AeroTrixi.jl

AeroTrixi.jl is a package for high-fidelity aerodynamic simulations built on top of Trixi.jl. Currently, it extends Trixi.jl's capabilities with a specialized analysis callback for aerodynamic applications. In the future, we plan to add more features for nonideal and rarefied gases, for instance.

Features

• Surface pressure and friction coefficients: Compute and save pointwise aerodynamic coefficients along boundaries
• Thermodynamic data: tabulated internal energies and specific heats with interpolation and temperature inversion routines for multi-species flows via the ThermoData1T type
• Entropy-conservative/stable fluxes for non-calorically perfect multi-species flows: CompressibleEulerEquationsMs1T2D type for 2D multi-species equations, entropy conservative fluxes, based on tabulated thermodynamic data

Installation

AeroTrixi.jl is registered in the Julia General Registry. Install it using:

using Pkg
Pkg.add("AeroTrixi")

Quick Start

To get started, it is best to take a look at the examples. Currently, the additional functionality provided by AeroTrixi.jl is

• the extended AnalysisCallback which can be used to compute pointwise aerodynamic coefficients along boundaries,

such as SurfacePressureCoefficient and SurfaceFrictionCoefficient

• the ThermoData1T type for tabulated thermodynamic data for multi-species flows in thermal equilibrium
• the CompressibleEulerEquationsMs1T2D type for 2D multi-species flows, which is based on tabulated thermodynamic data

Documentation

See the API Reference for documentation on available/extended functions.

Credit

Referencing

If you use AeroTrixi.jl in your research, you should cite the upstream Trixi.jl repository:

@article{ranocha2022adaptive,
  title={Adaptive numerical simulations with {T}rixi.jl:
         {A} case study of {J}ulia for scientific computing},
  author={Ranocha, Hendrik and Schlottke-Lakemper, Michael and Winters, Andrew Ross
          and Faulhaber, Erik and Chan, Jesse and Gassner, Gregor},
  journal={Proceedings of the JuliaCon Conferences},
  volume={1},
  number={1},
  pages={77},
  year={2022},
  doi={10.21105/jcon.00077},
  eprint={2108.06476},
  eprinttype={arXiv},
  eprintclass={cs.MS}
}

@article{schlottkelakemper2021purely,
  title={A purely hyperbolic discontinuous {G}alerkin approach for
         self-gravitating gas dynamics},
  author={Schlottke-Lakemper, Michael and Winters, Andrew R and
          Ranocha, Hendrik and Gassner, Gregor J},
  journal={Journal of Computational Physics},
  pages={110467},
  year={2021},
  month={06},
  volume={442},
  publisher={Elsevier},
  doi={10.1016/j.jcp.2021.110467},
  eprint={2008.10593},
  eprinttype={arXiv},
  eprintclass={math.NA}
}

In addition, you can also refer to Trixi.jl directly as

@misc{schlottkelakemper2025trixi,
  title={{T}rixi.jl: {A}daptive high-order numerical simulations
         of hyperbolic {PDE}s in {J}ulia},
  author={Schlottke-Lakemper, Michael and Gassner, Gregor J and
          Ranocha, Hendrik and Winters, Andrew R and Chan, Jesse
          and Rueda-Ramírez, Andrés},
  year={2025},
  howpublished={\url{https://github.com/trixi-framework/Trixi.jl}},
  doi={10.5281/zenodo.3996439}
}

If using the entropy-conservative multi-species fluxes provided with CompressibleEulerEquationsMs1T2D, please also cite

@article{oblapenko2025entropyconservative,
  title={Entropy-conservative high-order methods for high-enthalpy gas flows},
  author={Oblapenko, Georgii and Torrilhon, Manuel},
  journal={Computers \& Fluids},
  volume={295},
  pages={106640},
  year={2025},
  publisher={Elsevier},
  doi={10.1016/j.compfluid.2025.106640}
}

@inproceedings{oblapenko2024entropy,
  title={Entropy-stable fluxes for high-order Discontinuous Galerkin simulations of high-enthalpy flows},
  author={Oblapenko, Georgii and Tarnovskiy, Arseniy and Ertl, Moritz and Torrilhon, Manuel},
  booktitle={STAB/DGLR Symposium 2024},
  pages={393--402},
  year={2026},
  organization={Springer},
  doi={10.1007/978-3-032-11115-9_36}
}

Acknowledgements

Furthermore, a lot of repository logistics and their structure (GitHub CI, testing, docs, etc.) are taken straight from Trixi.jl. Thus, we greatly acknowledge all efforts from the Trixi.jl Authors.

We also thank Arpit Babbar who provided the first implementation of point-wise analysis quantities.