 timesteps         2.876     0.8788\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [12] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 0 1 2 3 6 10 \n",
      "    to resolve C C2H2 C2H4 C2H6 CH CH2 CH2(S) CH2CO CH2O CH2OH CH3 CH3O CH3OH CH4 CN CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HNO HO2 HOCN N N2 N2O NCO NH NH2 NH3 NO NO2 O O2 OH T point 10 velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 1 timesteps       1.5e-05      5.847\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 2 timesteps     3.375e-05      5.285\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 5 timesteps     0.0001709      4.687\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     1.283e-05      6.361\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 20 timesteps       0.01264      3.587\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 20 timesteps     4.008e-05      6.267\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 20 timesteps      0.002468      4.396\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 20 timesteps        0.7206      1.942\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [18] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 0 1 2 3 \n",
      "    to resolve C C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H8 CH CH2 CH2(S) CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CN CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HNO HO2 HOCN N N2 N2O NCO NH NH2 NH3 NO NO2 O O2 OH T velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 1 timesteps       1.5e-05      5.956\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 2 timesteps     3.375e-05      5.451\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 5 timesteps     0.0001709      5.427\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0001026      5.153\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 20 timesteps     1.234e-05      6.588\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 20 timesteps      0.005405       4.03\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 20 timesteps         2.367       1.59\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [22] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 0 1 2 3 4 12 \n",
      "    to resolve C C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HNO HO2 HOCN N N2 N2O NCO NH NH2 NH3 NO NO2 O O2 OH T velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 1 timesteps       1.5e-05      5.481\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 2 timesteps     3.375e-05      5.154\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 5 timesteps     0.0002563      4.991\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps      0.001232      4.714\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 20 timesteps      0.002107       4.16\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 20 timesteps         1.384       1.13\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [28] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 0 1 2 3 4 5 \n",
      "    to resolve C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H8 CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N2 N2O NH3 NO NO2 O O2 OH T velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [34] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 0 1 2 3 4 5 6 7 \n",
      "    to resolve C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N2 NH2 NH3 NO NO2 O O2 OH T velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [42] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 0 1 2 3 4 5 6 7 8 9 10 11 12 \n",
      "    to resolve C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H8 CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N2 NO NO2 O O2 OH T velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [55] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 \n",
      "    to resolve C2H2 C2H3 C2H4 C2H5 C2H6 C3H8 CH2 CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CO H H2O2 HCCO HCCOH HCO HO2 N2 NO2 O OH \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [75] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 8 9 10 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 \n",
      "    to resolve C2H3 C2H4 C2H5 C2H6 C3H8 CH2 CH2CHO CH2CO CH2O CH3 CH3CHO H H2O2 HCCO HCO HO2 O OH \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [97] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "no new points needed in flame\n"
     ]
    },
    {
     "ename": "CanteraError",
     "evalue": "\n*******************************************************************************\nCanteraError thrown by SolutionArray::writeHeader:\nField name 'energy_soret' exists; use 'overwrite' argument to overwrite.\n*******************************************************************************\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mCanteraError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[21], line 49\u001b[0m\n\u001b[1;32m     45\u001b[0m f\u001b[38;5;241m.\u001b[39msoret_enabled \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[1;32m     47\u001b[0m f\u001b[38;5;241m.\u001b[39msolve(loglevel, refine_grid\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrefine\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m---> 49\u001b[0m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m4-Output/ch4_burner_flame.yaml\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43menergy_soret\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m     50\u001b[0m \u001b[43m       \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43msolution with the energy equation enabled and multicomponent transport\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32mbuild/python/cantera/_onedim.pyx:2230\u001b[0m, in \u001b[0;36mcantera._onedim.Sim1D.save\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mCanteraError\u001b[0m: \n*******************************************************************************\nCanteraError thrown by SolutionArray::writeHeader:\nField name 'energy_soret' exists; use 'overwrite' argument to overwrite.\n*******************************************************************************\n"
     ]
    }
   ],
   "source": [
    "#%%capture\n",
    "#################\n",
    "#f.energy_enabled = False\n",
    "\n",
    "tol_ss = [1.0e-5, 1.0e-9]  # [rtol atol] for steady-state\n",
    "tol_ts = [1.0e-5, 1.0e-4]  # [rtol atol] for time stepping\n",
    "\n",
    "f.flame.set_steady_tolerances(default=tol_ss)\n",
    "f.flame.set_transient_tolerances(default=tol_ts)\n",
    "\n",
    "f.set_refine_criteria(ratio=10.0, slope=1, curve=1)\n",
    "\n",
    "f.set_max_jac_age(50, 50)\n",
    "\n",
    "f.set_time_step(1.0e-5, [1, 2, 5, 10, 20])\n",
    "\n",
    "loglevel = 1  # amount of diagnostic output (0 to 5)\n",
    "\n",
    "#refine_grid = 'refine'  # True to enable refinement, False to\n",
    "\n",
    "#f.solve(loglevel, refine_grid)\n",
    "\n",
    "#f.save('4-Output/ch4_burner_flame.xml', 'no_energy',\n",
    "#       'solution with the energy equation disabled')\n",
    "\n",
    "#################\n",
    "f.energy_enabled = True\n",
    "\n",
    "f.set_refine_criteria(ratio=3.0, slope=0.1, curve=0.2)\n",
    "\n",
    "#f.solve(loglevel, refine_grid='refine')\n",
    "\n",
    "#f.save('4-Output/ch4_burner_flame.xml', 'energy',\n",
    "#       'solution with the energy equation enabled')\n",
    "\n",
    "#################\n",
    "f.transport_model = 'multicomponent'\n",
    "\n",
    "#f.solve(loglevel, refine_grid='refine')\n",
    "\n",
    "#f.save('4-Output/ch4_burner_flame.xml', 'energy_multi',\n",
    "#       'solution with the energy equation enabled and multicomponent transport')\n",
    "\n",
    "#################\n",
    "f.soret_enabled = True\n",
    "\n",
    "f.solve(loglevel, refine_grid='refine')\n",
    "\n",
    "f.save('4-Output/ch4_burner_flame.yaml', 'energy_soret',\n",
    "       'solution with the energy equation enabled and multicomponent transport')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div class=\"alert alert-danger \"><b> Here the plot is for the temperature. Try to change it to plot the laminar flame speed.\n",
    "<b></div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "CanteraError",
     "evalue": "\n*******************************************************************************\nCanteraError thrown by SolutionArray::writeEntry:\nFile '4-Output/ch4_burner_flame.csv' already exists; use option 'overwrite' to replace CSV file.\n*******************************************************************************\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mCanteraError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[22], line 8\u001b[0m\n\u001b[1;32m      5\u001b[0m plt\u001b[38;5;241m.\u001b[39mtitle(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mLaminar flame speed vs x-axis for an equivalence ratio of 1.3\u001b[39m\u001b[38;5;124m'\u001b[39m, fontsize\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m20\u001b[39m)\n\u001b[1;32m      6\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n\u001b[0;32m----> 8\u001b[0m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m4-Output/ch4_burner_flame.csv\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[0;32mbuild/python/cantera/_onedim.pyx:2230\u001b[0m, in \u001b[0;36mcantera._onedim.Sim1D.save\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mCanteraError\u001b[0m: \n*******************************************************************************\nCanteraError thrown by SolutionArray::writeEntry:\nFile '4-Output/ch4_burner_flame.csv' already exists; use option 'overwrite' to replace CSV file.\n*******************************************************************************\n"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(f.grid, f.velocity)\n",
    "plt.xlabel('grid (m)',fontsize=15)\n",
    "plt.ylabel('Laminar flame speed ($m/s$)',fontsize=15)\n",
    "plt.title('Laminar flame speed vs x-axis for an equivalence ratio of 1.3', fontsize=20)\n",
    "plt.show()\n",
    "\n",
    "f.save('4-Output/ch4_burner_flame.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NB : This type of flame is not used in practice as most of the combustion chambers are based on the study of premixed flames configurations.. Nevertheless, it can be useful for some special configurations that you may want to study.<br>\n",
    "Next, we will see the **counter-flow diffusion flame** as this is an important configuration to study when studying a combustion chamber. However, if you need to look for other types, some more are available (see above)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. An opposed-flow ethane/air diffusion flame"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A counter-flow diffusion flame is basically a flame where the oxidizer and the fuel meet to burn (they are not mixed before burning, contrary to the two others types that we have seen before)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![title](Images/CDF.png)\n",
    "From Pfitzner, Michael & Müller, Hagen & Ferraro, Federica. (2013). Implementation of a Steady Laminar Flamelet Model for nonpremixed combustion in LES and RANS simulations. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First of all, the solution object is instanciated as usual. The mass flow rate of the oxidizer and the fuel have been fitted so that the flame is in stoichiometric conditions. In addition to the composition, the temperature and the pressure, the density and the mass flow rate are given. By dividing the second by the first, the velocity of each gases is given which leads to the calculation of the strain rate, an important parameter to counter-flow diffusion flame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Velocity of the fuel : 0.19953599999999996\n",
      "Velocity of the oxidizer : 0.6192496551724137\n",
      "Strain rate of the diffusion flame : 40.93928275862068\n"
     ]
    }
   ],
   "source": [
    "gas = ct.Solution('gri30.yaml', transport='mixture-averaged')\n",
    "\n",
    "p = 1e5  # pressure\n",
    "\n",
    "comp_f = 'C2H6:1'                       # fuel composition\n",
    "tin_f = 300.0                           # fuel inlet temperature\n",
    "\n",
    "rho_f = p / (8.314 / 0.030 * tin_f)     # fuel inlet density\n",
    "mdot_f = 0.24                           # fuel inlet mass flow rate (kg/m^2/s)\n",
    "vel_f = mdot_f / rho_f                  # fuel inlet velocity\n",
    "print('Velocity of the fuel : ' + str(vel_f))\n",
    "\n",
    "comp_o = 'O2:0.21, N2:0.78, AR:0.01'    # oxidizer composition\n",
    "tin_o = 300.0                           # oxidizer inlet temperature\n",
    "\n",
    "rho_o = p / (8.314 / 0.029 * tin_o)     # oxidizer inlet density\n",
    "mdot_o = 0.72                           # oxidizer inlet mass flow rate (kg/m^2/s)\n",
    "vel_o = mdot_o / rho_o                  # oxidizer inlet velocity\n",
    "print('Velocity of the oxidizer : ' + str(vel_o))\n",
    "\n",
    "gas.TP = tin_o, p\n",
    "\n",
    "width = 0.02\n",
    "a = (vel_o + vel_f) / width              # calculation of the strain rate (s^-1)\n",
    "print('Strain rate of the diffusion flame : ' + str(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This will instanciate the counterflow diffusion flame. As you might notice, and this is valid for all the other cases, you can instanciate the initial grid yourself. This allows you to refine the grid when you want. This can be a good trick when your simulation is crashing and you want to compute it again.<br><br>\n",
    "As far as the inlets are concerned, the composition, temperature and mass flow rate needs to be specified."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "initial_grid = np.linspace(0, width, 6)\n",
    "f = ct.CounterflowDiffusionFlame(gas, initial_grid)\n",
    "\n",
    "# Set the state of the two inlets\n",
    "f.fuel_inlet.mdot = mdot_f\n",
    "f.fuel_inlet.X = comp_f\n",
    "f.fuel_inlet.T = tin_f\n",
    "\n",
    "f.oxidizer_inlet.mdot = mdot_o\n",
    "f.oxidizer_inlet.X = comp_o\n",
    "f.oxidizer_inlet.T = tin_o"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This starts the calculation of the counterflow diffusion flame. By experience, you will see that, out of this wonderful tutorial case, diffusion flames are hard to converge. You can either play with the size of the domain and every parameter said all along the tutorial (indeed, sometimes it converges better if the domain is smaller), or you can try a method that is developed here in CERFACS that resolves **the diffusion flame in the z-space**.<br>\n",
    "If you want some more information about that, come and ask the chemistry team of cerfacs."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps      1.78e-06      5.845\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     3.041e-05      4.321\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0007794      3.771\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps       0.01332      1.268\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [6] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "grid refinement disabled.\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0002563      5.361\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps      0.001297      4.651\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps       0.01478      3.032\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [6] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 0 1 2 3 \n",
      "    to resolve AR C2H C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2CN H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N2 NH3 NO NO2 O O2 OH T velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     2.848e-05      6.246\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     3.604e-05       6.03\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0006158      5.287\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps      0.003117      4.094\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [10] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 1 2 3 4 5 \n",
      "    to resolve C2H2 C2H3 C2H4 C2H5 C2H6 C3H7 C3H8 CH2 CH2CHO CH2CO CH2O CH2OH CH3 CH3CHO CH3O CH3OH CH4 CO CO2 H H2 H2O H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N2O NH3 NO NO2 O O2 OH T velocity \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0001139      5.442\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps      0.001297      4.539\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps       0.02217      2.194\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [15] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 2 3 4 5 6 7 8 \n",
      "    to resolve C C2H C2H2 C2H3 C2H4 C2H5 C3H7 C3H8 CH CH2 CH2(S) CH2CHO CH2CO CH2O CH2OH CH3 CH3OH CH4 CN CO H H2 H2O2 HCCO HCCOH HCN HCNO HCO HNCO HO2 N N2O NCO NH NH2 NH3 NO O O2 OH T \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     2.848e-05      5.809\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0004865      4.708\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [22] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 3 4 6 7 8 9 10 11 12 \n",
      "    to resolve C C2H C2H2 C2H3 C2H4 C2H5 C3H7 C3H8 CH CH2 CH2(S) CH2O CH2OH CH3 CH3O CN CO2 H H2O2 HCCO HCCOH HCN HCNO HCO HNCO HNO HO2 HOCN N N2O NCO NH NH2 NO O OH \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps     0.0001281       4.59\n",
      "Attempt Newton solution of steady-state problem...    failure. \n",
      "Take 10 timesteps      0.003284      3.884\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [31] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 12 13 14 15 19 20 \n",
      "    to resolve C C2H CH CH2 CH2(S) CH2O CH2OH CN H H2O2 HCCO HCCOH HCO HNO HOCN N NCO NH NH2 NO \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [37] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "##############################################################################\n",
      "Refining grid in flame.\n",
      "    New points inserted after grid points 15 16 \n",
      "    to resolve C CH CN HCO N \n",
      "##############################################################################\n",
      "\n",
      "..............................................................................\n",
      "Attempt Newton solution of steady-state problem...    success.\n",
      "\n",
      "Problem solved on [39] point grid(s).\n",
      "\n",
      "..............................................................................\n",
      "no new points needed in flame\n"
     ]
    },
    {
     "ename": "CanteraError",
     "evalue": "\n*******************************************************************************\nCanteraError thrown by SolutionArray::writeHeader:\nField name 'energy' exists; use 'overwrite' argument to overwrite.\n*******************************************************************************\n",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mCanteraError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[0;32mIn[25], line 26\u001b[0m\n\u001b[1;32m     23\u001b[0m f\u001b[38;5;241m.\u001b[39msolve(loglevel\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m1\u001b[39m, refine_grid\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrefine\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m     25\u001b[0m \u001b[38;5;66;03m# save your results\u001b[39;00m\n\u001b[0;32m---> 26\u001b[0m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43msave\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43m4-Output/c2h6_diffusion.yaml\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43menergy\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     28\u001b[0m \u001b[38;5;66;03m#################\u001b[39;00m\n\u001b[1;32m     29\u001b[0m \u001b[38;5;66;03m# Third flame:\u001b[39;00m\n\u001b[1;32m     30\u001b[0m \u001b[38;5;66;03m# specify grid refinement criteria, turn on the energy equation,\u001b[39;00m\n\u001b[1;32m     31\u001b[0m f\u001b[38;5;241m.\u001b[39menergy_enabled \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n",
      "File \u001b[0;32mbuild/python/cantera/_onedim.pyx:2230\u001b[0m, in \u001b[0;36mcantera._onedim.Sim1D.save\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mCanteraError\u001b[0m: \n*******************************************************************************\nCanteraError thrown by SolutionArray::writeHeader:\nField name 'energy' exists; use 'overwrite' argument to overwrite.\n*******************************************************************************\n"
     ]
    }
   ],
   "source": [
    "#%%capture\n",
    "# First flame:\n",
    "# disable the energy equation\n",
    "f.energy_enabled = False\n",
    "\n",
    "# Set error tolerances\n",
    "tol_ss = [1.0e-5, 1.0e-11]  # [rtol, atol] for steady-state problem\n",
    "tol_ts = [1.0e-5, 1.0e-11]  # [rtol, atol] for time stepping\n",
    "f.flame.set_steady_tolerances(default=tol_ss)\n",
    "f.flame.set_transient_tolerances(default=tol_ts)\n",
    "\n",
    "# and solve the problem without refining the grid\n",
    "f.solve(loglevel=1, refine_grid='disabled')\n",
    "\n",
    "#################\n",
    "# Second flame:\n",
    "# specify grid refinement criteria, turn on the energy equation,\n",
    "f.energy_enabled = True\n",
    "\n",
    "f.set_refine_criteria(ratio=3, slope=0.8, curve=0.8)\n",
    "\n",
    "# and solve the problem again\n",
    "f.solve(loglevel=1, refine_grid='refine')\n",
    "\n",
    "# save your results\n",
    "f.save('4-Output/c2h6_diffusion.yaml', 'energy')\n",
    "\n",
    "#################\n",
    "# Third flame:\n",
    "# specify grid refinement criteria, turn on the energy equation,\n",
    "f.energy_enabled = True\n",
    "\n",
    "f.set_refine_criteria(ratio=2, slope=0.2, curve=0.2, prune=0.04)\n",
    "\n",
    "# and solve the problem again\n",
    "f.solve(loglevel=1, refine_grid='refine')\n",
    "\n",
    "# save your results\n",
    "f.save('4-Output/c2h6_diffusion.yaml', 'energy continuation')\n",
    "\n",
    "#################################################################\n",
    "# Save your results if needed\n",
    "#################################################################\n",
    "# write the velocity, temperature, and mole fractions to a CSV file\n",
    "f.save('4-Output/c2h6_diffusion.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "These are interesting quantities to plot, such as the different mass fractions and the temperature of the flame. This is all done in the following graph (with the values being adimensionalised)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Get interesting values\n",
    "z = f.flame.grid\n",
    "T = f.T\n",
    "u = f.velocity\n",
    "\n",
    "# Get interesting indices for computation of species\n",
    "fuel_species = 'C2H6'\n",
    "ifuel = gas.species_index(fuel_species)\n",
    "io2 = gas.species_index('O2')\n",
    "in2 = gas.species_index('N2')\n",
    "\n",
    "# Initiate interesting vectors\n",
    "c2h6 = np.zeros(f.flame.n_points,'d')\n",
    "o2 = np.zeros(f.flame.n_points,'d')\n",
    "hr = np.zeros(f.flame.n_points,'d')\n",
    "\n",
    "# Computes interesting quantities for analyzing a counter-flow flame\n",
    "for n in range(f.flame.n_points):\n",
    "    f.set_gas_state(n)\n",
    "    c2h6[n]= gas.Y[ifuel]\n",
    "    o2[n]= gas.Y[io2]\n",
    "    hr[n] = - np.dot(gas.net_production_rates, gas.partial_molar_enthalpies)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1400x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig=figure(1)\n",
    "\n",
    "a=fig.add_subplot(111)\n",
    "a.plot(z,T/np.max(T),z,c2h6/np.max(c2h6),z,o2/np.max(o2))\n",
    "plt.title(r'$T_{adiabatic}$ vs. Position',fontsize=25)\n",
    "plt.xlabel(r'Position [m]', fontsize=15)\n",
    "plt.ylabel('Normalized values of different quantities',fontsize=15)\n",
    "plt.legend(['Temperature','$Y_{C_2H_6}$', '$Y_{O_2}$'],fontsize=15)\n",
    "show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "cantera-avbp3.0-py39",
   "language": "python",
   "name": "cantera-avbp3.0-py39"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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