Implementing a new recovery scheme for primitive variables in the general relativistic magnetohydrodynamic code Spritz
Jay V. Kalinani, Riccardo Ciolfi, Wolfgang Kastaun, Bruno Giacomazzo,, Federico Cipolletta, Lorenzo Ennoggi

TL;DR
This paper details the implementation and testing of the RePrimAnd primitive variable recovery scheme in the GRMHD code Spritz, demonstrating improved robustness and accuracy in challenging astrophysical simulation scenarios.
Contribution
The paper introduces the integration of the RePrimAnd scheme into Spritz, enhancing the code's ability to recover primitive variables in extreme conditions.
Findings
RePrimAnd successfully recovers primitive variables in magnetized, low-density environments.
The scheme converges reliably in critical cases like neutron star collapse and BH-accretion disk evolution.
RePrimAnd outperforms previous recovery methods in challenging simulation scenarios.
Abstract
General relativistic magnetohydrodynamic (GRMHD) simulations represent a fundamental tool to probe various underlying mechanisms at play during binary neutron star (BNS) and neutron star (NS) - black hole (BH) mergers. Contemporary flux-conservative GRMHD codes numerically evolve a set of conservative equations based on `conserved' variables which then need to be converted back into the fundamental (`primitive') variables. The corresponding conservative-to-primitive variable recovery procedure, based on root-finding algorithms, constitutes one of the core elements of such GRMHD codes. Recently, a new robust, accurate and efficient recovery scheme called RePrimAnd was introduced, which has demonstrated the ability to always converge to a unique solution. The scheme provides fine-grained error policies to handle invalid states caused by evolution errors, and also provides analytical…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
