Speaker
Description
Turbulence is a key driver of dynamics in both isolated and binary neutron star (BNS) systems, and can be triggered by magnetic field instabilities. In particular the onset of the Kelvin-Helmholtz and Magnetorotational Instabilities plays a key role in the evolution of the magnetic field in a post-merger remnant from a BNS system. Modelling the impact of turbulence directly in numerical simulations of a BNS is impossible due to the small length scales involved, but the impact of turbulence on large scale physics can be incorporated through a subgrid model for turbulence. In this talk I will present a new such subgrid model, for Newtonian and Special Relativistic Magnetohydrodynamics, developed using machine learning techniques, and demonstrate its ability to capture the impact of turbulence on large scale magnetised fluid evolutions. This demonstrates the capability to deploy such a model in general relativistic simulations of Neutron Star spacetimes, capturing the impact of turbulence on magnetic field evolution and multimessenger observables.