A Simflowny-based high-performance 3D code for the generalized induction equation

• Published in: Computer physics communications Vol. 237; pp. 168 - 183
• Main Authors: Viganò, Daniele, Martínez-Gómez, David, Pons, José A., Palenzuela, Carlos, Carrasco, Federico, Miñano, Borja, Arbona, Antoni, Bona, Carles, Massó, Joan
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01-04-2019
• Subjects: AMR; High resolution shock capturing; Magnetic fields; MHD; Neutron stars; Simflowny; MHD; Neutron stars; Magnetic fields; AMR; Simflowny; High resolution shock capturing
• ISSN: 0010-4655; 1879-2944
• DOI: 10.1016/j.cpc.2018.11.022

In the interior of neutron stars, the induction equation regulates the long-term evolution of the magnetic fields by means of resistivity, Hall dynamics and ambipolar diffusion. Despite the apparent simplicity and compactness of the equation, the dynamics it describes is not trivial and its understanding relies on accurate numerical simulations. While a few works in 2D have reached a mature stage and a consensus on the general dynamics at least for some simple initial data, only few attempts have been performed in 3D, due to the computational costs and the need for a proper numerical treatment of the intrinsic non-linearity of the equation. Here, we carefully analyze the general induction equation, studying its characteristic structure, and we present a new Cartesian 3D code, generated by the user-friendly, publicly available Simflowny platform. The code uses high-order numerical schemes for the time and spatial discretization, and relies on the highly-scalable SAMRAI architecture for the adaptive mesh refinement. We present the application of the code to several benchmark tests, showing the high order of convergence and accuracy achieved and the capabilities in terms of magnetic shock resolution and three-dimensionality. This paper paves the way for the applications to a realistic, 3D long-term evolution of neutron stars interior and, possibly, of other astrophysical sources.