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: 2247678
• 负责人: Anthony Sciola
• 金额: $ 24.96万
• 依托单位: Johns Hopkins University
• 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=2247678&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，科学家们通常使用数值磁流体动力学(MHD)，这是一种导电流体模型，将所有相互穿透的粒子物种一起视为单一的连续介质。对于多尺度等离子体流的真实全球磁层模拟，MHD模型越来越需要非常高的数值分辨率。为了解决这一问题，该项目将为现有的全球磁层MHD模型开发新的参数化，通过物理信息机器学习和随机建模实现数据驱动的发现。该项目将支持一位较早的职业科学家担任高级人事职务。主要的更广泛的影响将是改进全球磁层模型的MHD部分，从而更好地模拟和预测空间天气。为了扩展结果并证明其稳健性，我们将用Gamera对几种类型的多尺度湍流MHD流动进行动力学模拟：(1)Orszag-Tang涡的二维模拟，(2)Kelvin-Helmholtz不稳定性的二维模拟，(3)近地磁尾突发性块体流动的三维模拟。将从基准高分辨率Gamera模式解决方案中诊断以下关键时空参考数据：(I)亚网格(小尺度)和大尺度场的分布，(Ii)亚网格尺度强迫，它封装了大尺度场上的诱导反馈。物理信息机器学习和随机建模将被用来发展次网格尺度和诱导强迫的预报模式，并与由粗尺度Gamera模拟的大尺度流动相耦合。所开发的次网格尺度的参数化技术将通过与实际应用相关的一套全面的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优势和更广泛的影响审查标准进行评估，被认为值得支持。