Enhanced Preconditioner for JOREK MHD Solver

TL;DR

This paper introduces a set of advanced developments to the JOREK MHD solver, including a generalized physics-based preconditioner, which significantly improves convergence, reduces computational costs, and enables larger time steps in large-scale tokamak plasma simulations.

Contribution

The paper presents a novel generalization of the physics-based preconditioner to mode groups, enhancing solver efficiency and scalability for complex non-linear MHD simulations.

Findings

Enhanced convergence in non-linear scenarios

Reduced memory consumption and computational costs

Ability to use larger time steps in simulations

Abstract

The JOREK extended magneto-hydrodynamic (MHD) code is a widely used simulation code for studying the non-linear dynamics of large-scale instabilities in divertor tokamak plasmas. Due to the large scale-separation intrinsic to these phenomena both in space and time, the computational costs for simulations in realistic geometry and with realistic parameters can be very high, motivating the investment of considerable effort for optimization. In this article, a set of developments regarding the JOREK solver and preconditioner is described, which lead to overall significant benefits for large production simulations. This comprises in particular enhanced convergence in highly non-linear scenarios and a general reduction of memory consumption and computational costs. The developments include faster construction of preconditioner matrices, a domain decomposition of preconditioning matrices for solver libraries that can handle distributed matrices, interfaces for additional solver libraries, an option to use matrix compression methods, and the implementation of a complex solver interface for the preconditioner. The most significant development presented consists in a generalization of the physics based preconditioner to "mode groups", which allows to account for the dominant interactions between toroidal Fourier modes in highly non-linear simulations. At the cost of a moderate increase of memory consumption, the technique can strongly enhance convergence in suitable cases allowing to use significantly larger time steps. For all developments, benchmarks based on typical simulation cases demonstrate the resulting improvements.