wglao/mct-jaxfluids

Differentiable Fluid Dynamics Package with mcTangent

JAX-Fluids: A Differentiable Fluid Dynamics Package

JAX-Fluids is a fully-differentiable CFD solver for 3D, compressible two-phase flows. We developed this package with the intention to push and facilitate research at the intersection of ML and CFD. It is easy to use - running a simulation only requires a couple lines of code. Written entirely in JAX, the solver runs on CPU/GPU/TPU and enables automatic differentiation for end-to-end optimization of numerical models.

To learn more about implementation details and details on numerical methods provided by JAX-Fluids, feel free to read our paper. And also check out the documentation of JAX-Fluids.

Authors:

• Deniz A. Bezgin
• Aaron B. Buhendwa
• Nikolaus A. Adams

Correspondence via mail.

Physical models and numerical methods

JAX-Fluids solves the Navier-Stokes-equations using the finite-volume-method on a Cartesian grid. The current version provides the following features:

• Explicit time stepping (Euler, RK2, RK3)
• High-order adaptive spatial reconstruction (WENO-3/5/7, WENO-CU6, WENO-3NN, TENO)
• Riemann solvers (Lax-Friedrichs, Rusanov, HLL, HLLC, Roe)
• Implicit turbulence sub-grid scale model ALDM
• Two-phase simulations via level-set method
• Immersed solid boundaries via level-set method
• Forcings for temperature, mass flow rate and kinetic energy spectrum
• Boundary conditions: Symmetry, Periodic, Wall, Dirichlet, Neumann
• CPU/GPU/TPU capability

Example simulations

Pip Installation

Before installing JAX-Fluids, please ensure that you have an updated and upgraded pip version.

CPU-only support

To install the CPU-only version of JAX-Fluids, you can run

git clone https://github.com/tumaer/JAXFLUIDS.git
cd JAXFLUIDS
pip install .

Note: if you want to install JAX-Fluids in editable mode, e.g., for code development on your local machine, run

pip install --editable .

Note: if you want to use jaxlib on a Mac with M1 chip, check the discussion here.

GPU and CPU support

If you want to install JAX-Fluids with CPU and GPU support, you must first install CUDA - we have tested JAX-Fluids with CUDA 11.1 or newer. After installing CUDA, run the following

git clone https://github.com/tumaer/JAXFLUIDS.git
cd JAXFLUIDS
pip install .[cuda] -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html

For more information on JAX on GPU please refer to the github of JAX

Quickstart

This github contains five jupyter-notebooks which will get you started quickly. They demonstrate how to run simple simulations like a 1D sod shock tube or a 2D supersonic cylinder flow. Furthermore, they show how you can easily switch the numerical and/or case setup in order to, e.g., increase the order of the spatial reconstruction stencil or decrease the resolution of the simulation.

Upcoming features

• 5-Equation diffuse interface model for multiphase flows
• CPU/GPU/TPU parallelization based on homogenous domain decomposition

Documentation

Check out the documentation of JAX-Fluids.

Citation

https://doi.org/10.1016/j.cpc.2022.108527

@article{BEZGIN2022108527,
   title = {JAX-Fluids: A fully-differentiable high-order computational fluid dynamics solver for compressible two-phase flows},
   journal = {Computer Physics Communications},
   pages = {108527},
   year = {2022},
   issn = {0010-4655},
   doi = {https://doi.org/10.1016/j.cpc.2022.108527},
   url = {https://www.sciencedirect.com/science/article/pii/S0010465522002466},
   author = {Deniz A. Bezgin and Aaron B. Buhendwa and Nikolaus A. Adams},
   keywords = {Computational fluid dynamics, Machine learning, Differential programming, Navier-Stokes equations, Level-set, Turbulence, Two-phase flows}
} 

License

This project is licensed under the GNU General Public License v3 - see the LICENSE file or for details https://www.gnu.org/licenses/.