Collaborative Research: GEM--Towards Developing Physics-informed Subgrid Models for Geospace MagnetoHydroDynamics (MHD) Simulations

合作研究：GEM——开发用于地球空间磁流体动力学 (MHD) 模拟的物理信息子网格模型

• 批准号: 2247677
• 负责人: Dmitri Kondrashov
• 金额: $ 34.81万
• 依托单位: University of California-Los Angeles
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-08-15 至 2026-07-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2247677&HistoricalAwards=false
• 关键词: Collaborative; Research; GEM; Towards; Developing

While simulating the interaction between the solar wind and magnetosphere system, scientists usually use numerical magnetohydrodynamics (MHD), a model of electrically conducting fluids that treats all interpenetrating particle species together as a single continuous medium. Increasingly, MHD models require very-high numerical resolution for realistic global magnetosphere simulations of multiscale plasma flows. To address this problem, this project will develop new parameterizations for an existing global magnetosphere MHD model with the data-driven discovery by physics-informed machine learning and stochastic modeling. The project will support an earlier career scientist in a senior personnel role. The main broader impact will be the improvement of MHD components of global magnetosphere models, leading to better modeling and prediction of space weather. The developed techniques are very general and can be adapted to other complex high-dimensional dynamical systems with benefits to other areas of science and engineering. To broaden the results and prove their robustness, a hierarchy of physical problems will be employed for dynamical simulations of several types of multiscale turbulent MHD flows by GAMERA, to ascend systematically by increasing the reference data complexity: (1) 2D simulation of Orszag-Tang vortex, (2) 2D simulation of the Kelvin-Helmholtz instability, (3) 3D simulation of bursty bulk flows in the near-Earth magnetotail. The following key spatiotemporal reference data will be diagnosed from benchmark high-resolution GAMERA model solutions: (i) distributions of subgrid (small-scale) and large-scale fields, (ii) subgrid-scale forcing that encapsulates induced feedbacks on the large-scale fields. Physics-informed machine learning and stochastic modeling will be used to develop prognostic models of subgrid-scales and induced forcing, coupled to large-scale flow simulated by the coarse-scale GAMERA. Skills of the developed subgrid-scale parameterizations will be formally and systematically evaluated by the comprehensive set of physics-informed metrics relevant to practical applications.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.

在模拟太阳风与磁层系统之间的相互作用的同时，科学家通常使用数值磁流失动力学（MHD），这是一种电动传导流体模型，将所有互穿颗粒物种一起作为单个连续培养基将其视为单个连续培养基。 MHD模型越来越多地需要非常高的数值分辨率来实现多尺度等离子体流的现实全局磁层模拟。 为了解决此问题，该项目将通过物理信息通过物理学的机器学习和随机建模来开发现有的全局磁层MHD模型的新参数化。 该项目将支持早期的职业科学家担任高级人事角色。 更广泛的影响是改善全球磁层模型的MHD组件，从而更好地建模和预测太空天气。 开发的技术非常通用，可以适应其他复杂的高维动力系统，对其他科学和工程领域有好处。 To broaden the results and prove their robustness, a hierarchy of physical problems will be employed for dynamical simulations of several types of multiscale turbulent MHD flows by GAMERA, to ascend systematically by increasing the reference data complexity: (1) 2D simulation of Orszag-Tang vortex, (2) 2D simulation of the Kelvin-Helmholtz instability, (3) 3D simulation of bursty bulk flows in近地磁尾。 将从基准高分辨率GAMERA模型解决方案中诊断出以下关键时空参考数据：（i）亚网格（小规模）和大规模领域的分布，（ii）亚网格尺度，强迫将诱导的反馈封装在大规模领域上。 物理知识的机器学习和随机建模将用于开发亚网格尺度和诱导的强迫的预后模型，并与粗尺度gamera模拟的大规模流相结合。 开发的子网格尺度参数化的技能将通过与实际应用相关的全面的物理信息指标进行正式和系统地评估。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响审查标准来通过评估来通过评估来支持的。