Ryujin is a high-performance second-order collocation-type finite-element scheme for solving the compressible Navier-Stokes and Euler equations of gas dynamics on unstructured meshes. The solver is based on the convex limiting technique introduced by Guermond et al. and uses the finite element library deal.II (website).
As such it is invariant-domain preserving, the solver maintains important physical invariants and is guaranteed to be stable without the use of ad-hoc tuning parameters.
Ryujin is freely available under the terms of the MIT license.
If you use this software for an academic publication please consider citing some of the following references ([1], [2], [3]):
@article {ryujin-2021-1,
author = {Matthias Maier and Martin Kronbichler},
title = {Efficient parallel 3D computation of the compressible Euler
equations with an invariant-domain preserving second-order
finite-element scheme},
doi = {10.1145/3470637},
url = {https://arxiv.org/abs/2007.00094},
journal = {ACM Transactions on Parallel Computing},
year = {2021},
volume = {8},
number = {3},
pages = {16:1-30},
}
@article{ryujin-2021-2,
author = {Jean-Luc Guermond and Matthias Maier and Bojan Popov and
Ignacio Tomas},
title = {Second-order invariant domain preserving approximation of the
compressible Navier--Stokes equations},
doi = {10.1016/j.cma.2020.113608},
url = {https://arxiv.org/abs/2009.06022},
journal = {Computer Methods in Applied Mechanics and Engineering},
year = {2021},
volume = {375},
number = {1},
pages = {113608},
}
@article{ryujin-2021-3,
author = {Jean-Luc Guermond and Martin Kronbichler and Matthias Maier and
Bojan Popov and Ignacio Tomas},
title = {On the implementation of a robust and efficient finite
element-based parallel solver for the compressible Navier-stokes
equations},
doi = {10.1016/j.cma.2021.114250},
url = {https://arxiv.org/abs/2106.02159},
journal = {Computer Methods in Applied Mechanics and Engineering},
year = {2022},
volume = {389},
pages = {114250},
}
For questions please contact Matthias Maier maier@math.tamu.edu and Martin Kronbichler martin.kronbichler@uni-a.de.
- Martin Kronbichler (@kronbichler), University of Augsburg, Germany
- Matthias Maier (@tamiko), Texas A&M University, TX, USA
- Ignacio Tomas (@itomasSNL), Texas Tech University, TX, USA
- Eric Tovar (@ejtovar), Los Alamos National Laboratory, USA