https://github.com/arpit-babbar/trixilw.jl

Lax-Wendroff Flux Reconstruction on curvilinear grids

https://github.com/arpit-babbar/trixilw.jl

Science Score: 59.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .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
  • Institutional organization owner
  • JOSS paper metadata
  • Scientific vocabulary similarity
    Low similarity (14.6%) to scientific vocabulary
Last synced: 7 months ago · JSON representation

Repository

Lax-Wendroff Flux Reconstruction on curvilinear grids

Basic Info
  • Host: GitHub
  • Owner: Arpit-Babbar
  • Language: Julia
  • Default Branch: master
  • Homepage:
  • Size: 987 KB
Statistics
  • Stars: 5
  • Watchers: 2
  • Forks: 0
  • Open Issues: 5
  • Releases: 1
Created almost 3 years ago · Last pushed 12 months ago
Metadata Files
Readme

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

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

All Time
  • Total Commits: 100
  • Total Committers: 2
  • Avg Commits per committer: 50.0
  • Development Distribution Score (DDS): 0.02
Past Year
  • Commits: 18
  • Committers: 2
  • Avg Commits per committer: 9.0
  • Development Distribution Score (DDS): 0.111
Top Committers
Name Email 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)
Top Labels
Issue Labels
Pull Request Labels