Preprints
https://doi.org/10.5194/egusphere-2024-301
https://doi.org/10.5194/egusphere-2024-301
16 Feb 2024
 | 16 Feb 2024
Status: this preprint is open for discussion.

Physics-motivated Cell-octree Adaptive Mesh Refinement in the Vlasiator 5.3 Global Hybrid-Vlasov Code

Leo Kotipalo, Markus Battarbee, Yann Pfau-Kempf, and Minna Palmroth

Abstract. Automatically adaptive grid resolution is a common way of improving simulation accuracy while keeping the computational efficiency at a manageable level. In space physics adaptive grid strategies are especially useful as simulation volumes are extreme, while the most accurate physical description is based on electron dynamics and hence requires very small grid cells and time steps. Therefore, many past global simulations encompassing e.g. the near-Earth space have made tradeoffs in terms of the physical description and used laws of magnetohydrodynamics (MHD) that require less accurate grid resolutions. Recently, using supercomputers, it has become possible to model the near-Earth space domain with an ion-hybrid scheme going beyond the MHD-based fluid dynamics. These simulations, however, must develop a new adaptive mesh strategy beyond what is used in MHD simulations.

We developed an automatically adaptive grid refinement strategy for ion-hybrid Vlasov schemes, and implemented it within the Vlasiator global solar wind – magnetosphere – ionosphere simulation Vlasiator. This method automatically adapts the resolution of the Vlasiator grid using two indices: one formed as a maximum of dimensionless gradients measuring the rate of spatial change in selected variables, and the other derived from the ratio of the current density to the magnetic field density perpendicular to the current. Both these indices can be tuned independently to reach a desired level of refinement and computational load. We test the indices independently and compare the results to a control run using static refinement.

The results show that adaptive refinement highlights relevant regions of the simulation domain and keeps the computational effort at a manageable level. We find that the refinement shows some overhead in rate of cells solved per second. This overhead can be large compared to the control run without adaptive refinement, possibly due to resource utilisation, grid complexity and issues in load balancing. These issues lay a development roadmap for future optimisations.

Leo Kotipalo, Markus Battarbee, Yann Pfau-Kempf, and Minna Palmroth

Status: open (until 03 May 2024)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
Leo Kotipalo, Markus Battarbee, Yann Pfau-Kempf, and Minna Palmroth

Data sets

AMR Test Configuration Leo Kotipalo https://doi.org/10.23729/f7f9d95d-1e23-49e3-9c35-1d8e26c47bf7

Model code and software

fmihpc/vlasiator: Vlasiator 5.3 Yann Pfau-Kempf, Sebastian von Alfthan, Urs Ganse, Markus Battarbee, Leo Kotipalo, Tuomas Koskela, Ilja, Arto Sandroos, Kostis Papadakis, Markku Alho, Hongyang Zhou, Miro Palmu, Maxime Grandin, Jonas Suni, Dimitry Pokhotelov, and Konstatinos Horaites https://doi.org/10.5281/zenodo.10600112

Leo Kotipalo, Markus Battarbee, Yann Pfau-Kempf, and Minna Palmroth

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Short summary
This paper examines a method called adaptive mesh refinement in optimization of the space plasma simulation model Vlasiator. The method locally adjusts resolution in regions which are most relevant to model, based on the properties of the plasma. The runs testing this method show that adaptive refinement manages to highlight the desired regions with manageable performance overhead. Performance in larger scale production runs and mitigation of overhead are avenues of further research.