PythonFOAM:

In-situ data analyses with OpenFOAM and Python

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

You can find an extensive hands-on tutorial, courtesy of the ALCF PythonFOAM workshop, here: https://www.youtube.com/watch?v=-Sa2OEssru8

Prerequisites

• OpenFOAM
• numpy (python) with devel headers
• tensorflow (python) ### Version 2.1
• matplotlib (python)
• python-dev-tools (python)

Contents

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.

To compile and run

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/.

Update - 12/16/2022

You can now debug the C++ components of PythonFOAM with visual studio code. For this you need to have OpenFOAM-8 built in debug mode. Here is a quick tutorial to do so:

1. Download OpenFOAM-8 source

git clone https://github.com/OpenFOAM/OpenFOAM-8.git
git clone https://github.com/OpenFOAM/ThirdParty-8.git

Go to line 84 in OpenFOAM-8/etc/bashrc and

export WM_COMPILE_OPTION=Debug

then use source OpenFOAM-8/etc/bashrc to load environment variables. After this step, go to ThirdParty-8/ and use ./Allwmake. After - go to OpenFOAM-8/ and use ./Allwmake -j. (Note we are skipping Paraview compilation). We recommend keeping one build of debug OpenFOAM and one build of optimized OpenFOAM on your system at all times.

2. Download Visual studio and make sure your visual studio has C/C++ (intellisense and extension pack) extensions.

3. Navigate to your solver build directory - here let us use PODFoam_Debug/ as an example. This folder has the files and wmake instructions to build PODFoam_Debug - you will note that the folder also shares the directories required to run a CFD case (i.e., the contents of run_case/ are in the same build directory). This is required for debug mode execution of our solver.

4. Create a new hidden folder in the PODFoam_Debug directory called .vscode/. In it create 4 files

launch.json
c_cpp_properties.json
tasks.json
settings.json

Use the files in PODFoam_Debug/.vscode in this repository to add file contents (further information here: https://github.com/Rvadrabade/Debugging-OpenFOAM-with-Visual-Studio-Code/).

5. In a new terminal - source prep_env.sh -debug to ensure that you are running with the debug version of OpenFOAM. Note that here you have to make sure you are pointing to your correct bashrc. The links in this example are for my personal machine. Follow previous steps to compile a debug version of PODFoam_Debug from PODFoam_Debug/. There should be no issues here.

6. Navigate to PODFoam_Debug/ and run visual studio code with code .. Set a breakpoint in PODFoam_Debug.C and hit F5 in the debug panel to initialize debugging. Standard gdb rules apply hereon.

Note we are still investigating mixed-mode debugging for C++ and Python.

Docker

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 ([email protected]). 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. Several aspects of this research were also performed at the Department of Computer Science at IIT-Chicago (SPEAR Team with support from NSF Award #2119294-PI Zhiling Lan).

LICENSE

Argonne open source for the Python integration