PythonFOAM:

Using Python modules for in-situ data analytics with OpenFOAM. NOTE that this is NOT PyFOAM which is an automation tool for running OpenFOAM cases. What you see in this repository, is OpenFOAM calling Python functions and classes for in-situ data analytics. You may offload some portion of your compute task to Python for a variety of reasons (chiefly data-driven tasks using the Python ML ecosystem and quick prototyping of algorithms).

OpenFOAM versions that should compile without changes:

• openfoam.com versions: v2012, v2106
• openfoam.org versions: 8

• OpenFOAM
• numpy (python) with devel headers
• tensorflow (python)
• matplotlib.pyplot (python)

We have changed instructions to compile and run our examples by automating some of the environment variable declarations. We have also added an example of calling Python from a turbulence model implementation.

1. Solver_Examples/

   1. PODFoam/: A pimpleFoam solver with in-situ collection of snapshot data for a streaming singular value decomposition. Python bindings are used to utilize a Python Streaming-SVD class object from OpenFOAM.

   2. APMOSFoam/: A pimpleFoam solver with in-situ collection of snapshot data for a parallelized singular value decomposition. While the previous example performs the SVD on data only on one rank - this solver performs a global, but distributed, SVD. However, SVD updates are not streaming.

   3. AEFoam/: A pimpleFoam solver with in-situ collection of snapshot data for training a deep learning autoencoder.

2. Turbulence_Model_Examples/ (Work in progress) See detailed README.md in this folder.

Inspect prep_env.sh to set paths to various Python, numpy headers and libraries and to source your OpenFOAM 8 installation. Replace these with the include/lib paths to your personal Python environments. The Python module within Run_Case/ directories of different Solvers/ require the use of numpy, matplotlib, and tensorflow so ensure that your environment has these installed. The best way to obtain these is to pip install tensorflow==2.1 which will automatically find the right numpy dependency and then pip install matplotlib to obtain plot capability. You will also need to install mpi4py which you can using pip install mpi4py.

1. Solvers: After running source prep_env.sh, to run the solver examples go into the respective folder (for example PODFoam/) and use wclean && wmake to build your model. Run your solver example from Run_Case/. Note the presence of python_module.py within Run_Case/.

2. Turbulence model examples: See README.md in Turbulence_Model_Examples/.

A Docker container with the contents of this repo is available here. You can use

docker pull romitmaulik1/pythonfoam_docker:latest

on a machine with docker in it to download an image that has PythonFOAM set up on it. Subsequently

docker run -t -d -e DISPLAY=$DISPLAY -v /tmp/.X11-unix:/tmp/.X11-unix --privileged --name pythonfoam_container romitmaulik1/pythonfoam_docker
xhost +local:docker # For running GUI applications from docker
docker start pythonfoam_container
docker exec -i -t pythonfoam_container /bin/bash

will create a container (named pythonfoam_container) from the image and start a shell for you to run experiments. Navigate to /home/PythonFOAM within the shell to obtain the source code and test cases. For a quick crash course on using Docker, see this tutorial by Jean Rabault.

Points of contact for further assistance - Romit Maulik (rmaulik@anl.gov). This work was performed by using the resources of the Argonne Leadership Computing Facility, a U.S. Department of Energy (Office of Science) user facility at Argonne National Laboratory, Lemont, IL, USA.

Argonne open source for the Python integration