A simple finite volume tool for Julia
Author simulkade
25 Stars
Updated Last
2 Years Ago
Started In
December 2014


Build Status



  • The code now works with Julia 1.0. All you need to do is to check out the master branch:
] add https://github.com/simulkade/JFVM.jl
  • 3D visualization requires calling Mayavi via PyCall. It made too many problems recently, so I have decided to disable it until I find a better solution for 3D visualization. Suggestions/PRs are very welcome.
  • I have decided to move the visualization to a new package JFVMvis.jl, that ypu need to install by:
] add https://github.com/simulkade/JFVMvis.jl


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:

  • Cartesian (1D, 2D, 3D)
  • Cylindrical (1D, 2D, 3D)
  • Radial (2D r and \theta)

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

  • Dirichlet (constant value)
  • Neumann (constant flux)
  • Robin (a linear combination of the above)
  • Periodic (so funny when visualize)

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.


You need to have matplotlib (only for visualization)


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


  • open Julia and type
] add https://github.com/simulkade/JFVM.jl
  • For visualization, download and install Anaconda
    Run anaconda command prompt (as administrator) and install matplotlib by
conda install matplotlib

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


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)
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

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

More examples


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

  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}