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.

在模拟太阳风和磁层系统之间的相互作用时，科学家通常使用数值磁流体动力学（MHD），这是一种导电流体模型，将所有互穿粒子种类一起视为单一连续介质。 MHD 模型越来越需要非常高的数值分辨率来实现多尺度等离子体流的真实全球磁层模拟。 为了解决这个问题，该项目将为现有的全球磁层 MHD 模型开发新的参数化，通过基于物理的机器学习和随机建模进行数据驱动的发现。 该项目将支持一名早期职业科学家担任高级人事职务。 主要更广泛的影响将是全球磁层模型 MHD 组件的改进，从而更好地建模和预测空间天气。 所开发的技术非常通用，可以适用于其他复杂的高维动力系统，从而有利于其他科学和工程领域。 为了拓宽结果并证明其鲁棒性，GAMERA将采用物理问题的层次结构对几种类型的多尺度湍流MHD流进行动力学模拟，通过增加参考数据复杂性来系统提升：（1）Orszag-Tang的2D模拟涡旋，(2) 开尔文-亥姆霍兹不稳定性的 2D 模拟，(3) 近地磁尾中突发整体流的 3D 模拟。 以下关键时空参考数据将从基准高分辨率 GAMERA 模型解决方案中诊断出来：（i）子网格（小规模）和大规模场的分布，（ii）子网格尺度强迫，封装了大尺度上的感应反馈。尺度字段。 基于物理的机器学习和随机建模将用于开发次网格尺度和诱导强迫的预测模型，并与粗尺度 GAMERA 模拟的大规模流相结合。 所开发的亚网格尺度参数化的技能将通过与实际应用相关的一整套物理信息指标进行正式和系统的评估。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的评估进行评估，被认为值得支持。影响审查标准。