https://github.com/arpit-babbar/trixilw.jl
Lax-Wendroff Flux Reconstruction on curvilinear grids
Science Score: 59.0%
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○CITATION.cff file
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✓codemeta.json file
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✓.zenodo.json file
Found .zenodo.json file -
✓DOI references
Found 2 DOI reference(s) in README -
✓Academic publication links
Links to: sciencedirect.com -
✓Committers with academic emails
1 of 2 committers (50.0%) from academic institutions -
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○Scientific vocabulary similarity
Low similarity (14.6%) to scientific vocabulary
Repository
Lax-Wendroff Flux Reconstruction on curvilinear grids
Basic Info
Statistics
- Stars: 5
- Watchers: 2
- Forks: 0
- Open Issues: 5
- Releases: 1
Metadata Files
README.md
TrixiLW.jl
TrixiLW.jl in implementation of Lax-Wendroff Flux Reconstruction scheme for curvilinear meshes with adaptive mesh refinement and error based time stepping using Trixi.jl as a library. To run the code, enter the following in the julia REPL.
Users
Using the multiple dispatch of julia, most things that you wish to do with the code (including developing your own algorithms) can be done by working with TrixiLW.jl as a user. Execute the following in the julia REPL.
julia
julia> import Pkg; Pkg.add(url="https://github.com/arpit-babbar/TrixiLW.jl.git")
Then, TrixiLW.jl can be loaded as a julia package using the following command
julia
julia> using TrixiLW
You can also run any of the available examples. The first time you use using and the first time you use an example will be slower than the subsequent times.
Developers
You should do this if you find something in TrixiLW.jl to be incompatible with your use case. In this case, I will also be happy to make changes in TrixiLW.jl to adapt it to your needs. This is likely to greatly help TrixiLW.jl. Thus, feel free to raise an issue, make a pull request or email me. For development, clone (ideally, after forking) the repository and then run the following in the julia REPL when you are in the TrixiLW.jl directory to install the dependencies
julia
julia> using Pkg; Pkg.activate("."); Pkg.instantiate()
You can skip the Pkg.activate(".") command by starting julia as julia --project=. in the TrixiLW.jl directory. You can now run any example, e.g., as
julia
julia> include("examples/p4est_2d_dgsem/elixir_advection_basic.jl")
Cite us!
If you use this code in your work, please cite us as
bibtex
@article{BabbarChandrashekar2025,
title = {Lax-Wendroff flux reconstruction on adaptive curvilinear meshes with error based time stepping for hyperbolic conservation laws},
journal = {Journal of Computational Physics},
volume = {522},
pages = {113622},
year = {2025},
issn = {0021-9991},
doi = {https://doi.org/10.1016/j.jcp.2024.113622},
url = {https://www.sciencedirect.com/science/article/pii/S0021999124008702},
author = {Arpit Babbar and Praveen Chandrashekar},
keywords = {Hyperbolic conservation laws, Lax-Wendroff flux reconstruction, Curvilinear grids, Admissibility preservation and shock capturing, Adaptive mesh refinement, Error based time stepping},
abstract = {Lax-Wendroff Flux Reconstruction (LWFR) is a single-stage, high order, quadrature free method for solving hyperbolic conservation laws. This work extends the LWFR scheme to solve conservation laws on curvilinear meshes with adaptive mesh refinement (AMR). The scheme uses a subcell based blending limiter to perform shock capturing and exploits the same subcell structure to obtain admissibility preservation on curvilinear meshes. It is proven that the proposed extension of LWFR scheme to curvilinear grids preserves constant solution (free stream preservation) under the standard metric identities. For curvilinear meshes, linear Fourier stability analysis cannot be used to obtain an optimal CFL number. Thus, an embedded-error based time step computation method is proposed for LWFR method which reduces fine-tuning process required to select a stable CFL number using the wave speed based time step computation. The developments are tested on compressible Euler's equations, validating the blending limiter, admissibility preservation, AMR algorithm, curvilinear meshes and error based time stepping.}
}
Owner
- Name: Arpit Babbar
- Login: Arpit-Babbar
- Kind: user
- Repositories: 3
- Profile: https://github.com/Arpit-Babbar
GitHub Events
Total
- Issues event: 1
- Watch event: 4
- Push event: 44
Last Year
- Issues event: 1
- Watch event: 4
- Push event: 44
Committers
Last synced: 12 months ago
Top Committers
| Name | Commits | |
|---|---|---|
| Arpit-Babbar | a****r@g****m | 98 |
| Devansh Tripathi | d****2@i****n | 2 |
Committer Domains (Top 20 + Academic)
Issues and Pull Requests
Last synced: 7 months ago
All Time
- Total issues: 9
- Total pull requests: 30
- Average time to close issues: 13 days
- Average time to close pull requests: 1 day
- Total issue authors: 2
- Total pull request authors: 2
- Average comments per issue: 0.11
- Average comments per pull request: 0.2
- Merged pull requests: 28
- Bot issues: 0
- Bot pull requests: 0
Past Year
- Issues: 4
- Pull requests: 4
- Average time to close issues: 19 days
- Average time to close pull requests: 2 days
- Issue authors: 2
- Pull request authors: 2
- Average comments per issue: 0.0
- Average comments per pull request: 1.25
- Merged pull requests: 4
- Bot issues: 0
- Bot pull requests: 0
Top Authors
Issue Authors
- Arpit-Babbar (7)
- andrewwinters5000 (1)
Pull Request Authors
- Arpit-Babbar (24)
- Devansh1106 (1)