GRAthena++: magnetohydrodynamical evolution with dynamical spacetime
Abstract
We present a selfcontained overview of GRAthena++, a generalrelativistic magnetohydrodynamics (GRMHD) code, that incorporates treatment of dynamical spacetime, based on the recent work of (Daszuta+, 2021)[49] and (Cook+, 2023)[45]. General aspects of the Athena++ framework we build upon, such as octtree based, adaptive mesh refinement (AMR) and constrained transport, together with our modifications, incorporating the Z4c formulation of numerical relativity, judiciously coupled, enables GRMHD with dynamical spacetimes. Initial verification testing of GRAthena++ is performed through benchmark problems that involve isolated and binary neutron star spacetimes. This leads to stable and convergent results. Gravitational collapse of a rapidly rotating star through black hole formation is shown to be correctly handled. In the case of nonrotating stars, magnetic field instabilities are demonstrated to be correctly captured with total relative violation of the divergencefree constraint remaining near machine precision. The use of AMR is showcased through investigation of the KelvinHelmholtz instability which is resolved at the collisional interface in a merger of magnetised binary neutron stars. The underlying taskbased computational model enables GRAthena++ to achieve strong scaling efficiencies above $80\%$ in excess of $10^5$ CPU cores and excellent weak scaling up to $\sim 5 \times 10^5$ CPU cores in a realistic production setup. GRAthena++ thus provides a viable path towards robust simulation of GRMHD flows in strong and dynamical gravity with exascale high performance computational infrastructure.
 Publication:

arXiv eprints
 Pub Date:
 June 2024
 DOI:
 10.48550/arXiv.2406.05126
 arXiv:
 arXiv:2406.05126
 Bibcode:
 2024arXiv240605126D
 Keywords:

 General Relativity and Quantum Cosmology;
 Astrophysics  High Energy Astrophysical Phenomena
 EPrint:
 Invited chapter for the edited book {\it New Frontiers in GRMHD Simulations} (Eds. C. Bambi, Y. Mizuno, S. Shashank and F. Yuan, Springer Singapore, expected in 2024)