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Collaborative Research: Elements: A Physics-Rich, Singularity-Free, Spherical AMR Framework for Space Physics and Astrophysics

NSF

open

About This Grant

This project addresses the needs of scientific communities studying the Earth, planets, stars, and their cosmic environments for a robust and extensible modeling framework specifically adapted to the surface of a sphere. The aim is to develop a very general scientific software framework called GeoMesh-HAMMER that is suitable for running on supercomputers. GeoMesh-HAMMER will be used to study the dynamics of different space plasma environments, including the magnetospheres around planets, stellar winds, and planetary nebulae. The novelty of this approach is in leveraging recent advances in modeling technologies for rarefied plasmas that are not in a state of equilibrium, but whose thermal properties are affected by magnetic fields and the presence of different gas species. In addition, the framework aims to implement state of the art techniques to concentrate the computational resources on the regions of greatest interest, such as a shock wave in front of a magnetosphere, or a burst of plasma from the sun known as a coronal mass ejection, with a much sparser allocation of computing resources for the surrounding space. This approach dramatically improves the efficiency of computer models, resulting in increased productivity and reduced power usage. GeoMesh-HAMMER will be built using an open source development strategy in collaboration with space science and astrophysics communities to maximize the impact on as many fields of study as possible. The adaptive multi-level framework is not limited to solar or space physics, but could also find applications in modeling weather, ocean currents, geodynamics, and digital mapping, all of which play immensely important roles in modern society. Computational astrophysics and computational space physics are fields of study that have seen an increasing amount of shared interest in recent years. Both fields rely on simulating novel flow physics that was not accessible until recently. Both fields have a great need to accurately simulate problems on meshes that are optimized for spherical geometry, are adaptive, and are free of coordinate singularities. Finally, both fields are seeing the need for going beyond the magnetohydrodynamic approximation. The commonality in the needs for the space physics and astrophysics communities argues for a common framework between the two communities. This project is based on the realization that computational physics informs the development of innovative cyberinfrastructure (CI), which in turn supports the best physics-driven needs of the two communities. This project addresses community building around the proposed CI with the goal of realizing a decades long goal of both communities to model rarefied multi-component magnetized plasmas not in the state of thermodynamic equilibrium. To achieve the best advantage of these advances, the solution methods for these equations have to be embedded in a CI that supports spherical meshes with adaptive mesh refinement (AMR) and are free from singularities. The proposed framework will incorporate state of the art technologies such as fluctuation-form update to handle non-conservative terms and physical constraint preservation so that high Mach number flows and strong magnetic fields can be simulated. The CI, called GeoMesh-HAMMER, will be a powerful new tool for both communities to carry out simulations of planetary and stellar environments on spherical geodesic-based meshes. This award by the Office of Advanced Cyberinfrastructure is jointly supported by the Division of Physics in the Directorate for Mathematical and Physical Sciences. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

Focus Areas

physics

Eligibility

universitynonprofitsmall business

How to Apply

Funding Range

Up to $269K

Deadline

2028-08-31

Complexity
Medium
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