FLuid And Machine learning Engine 3D
Code for compressible flow simulation with neural network for real gas and chemical reaction.
- Use
CUDA.jl
,AMDGPU.jl
andMPI.jl
for multi-GPU parallelization - 3D, with high order scheme (up to 7th order)
- LES modeling for complex flows
- Use
Lux.jl
trained neural network model for chemical reaction prediction - GPU kernel for fast chemical reaction evaluation
- Call
Cantera
to parse the input file, easy to use - Call
Cantera
for chemical reaction evaluation, either Python or C++ interface - Use
HDF5
andVTK
for I/O - Use
matplotlib
,Makie.jl
orParaview
for post-processing - Use high order sharp interface immersed boundary method on curvilinear coordinates to represent complex geometry (work in progress)
- Be able to simulate incompressible flow efficiently (work in progress)
Generate mesh and metrics with
julia parse_mesh.jl
It will compute metrics and store it to metrics.h5
, if you want to visualize the mesh, make sure const vis::Bool = true
, then the mesh.vts
file can be opened with Paraview, etc.
Run the simulation with
julia run.jl
Or to use multi-GPU, make Nprocs=<nprocs>
and run it with mpiexecjl -n <nprocs> julia run.jl
.
For more details, checkout the comments in run.jl
.
For post-processing, a sample code is provided in Utils/plot.jl
. It can be used to visualize and analyze the result. Alternatively, Paraview
or Makie.jl
can also be used for 3D rendering.
Turner, J. M., Seo, J. H., & Mittal, R. (2024). A high-order sharp-interface immersed boundary solver for high-speed flows. Journal of Computational Physics, 500, 112748.
Clausen, J. R. (2013). Entropically damped form of artificial compressibility for explicit simulation of incompressible flow. Physical Review E, 87(1), 013309.