A resistive MHD module in the GPU-accelerated GRMHD code GRaM-X

Engineering notes

Key topics: autonomous driving. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Abstract Relativistic macroscopic plasma dynamics can be described by general-relativistic magnetohydrodynamics. In many high-energy astrophysical settings, such as the interior dynamics of magnetized stars, the ideal GRMHD approximation, in which we assume infinite conductivity, provides an excellent description. However, ideal GRMHD neglects resistive effects that are essential for processes such as magnetic reconnection, dissipation, and magnetospheric dynamics. Incorporating resistivity into astrophysical plasma models accounts for the fact that plasmas in such environments are not perfect conductors. We present a resistive version of the GPU-accelerated GRMHD code GRaM-X, which evolves the full resistive GRMHD equations using the Z4c formalism for Einstein's equations. We implement a second-order implicit–explicit Runge–Kutta scheme to handle stiff source terms, obtain the primitive quantities from the conserved quantities using one- and four-dimensional recovery methods, and employ the HLLE Riemann solver in combination with TVD and WENO reconstruction schemes. We validate the module using a range of standard tests, including 1D shocktubes, current sheets, Alfvén waves, 2D cylindrical explosions, and 3D TOV stars. The results of these tests demonstrate accurate recovery of the ideal MHD limit, correct resistive behavior, and stable evolution in dynamical spacetimes. Leveraging the GPU-accelerated resistive version of GRaM-X enables efficient large-scale simulations, paving the way for realistic studies of binary mergers, accretion flows, and relativistic jets within the framework of multi-messenger astrophysics.

Links and sources

Comments