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تسجيل مستخدم جديدGRMHD large eddy simulations with gradient subgrid-scale model
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The detection of binary neutron star mergers represents one of the most important astrophysical discoveries of the recent years. Due to the extreme matter and gravity conditions and the rich dynamics developed, it becomes a tremendous challenge to accurately simulate numerically all the scales present during the collision. Here we present how to study such systems by using large eddy simulations with a self-consistent subgrid-scale gradient model, that we generalized to the special relativistic case in a previous work and now extend to the general relativistic case. Adapted from nonrelativistic scenarios, the so-called gradient model allows to capture part of the effects of the hidden dynamics on the resolved scales, by means of a physically-agnostic, mathematically-based Taylor expansion of the nonlinear terms in the conservative evolution equations fluxes. We assess the validity of this approach in bounding-box simulations of the magnetic Kelvin-Helmholtz instability. Several resolutions and a broad range of scenarios are considered in order to carefully test the performance of the model under three crucial aspects: (i) highly curved backgrounds, (ii) jumps on the fluid density profiles and (iii) strong shocks. The results suggest our extension of the gradient subgrid-scale model to general relativistic magnetohydrodynamics is a promising approach for studying binary neutron stars mergers, and potentially to other relevant astrophysical scenarios.
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MHD turbulence is likely to play an important role in several astrophysical scenarios where the magnetic Reynolds is very large. Numerically, these cases can be studied efficiently by means of Large Eddy Simulations, in which the computational resour
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The detection of binary neutron star mergers represents one of the most important and complex astrophysical discoveries of the recent years. One of the unclear aspects of the problem is the turbulent magnetic field amplification, initially triggered
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