Implementation of thermal conduction energy transfer models in the Bifrost Solar atmosphere MHD code

TL;DR

This paper evaluates various numerical methods for modeling thermal conduction in the solar atmosphere within the Bifrost MHD code, highlighting their accuracy, efficiency, and limitations to guide future simulations.

Contribution

It provides a comprehensive comparison of explicit, implicit, and hyperbolic methods for thermal conduction modeling in solar atmosphere simulations, including new analytical benchmarks.

Findings

Hyperbolic approximation is most accurate and fastest for long simulations.

No single method is optimal for all accuracy and efficiency needs.

Hyper-diffusivity improves method performance and convergence.

Abstract

Context: Thermal conductivity provides important contributions to the energy evolution of the upper solar atmosphere, behaving as a non-linear concentration-dependent diffusion equation. Recently, different methods have been offered as best-fit solutions to these problems in specific situations, but their effectiveness and limitations are rarely discussed. Aims. We rigorously test the different implementations of solving the conductivity flux, in the massively-parallel magnetohydrodynamics code, Bifrost, with the aim of specifying the best scenarios for the use of each method. Methods: We compare the differences and limitations of explicit versus implicit methods, and analyse the convergence of a hyperbolic approximation. Among the tests, we use a newly derived 1st-order self-similar approximation to compare the efficacy of each method analytically in a 1D pure-thermal test scenario. Results: We find that although the hyperbolic approximation proves the most accurate and the fastest to compute in long-running simulations, there is no optimal method to calculate the mid-term conductivity with both accuracy and efficiency. We also find that the solution of this approximation is sensitive to the initial conditions, and can lead to faster convergence if used correctly. Hyper-diffusivity is particularly useful in aiding the methods to perform optimally. Conclusions: We discuss recommendations for the use of each method within more complex simulations, whilst acknowledging the areas of opportunity for new methods to be developed.