PeleLM

Overview

PeleLM is an adaptive-mesh low Mach number hydrodynamics code for reacting flows. PeleLM supports Embedded Boundary method to represent complex geometries and is parallelized with MPI + OpenMP for CPUs and MPI + CUDA or MPI + HIP for GPUs.

PeleLM is part of the Pele combustion Suite and PeleLM has a project homepage. Use this link to sign up for the PeleLM user forum, where updates and significant changes will be posted. The forum is also where general questions can be posted about building and running the code, processing code output, and details about the algorithm and its implementation.

Documentation

PeleLM complete documentation is available on ReadTheDoc. It is also possible to build a local version of the documentation once you have obtained the source code using :

    cd ${PELELM_HOME}/Docs
    make html

Getting started

A first simple 2D flame problem is available in the PeleLM QuickStart section:

https://pelelm.readthedocs.io/en/latest/GettingStarted.html

Core Algorithm

The PeleLM governing equations and core algorithm are described in:

https://pelelm.readthedocs.io/en/latest/Model.html

Tutorials

A set of self-contained tutorials describing more complex problems is also provided:

https://pelelm.readthedocs.io/en/latest/Tutorials.html

Contributing

New contributions to PeleLM are welcome !

The PeleLM contributions workflow follows these steps:

1. Fork the main repository
2. Create an AmazingNewFeature branch implementing your changes
3. Open a Pull Request from AmazingNewFeature on your fork to branch development of the main PeleLM repository

Follow GitHub directions to fork PeleLM main repo on your GitHub account, then clone the PeleLM dependencies (PelePhysics, IAMR,AMReX-Hydro, AMReX) along with your own PeleLM fork on your local machine.

Then step into the PeleLM folder and add the main PeleLM repository as the upstream remote in order to keep track of the main repo :

   git add remote upstream https://github.com/AMReX-Combustion/PeleLM

At any point, you can update the developement branch of your local repository with changes implemented in the main PeleLM repo by pulling from upstream :

    git checkout development
    git pull upstream development

You are now free to modify your own fork of PeleLM. To add a new feature to PeleLM, the procedure is:

1. Create a branch for the new feature from the development branch (locally) :

    git checkout development 
    git checkout -b AmazingNewFeature
   

2. and commit your changes to your local repo :

    git commit -m "Developed AmazingNewFeature"
   

3. Alongside your development, regularly merge changes from the main repo development branch into your AmazingNewFeature branch, fix any conficts, and push your changes to your GitHub fork :

    git pull upstream development     [Fix arising conflicts]
    git push -u origin AmazingNewFeature 
   

4. When you are ready to propose your new feature/improvement/bug fix to the main PeleLM repo, reiterate Step 3 and submit a Pull Request through the GitHub page from your fork onto the development branch of the main repo:

• Click on the compare & pull request button to start your PR.
• Provide a title and a short description for your PR:
  • what feature/fix do you propose
  • how did you test it
  • any other information deemed useful : does it modify the default PeleLM behavior ? ...
• Press Create pull request.

Please DO NOT write large Pull Requests, as they are very difficult and time-consuming to review. As much as possible, split them into small targeted PRs. For example, if find typos in the documentation open a pull request that only fixes typos. If you want to fix a bug, make a small pull request that only fixes a bug.

Acknowledgment

This research was supported by the Exascale Computing Project (ECP), Project Number: 17-SC-20-SC, a collaborative effort of two DOE organizations -- the Office of Science and the National Nuclear Security Administration -- responsible for the planning and preparation of a capable exascale ecosystem -- including software, applications, hardware, advanced system engineering, and early testbed platforms -- to support the nation's exascale computing imperative.