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JFVM

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IMPORTANT NOTES

] add https://github.com/simulkade/JFVM.jl
] add https://github.com/simulkade/JFVMvis.jl

Equations

You can solve the following PDE (or a subset of it):
advection diffusion

with the following boundary conditions:
boundary condition

Believe it or not, the above equations describe the majority of the transport phenomena in chemical and petroleum engineering and similar fields.

A simple finite volume tool written in Julia

This code is a Matlabesque implementation of my Matlab finite volume tool. The code is not in its most beautiful form, but it works if you believe my words. Please remember that the code is written by a chemical/petroleum engineer. Petroleum engineers are known for being simple-minded folks and chemical engineers have only one rule: "any answer is better than no answer". You can expect to easily discretize a linear transient advection-diffusion PDE into the matrix of coefficients and RHS vectors. Domain shape is limited to rectangles, circles (or a section of a circle), cylinders, and soon spheres. The mesh can be uniform or nonuniform:

You can have the following boundary conditions or a combination of them on each boundary:

It is relatively easy to use the code to solve a system of coupled linear PDE's and not too difficult to solve nonlinear PDE's.

Installation

You need to have matplotlib (only for visualization)

Linux

In Ubuntu-based systems, try

sudo apt-get install python-matplotlib

Then install JFVM by the following commands:

] add https://github.com/simulkade/JFVM.jl

Windows

] add https://github.com/simulkade/JFVM.jl
conda install matplotlib

Please let me know if it does not work on your windows machines.

Tutorial

I have written a short tutorial, which will be extended gradually.

In action

Copy and paste the following code to solve a transient diffusion equation:

using JFVM, JFVMvis
Nx = 10
Lx = 1.0
m = createMesh1D(Nx, Lx)
BC = createBC(m)
BC.left.a[:].=BC.right.a[:].=0.0
BC.left.b[:].=BC.right.b[:].=1.0
BC.left.c[:].=1.0
BC.right.c[:].=0.0
c_init = 0.0 # initial value of the variable
c_old = createCellVariable(m, 0.0, BC)
D_val = 1.0 # value of the diffusion coefficient
D_cell = createCellVariable(m, D_val) # assigned to cells
# Harmonic average
D_face = harmonicMean(D_cell)
N_steps = 20 # number of time steps
dt= sqrt(Lx^2/D_val)/N_steps # time step
M_diff = diffusionTerm(D_face) # matrix of coefficient for diffusion term
(M_bc, RHS_bc)=boundaryConditionTerm(BC) # matrix of coefficient and RHS for the BC
for i =1:5
    (M_t, RHS_t)=transientTerm(c_old, dt, 1.0)
    M=M_t-M_diff+M_bc # add all the [sparse] matrices of coefficient
    RHS=RHS_bc+RHS_t # add all the RHS's together
    c_old = solveLinearPDE(m, M, RHS) # solve the PDE
end
visualizeCells(c_old)

Now change the 4th line to m=createMesh2D(Nx, Nx, Lx, Lx) and see this: diffusion 2D

More examples

TO DO

IJulia notebooks

How to cite

If you have used the code in your research, please cite it as

Ali A Eftekhari. (2017, August 23). JFVM.jl: A Finite Volume Tool for Solving Advection-Diffusion Equations. Zenodo. http://doi.org/10.5281/zenodo.847056

@misc{ali_a_eftekhari_2017_847056,
  author       = {Ali A Eftekhari},
  title        = {{JFVM.jl: A Finite Volume Tool for Solving 
                   Advection-Diffusion Equations}},
  month        = aug,
  year         = 2017,
  doi          = {10.5281/zenodo.847056},
  url          = {https://doi.org/10.5281/zenodo.847056}
}