EchemFEM
EchemFEM: A Firedrake-based Python package for electrochemical transport - Published in JOSS (2024)
Science Score: 98.0%
This score indicates how likely this project is to be science-related based on various indicators:
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○CITATION.cff file
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✓codemeta.json file
Found codemeta.json file -
✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 10 DOI reference(s) in README and JOSS metadata -
✓Academic publication links
Links to: joss.theoj.org, zenodo.org -
✓Committers with academic emails
5 of 5 committers (100.0%) from academic institutions -
✓Institutional organization owner
Organization llnl has institutional domain (software.llnl.gov) -
✓JOSS paper metadata
Published in Journal of Open Source Software
Keywords
Repository
Finite Element Method for Electrochemical Transport (EchemFEM)
Basic Info
Statistics
- Stars: 24
- Watchers: 7
- Forks: 5
- Open Issues: 0
- Releases: 2
Topics
Metadata Files
README.md
EchemFEM
This code provides Finite Element solvers for electrochemical transport. Both continuous Galerkin (CG) and discontinuous Galerkin (DG) schemes are provided. The DG scheme for electroneutral Nernst-Planck is described in Roy et al., 2023. The CG scheme uses SUPG for the advection-migration term.
The following transport mechanisms are available: diffusion, advection, electromigration. EchemFEM supports both non-porous and porous cases. The ionic potential can either be described using an electroneutrality constraint or a Poisson equation. In the porous case, the electronic potential can be described by a Poisson equation. Some finite size effects are also implemented. For example, the generalized modified Poisson-Nernst-Planck model (GMPNP) is used in the examples of FireCat, coupling in with a microkinetics model.
LLNL-CODE-837342
Documentation
User guide and API documentation can be found here documentation.
Installation
Please install the open-source finite element library Firedrake.
To install EchemFEM, simply run the following in the parent echemfem folder:
pip install -e .
The documentation has more details about installation and running the code.
Citing
To cite EchemFEM, please cite the JOSS paper. For the DG discretization, please cite the JCP paper.
Owner
- Name: Lawrence Livermore National Laboratory
- Login: LLNL
- Kind: organization
- Email: github-admin@llnl.gov
- Location: Livermore, CA, USA
- Website: https://software.llnl.gov
- Twitter: LLNL_OpenSource
- Repositories: 520
- Profile: https://github.com/LLNL
For over 70 years, the Lawrence Livermore National Laboratory has applied science and technology to make the world a safer place.
JOSS Publication
EchemFEM: A Firedrake-based Python package for electrochemical transport
Authors
TotalEnergies OneTech, France
TotalEnergies OneTech, France
Tags
Firedrake Finite Element Method electrochemistryGitHub Events
Total
- Watch event: 2
- Push event: 3
Last Year
- Watch event: 2
- Push event: 3
Committers
Last synced: 5 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Thomas Roy | r****7@l****v | 118 |
| Jack Guo | g****9@l****v | 6 |
| ehlinger | e****1@l****v | 5 |
| Nitish Govindarajan | g****1@l****v | 1 |
| Nitish Govindarajan | n****o@r****v | 1 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 4 months ago
All Time
- Total issues: 7
- Total pull requests: 7
- Average time to close issues: 2 months
- Average time to close pull requests: about 10 hours
- Total issue authors: 3
- Total pull request authors: 2
- Average comments per issue: 4.14
- Average comments per pull request: 0.29
- Merged pull requests: 7
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 0
- Pull requests: 0
- Average time to close issues: N/A
- Average time to close pull requests: N/A
- Issue authors: 0
- Pull request authors: 0
- Average comments per issue: 0
- Average comments per pull request: 0
- Merged pull requests: 0
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- TomTranter (3)
- sudarshanv01 (3)
- alizma (1)
Pull Request Authors
- tlroy (4)
- jguo96 (3)
Top Labels
Issue Labels
Pull Request Labels
Dependencies
- pandas *
