Numerical generation of vector potentials from specified magnetic fields

TL;DR

This paper introduces two new inverse curl algorithms for converting magnetic fields into vector potentials on Cartesian grids, enabling seamless data transfer between different simulation codes in relativistic magnetohydrodynamics.

Contribution

It presents a cell-by-cell and a global linear algebra method for inverse curl computation, improving flexibility and robustness in relativistic magnetic field simulations.

Findings

Algorithms successfully generate smooth vector potentials in relativistic contexts

Cell-by-cell method scales linearly with grid size

Global linear algebra approach handles nonzero divergence better

Abstract

Many codes have been developed to study highly relativistic, magnetized flows around and inside compact objects. Depending on the adopted formalism, some of these codes evolve the vector potential $\mathbf{A}$, and others evolve the magnetic field $\mathbf{B}=\nabla\times\mathbf{A}$ directly. Given that these codes possess unique strengths, sometimes it is desirable to start a simulation using a code that evolves $\mathbf{B}$ and complete it using a code that evolves $\mathbf{A}$. Thus transferring the data from one code to another would require an inverse curl algorithm. This paper describes two new inverse curl techniques in the context of Cartesian numerical grids: a cell-by-cell method, which scales approximately linearly with the numerical grid, and a global linear algebra approach, which has worse scaling properties but is generally more robust (e.g., in the context of a magnetic field possessing some nonzero divergence). We demonstrate these algorithms successfully generate smooth vector potential configurations in challenging special and general relativistic contexts.