{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# III - 0D computations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"The following link may help you to find the answers or the documentation you need :\n",
"https://cantera.org/documentation/docs-3.0/sphinx/html/cython/zerodim.html\n",
"https://cantera.org/science/reactors/reactors.html\n",
" \n",
"The first one is about the function that can be used for computing a reactor and the second one explains the equations that are computed. "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Knowledge of 0D computations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ReactorBase class"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"0D Reactors are often used in combustion to model auto-ignition time and the chemistry associated to it. For reactors and reservoirs, the class used is the following : \n",
"ReactorBase(ThermoPhase contents, name=None, arguments)\n",
"
\n",
"Here are the different types of reactors you can have for a 0D computation :\n",
"1. **Reservoir** \n",
"Reactors with a constant state. The temperature, pressure, and chemical composition in a reservoir never change from their initial values.\n",
"\n",
"2. **Reactor**\n",
"3. **IdealGasReactor** \n",
"Constant volume, zero-dimensional reactor for ideal gas mixtures.\n",
"4. **ConstPressureReactor** \n",
"Homogeneous, constant pressure, zero-dimensional reactor. The volume of the reactor changes as a function of time in order to keep the pressure constant.\n",
"5. **IdealGasConstPressureReactor** \n",
"Homogeneous, constant pressure, zero-dimensional reactor for ideal gas mixtures. The volume of the reactor changes as a function of time in order to keep the pressure constant.\n",
"6. **FlowReactor** \n",
"A steady-state plug flow reactor with constant cross sectional area. Time integration follows a fluid element along the length of the reactor. The reactor is assumed to be frictionless and adiabatic."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### FlowDevice class"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reactors can be added inlets and outlets, to be able to loop or chain several reactors, control mass flow rates, volume or pressure. Here are the properties of the common baseclass *FlowDevice* for flow controllers between reactors and reservoirs : \n",
"FlowDevice(upstream, downstream, name=None, arguments)\n",
"
\n",
"The FlowDevice controls the fluids passage between an upstream and a downstream object, which should be specified. The arguments depend upon the different types of flow controllers available in Cantera :\n",
"1. **MassFlowController** \n",
"Mass flow controllers maintain a specified mass flow rate independent of upstream and downstream conditions. Unlike a real mass flow controller, a MassFlowController object will maintain the flow even if the downstream pressure is greater than the upstream pressure.\n",
"2. **Valve** \n",
"Valves are flow devices with mass flow rate that is a function of the pressure drop across them. Valve objects are often used between an upstream reactor and a downstream reactor or reservoir to maintain them both at nearly the same pressure. By setting the constant Kv to a sufficiently large value, very small pressure differences will result in flow between the reactors that counteracts the pressure difference.\n",
"3. **PressureController** \n",
"A pressure controller is designed to be used in conjunction, typically, with a MassFlowController. The master flow controller is installed on the inlet of the reactor, and the corresponding PressureController is installed on the outlet of the reactor. The PressureController mass flow rate is equal to the master mass flow rate, plus a small correction dependent on the pressure difference."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Walls"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Reactors can also add heat transfer and heterogeneous reactions at the walls, through a special\n",
"object \"wall\". Walls separate two reactors, or a reactor and a reservoir. A wall has a finite area, may\n",
"conduct or radiate heat between the two reactors on either side, and may move like a piston. They are\n",
"stateless objects in Cantera, meaning that no differential equation is integrated to determine any wall\n",
"property. A heterogeneous reaction mechanism may be specified for one or both of the wall surfaces."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### ReactorNet"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now, the evolution of a reactor, or a network of reactors -which are 0-D objects- with time, is performed\n",
"through an integrator object *ReactorNet* : \n",
"
Try to move the temperature of the gas state : \n",
"- at 960 K \n",
"- at 1040 K\n",
"
\n",
"What do you observe ?
"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"As you can see, slightly shifting the temperature up or down moves the auto-ignition time. As you never know a priori the order of magnitude of the ignition time, it is good to use the step version until you reach burnt gases."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. A simple constant pressure reactor"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Here, we want to create a simple constant pressure reactor. To do so, it is necessary to create a reactor and its environment (which will be a Reservoir). The interface between the two objects created is handled by a wall, of which the expansion rate can be defined by the user."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Set the mechanism properties"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"# Mechanisms used for the process\n",
"gri3 = ct.Solution('gri30.yaml')\n",
"air = ct.Solution('air.yaml')\n",
"\n",
"# Gas state\n",
"gri3.TPX = 1000.0, ct.one_atm, 'CH4:0.5,O2:1,N2:3.76'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Set the reservoir"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
Try to set : \n",
"- an IdealGasReactor object with the gri3 object created above \n",
"- a Reservoir representing the environment with the air object created above\n",
"
You will have to : \n",
"- set a first gas a with an air mixture at 0.21 of O2, 0.01 of AR and 0.78 of N2 \n",
"- set a second gas b with a gri30 mixture at 1 of CH4\n",
"
You will have to : \n",
"- set a first reservoir from gas a \n",
"- set a second reservoir from gas b \n",
"- set a downstream reservoir filled with gas b\n",
"
You will have to : \n",
"- change the condition of the gas b to respect an air mix. \n",
"- create and IdealGasReactor with the energy equation set to 'on'.\n",
"
"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"gas_b.TPX = 300, ct.one_atm, 'O2:1., N2:3.78, CH4:0.5'\n",
"mixer = ct.IdealGasReactor(gas_b, energy='on')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Watch out here, the mixture should be the one of gas_b, as the reactions will occur with the mechanism from gri30 (and not from the air)."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creation of the mass flow controller that will control the mixing (FlowDevice)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Now that you have specified all the elements of the reactor, you need to detail the link between them. This is what is done in the following lines :"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [],
"source": [
"mfca = ct.MassFlowController(res_a, mixer)\n",
"mdota = mfca.mass_flow_rate = mw_a*2./0.21\n",
"mfcb = ct.MassFlowController(res_b, mixer)\n",
"mdotb = mfcb.mass_flow_rate = mw_b*1.0"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creation of the valve between the mixer and the downstream (FlowDevice)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Finally, the valve is created."
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"outlet = ct.Valve(mixer, downstream, K=10.0)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This valve will enable the system to keep a constant pressure at the inlet of the mixer and at the outlet."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creation of the net reactor to compute simulation"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"
You need to create the ReactorNet with the mixer.
"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"sim = ct.ReactorNet([mixer])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" t [s] T [K] h [J/kg] P [Pa] X_CH4\n",
" 7.6903 300 -2.5352e+05 1.0133e+05 0.094978\n",
" 15.406 300 -2.5351e+05 1.0133e+05 0.095017\n",
" 23.126 300 -2.5351e+05 1.0133e+05 0.095022\n",
" 30.846 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 38.566 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 46.286 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 54.006 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 61.726 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 69.446 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 77.166 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 84.886 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 92.606 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 100.33 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 108.05 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 115.77 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 123.49 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 131.21 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 138.93 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 146.65 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 154.37 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 162.09 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 169.81 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 177.53 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 185.25 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 192.97 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 200.69 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 208.41 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 216.13 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 223.85 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 231.57 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 239.29 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 247.01 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 254.73 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 262.45 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 270.17 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 277.89 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 285.61 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 293.33 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 301.05 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 308.77 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 316.49 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 324.21 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 331.93 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 339.65 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 347.37 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 355.09 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 362.81 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 370.53 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 378.25 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 385.97 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 393.69 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 401.41 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 409.13 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 416.85 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 424.57 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 432.29 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 440.01 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 447.73 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 455.45 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 463.17 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 470.89 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 478.61 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 486.33 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 494.05 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 501.77 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 509.49 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 517.21 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 524.93 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 532.65 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 540.37 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 548.09 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 555.81 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 563.53 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 571.25 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 578.97 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 586.69 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 594.41 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 602.13 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 609.85 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 617.57 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 625.29 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 633.01 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 640.73 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 648.45 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 656.17 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 663.89 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 671.61 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 679.33 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 687.05 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 694.77 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 702.49 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 710.21 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 717.93 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 725.65 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 733.37 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 741.09 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 748.81 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 756.53 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 764.25 300 -2.5351e+05 1.0133e+05 0.095023\n",
" 771.97 300 -2.5351e+05 1.0133e+05 0.095023\n",
"\n",
" gri30:\n",
"\n",
" temperature 300 K\n",
" pressure 1.0133e+05 Pa\n",
" density 1.1269 kg/m^3\n",
" mean mol. weight 27.742 kg/kmol\n",
" phase of matter gas\n",
"\n",
" 1 kg 1 kmol \n",
" --------------- ---------------\n",
" enthalpy -2.5351e+05 -7.0327e+06 J\n",
" internal energy -3.4342e+05 -9.5271e+06 J\n",
" entropy 7221.9 2.0035e+05 J/K\n",
" Gibbs function -2.4201e+06 -6.7137e+07 J\n",
" heat capacity c_p 1070.4 29695 J/K\n",
" heat capacity c_v 770.71 21381 J/K\n",
"\n",
" mass frac. Y mole frac. X chem. pot. / RT\n",
" --------------- --------------- ---------------\n",
" O2 0.2192 0.19005 -26.334\n",
" CH4 0.054952 0.095023 -54.676\n",
" N2 0.71281 0.70588 -23.381\n",
" AR 0.013032 0.0090498 -23.315\n",
" [ +49 minor] 3.888e-33 4.4903e-33 \n",
"\n"
]
}
],
"source": [
"# Since the mixer is a reactor, we need to integrate in time to reach steady\n",
"# state. A few residence times should be enough.\n",
"print('{0:>14s} {1:>14s} {2:>14s} {3:>14s} {4:>14s}'.format('t [s]', 'T [K]', 'h [J/kg]', 'P [Pa]', 'X_CH4'))\n",
"t = 0.0\n",
"for n in range(100):\n",
" tres = mixer.mass/(mdota + mdotb)\n",
" t += 2*tres\n",
" sim.advance(t)\n",
" print('{0:14.5g} {1:14.5g} {2:14.5g} {3:14.5g} {4:14.5g}'.format(t, mixer.T, mixer.thermo.h, mixer.thermo.P, mixer.thermo['CH4'].X[0]))\n",
"\n",
"print(mixer.thermo.report())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Notice that the gas did not ignite, and that the composition is indeed stoichiometric. We could trigger\n",
"the ignition, by assuming that the reactor is 'already lit'. To do so, you need to initialize the content\n",
"of the reactor with hot gases. The simplest way to do so is by filling it with stoichiometric mixture of\n",
"air and methane at equilibrium. This is easily done by replacing the lines : \n",
"\n",
"gas_b.TPX = 300.0, ct.one_atm, 'O2:0.21, N2:0.78, AR:0.01'\n",
"mixer = ct.IdealGasReactor(gas_b)\n",
" \n",
"with :\n",
" \n",
"gas_b.TPX = 300.0,ct.one_atm,'O2:1., N2:3.78, CH4:0.5'\n",
"gas_b.equilibrate(\"HP\")\n",
"mixer = ct.IdealGasReactor(gas_b)\n",
""
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Autoignition timing"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"An interesting feature of 0D simulations is that it allows to compute the temporal evolution of a\n",
"mixture under specific conditions towards its equilibrium state. That evolution can be viewed as\n",
"a sort of **autoignition** of the mixture. \n",
"To go further, that last exercise will guide you through\n",
"such a computation of the autoignition timing of a methane/air mixture. Several definitions of the\n",
"autoignition timing can be found in the literature. The most commonly accepted definition relies\n",
"on the temperature gradient : the time of the sharpest temperature increase is spotted as being the\n",
"autoignition point. In order to catch this time with precision, the time step of the simulation should\n",
"be as small as possible."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Create the gas object"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [],
"source": [
"gas = ct.Solution('gri30.yaml')\n",
"gas.TPX = 1250, ct.one_atm, 'CH4:0.5, O2:1, N2:3.76'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Initialisation of the different values"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"# Initial temperatures\n",
"Temperature_range = list(range(800, 1700, 100))\n",
"\n",
"# Specify the number of time steps and the time step size\n",
"nt = 100000\n",
"dt = 1.e-4 # s\n",
"\n",
"# Storing auto ignitions\n",
"auto_ignitions = []"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"For T = 800, Autoignition time = 9.999999999990033 s\n",
"For T = 900, Autoignition time = 7.055299999996896 s\n",
"For T = 1000, Autoignition time = 1.066899999999899 s\n",
"For T = 1100, Autoignition time = 0.19789999999999452 s\n",
"For T = 1200, Autoignition time = 0.043400000000000216 s\n",
"For T = 1300, Autoignition time = 0.011099999999999988 s\n",
"For T = 1400, Autoignition time = 0.0032999999999999982 s\n",
"For T = 1500, Autoignition time = 0.0011000000000000003 s\n",
"For T = 1600, Autoignition time = 0.0004 s\n"
]
}
],
"source": [
"for index, Temperature in enumerate(Temperature_range):\n",
" #################################################################\n",
" # Initial temperature, Pressure and stoichiometry\n",
" gas.TPX = Temperature, ct.one_atm, 'CH4:0.5, O2:1, N2:3.76'\n",
" # Create the batch reactor\n",
" r = ct.IdealGasReactor(gas)\n",
" # Now create a reactor network consisting of the single batch reactor\n",
" sim = ct.ReactorNet([r])\n",
" # Storage space\n",
" mfrac = []\n",
" # ...\n",
" time = []\n",
" temperature = []\n",
" HR = []\n",
" # Run the simulation\n",
" # Initial simulation time\n",
" current_time = 0.0\n",
" # Loop for nt time steps of dt seconds.\n",
" for n in range(nt):\n",
" current_time += dt\n",
" sim.advance(current_time)\n",
" time.append(current_time)\n",
" temperature.append(r.T)\n",
" mfrac.append(r.thermo.Y)\n",
" HR.append(- np.dot(gas.net_production_rates, gas.partial_molar_enthalpies))\n",
" #################################################################\n",
" # Catch the autoignition timing\n",
" #################################################################\n",
" # Get the ignition delay time by the maximum value of the Heat Release rate\n",
" auto_ignition = time[HR.index(max(HR))]\n",
" print('For T = ' + str(Temperature) + ', Autoignition time = ' + str(auto_ignition) + ' s')\n",
" # Posterity\n",
" FinalTemp = temperature[nt - 1]\n",
" auto_ignitions.append(auto_ignition)\n",
" # #################################################################\n",
" # # Save results\n",
" # #################################################################\n",
" # # write output CSV file for importing into Excel\n",
" # csv_file = '3-Output/Phi-1_P-1_T-' + str(Temperature) + '_UV.csv'\n",
" # with open(csv_file, 'w') as outfile:\n",
" # writer = csv.writer(outfile)\n",
" # writer.writerow(['Auto ignition time [s]', 'Final Temperature [K]'] + gas.species_names)\n",
" # writer.writerow([auto_ignition, FinalTemp] + list(mfrac[:]))\n",
" # print('output written to ' + csv_file)\n",
"T_invert = [1000 / Temperature for Temperature in Temperature_range]"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"#################################################################\n",
"# Plot results\n",
"#################################################################\n",
"# create plot\n",
"plt.plot(Temperature_range, auto_ignitions, 'b-o')\n",
"plt.xlabel(r'Temperature [K]', fontsize=20)\n",
"plt.ylabel(\"Auto ignition [s]\", fontsize=20)\n",
"plt.yscale('log')\n",
"plt.title(r'Autoignition of $CH_{4}$ + Air mixture at $\\Phi$ = 1, and P = 1 bar',\n",
"fontsize=22, horizontalalignment='center')\n",
"plt.axis()\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is a typical curve of the auto-ignition time as a function of the initial temperature, meaning that when the temperature increases, this time is reduced."
]
}
],
"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",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.5"
}
},
"nbformat": 4,
"nbformat_minor": 2
}