MPI-AMRVAC 2.0 for Solar and Astrophysical Applications

TL;DR

MPI-AMRVAC 2.0 is an advanced open-source simulation framework supporting adaptive mesh refinement, magnetic field splitting, and anisotropic thermal conduction, enabling high-accuracy, scalable astrophysical and solar physics simulations.

Contribution

The paper introduces MPI-AMRVAC 2.0 with new features like radial grid stretching, magnetic field splitting, and improved thermal conduction modeling, enhancing simulation accuracy and scalability.

Findings

Demonstrated effectiveness of radial grid stretching with AMR in astrophysical flows.

Showcased improved accuracy and robustness in low plasma beta magnetic environments.

Achieved satisfactory strong scaling up to 2000 cores in 3D solar coronal rain simulations.

Abstract

We report on the development of MPI-AMRVAC version 2.0, which is an open-source framework for parallel, grid-adaptive simulations of hydrodynamic and magnetohydrodynamic (MHD) astrophysical applications. The framework now supports radial grid stretching in combination with adaptive mesh refinement (AMR). The advantages of this combined approach are demonstrated with one-dimensional, two-dimensional and three-dimensional examples of spherically symmetric Bondi accretion, steady planar Bondi-Hoyle-Lyttleton flows, and wind accretion in Supergiant X-ray binaries. Another improvement is support for the generic splitting of any background magnetic field. We present several tests relevant for solar physics applications to demonstrate the advantages of field splitting on accuracy and robustness in extremely low plasma $\beta$ environments: a static magnetic flux rope, a magnetic null-point, and magnetic reconnection in a current sheet with either uniform or anomalous resistivity. Our implementation for treating anisotropic thermal conduction in multi-dimensional MHD applications is also described, which generalizes the original slope limited symmetric scheme from 2D to 3D. We perform ring diffusion tests that demonstrate its accuracy and robustness, and show that it prevents the unphysical thermal flux present in traditional schemes. The improved parallel scaling of the code is demonstrated with 3D AMR simulations of solar coronal rain, which show satisfactory strong scaling up to 2000 cores. Other framework improvements are also reported: the modernization and reorganization into a library, the handling of automatic regression tests, the use of inline/online Doxygen documentation, and a new future-proof data format for input/output