rodrigomrxvi / pythonfoam

In-situ data analyses and machine learning with OpenFOAM and Python

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

Prerequisites

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

Update - 02/04/2022

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.

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/.

Docker

Note - The docker container is outdated and refers to an older commit (1686978d8b50e1f7b44fe7a710e8cdae85cae56d)

A Docker container with the contents of this repo is available here. You can use docker pull romitmaulik1/pythonfoam_docker:reproduced on a machine with docker in it, or singularity build pythonfoam.img docker://romitmaulik1/pythonfoam_docker:reproduced. Do not forget to ensure OpenFOAM is sourced and available in your path by using source /opt/openfoam8/etc/bashrc. For a quick crash course on using Docker, see this tutorial by Jean Rabault. Singularity resources may be found here.

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.

LICENSE

Argonne open source for the Python integration