Higher-level large-eddy filtering strategy for general relativistic fluid simulations

TL;DR

This paper introduces a new large-eddy filtering strategy for simulating turbulent relativistic fluids in neutron star mergers, aiming to improve the accuracy and interpretation of complex numerical relativity simulations.

Contribution

It develops a Lagrangian filtering framework that addresses turbulence modeling challenges in relativistic fluid simulations, linking theoretical issues to practical implementation.

Findings

Framework enables better turbulence representation in relativistic simulations

Quantifies uncertainties in neutron star merger models

Implications for neutron star parameter estimation

Abstract

Nonlinear simulations of neutron star mergers are complicated by the need to represent turbulent dynamics. As we cannot (yet) perform simulations that resolve accurately both the gravitational-wave scale and the smallest scales at which magneto/hydrodynamic turbulence plays a role, we need to rely on approximations. Addressing this problem in the context of large-eddy models, we outline a coherent Lagrangian filtering framework that allows us to explore the many issues that arise, linking conceptual problems to practical implementations and the interpretation of the results. We develop understanding crucial for quantifying unavoidable uncertainties in current and future numerical relativity simulations and consider the implications for neutron-star parameter estimation and constraints on the equation of state of matter under extreme conditions.