# Fipy and Cantera/Diffusion 1D

### From charlesreid1

## Contents

# Overview

This example illustrates how to solve a simple, time-dependent 1D diffusion problem using Fipy with diffusion coefficients computed by Cantera. This is a very simple problem. The point is not to demonstrate earth-shaking complexity, the point is illustrating how to make these two packages talk to each other.

The equation being solved is:

subject to boundary conditions:

The mixture being used is argon in oxygen. Although the diffusion coefficient is a very weak function of composition, and therefore the assumption

would normally apply, this example illustrates the sweep solution technique (in which the diffusion coefficient is a function of the composition) anyway.

Note that because this is a binary mixture, we only need to solve for a single component (argon), because:

# The Script

Begin with import statements:

from fipy import * from numpy import * import Cantera import matplotlib.pylab as plt

Next, create a plot, which we'll use to visualize our results:

fig = plt.figure() ax = fig.add_subplot(111) ax.set_title('Ar')

Computing the diffusion coefficient using Cantera proceeds as follows:

- Pass in a Fipy CellVariable object
- Create a CellVariable to contain diffusion coefficient values
- Looping over each cell:
- Translate the value of the Fipy CellVariable at a given cell into a thermochemical state for a Cantera phase object (we want to use a single Cantera phase object, so we don't have to keep loading our CTI/XML mechanism file into memory)
- Compute the value of the diffusion coefficient
- Populate the diffusion CellVariable

- Return the diffusion CellVariable

We'll define this in a function, which we can then pass when specifying the diffusion coefficient for the Fipy equation. (Note that I'm going to use two global variables here, `a`

and `ari`

, which are defined later in the script. These represent the gas phase object and the argon species index, respectively.)

###################################### # Diffusion coefficient function def get_diffusion_coeff( phi ): D = CellVariable(name="DiffusionCoeff",mesh=mesh) for i in range(phi.shape[0]): var = phi.value[i] X0 = "AR:%0.8f, O2:%0.8f"%(var,1-var) a.set(X=X0) # mixture-averaged diffusion coefficients Dcoeff = a.mixDiffCoeffs()[ari] D.put(i,Dcoeff) return D

We also want to define a single Cantera phase, which we'll use in the `get_diffusion_coeff()`

function:

###################################### # Gas object # # This object will be used to evaluate all thermodynamic, kinetic, # and transport properties # rxnmech = 'h2o2.cti' # reaction mechanism file mix = 'ohmech' # gas mixture model comp = 'O2:1.0, AR:1.0' # gas composition a = Cantera.IdealGasMix(rxnmech, mix) a.set(T = 298.15, P = Cantera.OneAtm, X = comp) arlab = 'AR' o2lab = 'O2' ari = a.speciesIndex(arlab) o2i = a.speciesIndex(o2lab)

Now we define Fipy equations/grid:

# First, define the mesh nx = 100 dx = 0.01 L = nx * dx xs = linspace(0,L,nx) mesh = Grid1D(nx=nx,dx=dx) # Define boundary values arLeft = 1. arRight = 0. # Define timestep-related parameters # Courant stability constraint for timestep D0 = 1.0e-5 safetyFactor = 0.9 timestepDuration = safetyFactor * dx**2 / (2 * D0) # Simulation duration steps = 500 print "Nsteps =",steps t = timestepDuration * steps # Now define your sweep variables arvar = [CellVariable(name=arlab, mesh=mesh, value=arRight, hasOld=1)]

At this point, we can define the Fipy equation that we're solving, which is, as given above,

and the diffusion coefficient is a function of . This is subject to the boundary conditions set above (1 on the left side of the domain, 0 on the right side of the domain).

# Define the equation being solved eq = TransientTerm() == DiffusionTerm(coeff=get_diffusion_coeff(arvar[0])) # Set the boundary conditions arvar[0].constrain(arLeft, mesh.facesLeft) arvar[0].constrain(arRight, mesh.facesRight)

Now we timestep through the solutions, and plot the solution every 50 timesteps. At each timestep, we are performing 5 sweeps (that is, we're performing 5 iterations per timestep). Again, our diffusion coefficient is not a strong function of composition, because Ar and O2 are so similar in shape/properties, so even 5 sweeps is probably unnecessary. Again, this is purely for illustrative purposes.

# Timestep through solutions arvar[0].setValue(arRight) for step in range(steps): # only move forward in time once per time step arvar[0].updateOld() # but "sweep" many times per time step for sweep in range(5): res = eq.sweep(var=arvar[0], dt=timestepDuration) if step%50==0: ax.plot(xs,arvar[0].value) ax.hold(True) plt.show() plt.draw()

# Result

This will result in the following plot:

# References

Fipy documentation covering 1D diffusion problems: http://www.ctcms.nist.gov/fipy/examples/diffusion/generated/examples.diffusion.mesh1D.html

CanteraCantera
Outline of Cantera topics: Cantera Outline Understanding Cantera's Structure: Cantera Structure Cantera from Matlab: Using_Cantera#Matlab Cantera from Python: Using_Cantera#Python Cantera from C++: Using_Cantera#C++ Cantera + Fipy (PDE Solver): Cantera Gas Objects: Cantera/Gases Cantera 1D Domains, Stacks: Cantera_One-D_Domains Cantera Gas Mixing: Cantera_Gas_Mixing
Diffusion: Cantera/Diffusion Sensitivity Analysis: Cantera/Sensitivity Analysis Analysis of the Jacobian Matrix in Cantera: Jacobian_in_Cantera Chemical Equilibrium: Chemical_Equilibrium Kinetic Mechanisms: Cantera/Kinetic_Mechanisms Reactor Equations: Cantera/Reactor_Equations Differential vs. Integral Reactors: Cantera/Integral_and_Differential_Reactors Effect of Dilution on Adiabatic Flame Temperature: Cantera/Adiabatic_Flame_Temperature_Dilution
Cantera for Catalysis: Cantera_for_Catalysis Steps for Modeling 0D Multiphase Reactor: Cantera_Multiphase_Zero-D Reaction Rate Source Terms: Cantera/Reaction_Rate_Source_Terms Surface coverage: Cantera/Surface_Coverage Surface reactions: Cantera/Surface_Reactions
Chemkin file format: Chemkin CTI files: Cantera/CTI_Files
Pantera (monkey patches and convenience functions for Cantera): Pantera Extending Cantera's C API: Cantera/Extending_C_API Extending Cantera with Python Classes: Cantera/Adding Python Class Debugging Cantera: Cantera/Debugging_Cantera Debugging Cantera from Python: Cantera/Debugging_Cantera_from_Python Gas Mixing Functions: Cantera_Gas_Mixing Residence Time Reactor (new Cantera class): Cantera/ResidenceTimeReactor
Cantera Resources: Cantera Resources Cantera Lecture Notes: Cantera_Lecture
Category:Cantera Category:C++ · Template:CanteraFlag · e |

Installing Cantera
Mac OS X 10.5 (Leopard): Installing_Cantera#Leopard Mac OS X 10.6 (Snow Leopard): Installing_Cantera#Snow_Leopard Mac OS X 10.7 (Lion): Installing_Cantera#Lion Mac OS X 10.8 (Mountain Lion): Installing_Cantera#Mountain_Lion Ubuntu 12.04 (Precise Pangolin): Installing_Cantera#Ubuntu Windows XP: Installing_Cantera#Windows_XP Windows 7: Installing_Cantera#Windows_7
In old versions of Cantera, a preconfig file was used to specify library locations and options. Mac OS X 10.5 (Leopard) preconfig: Cantera_Preconfig/Leopard_Preconfig Mac OS X 10.6 (Snow Leopard) preconfig: Cantera_Preconfig/Snow_Leopard_Preconfig Mac OS X 10.8 (Mountain Lion) preconfig: Cantera_Config/MountainLion_SconsConfig Ubuntu 12.04 (Precise Pangolin) preconfig: Cantera_Config/Ubuntu1204_SconsConfig Flags· Template:InstallingCanteraFlag · e |