Collaborative Research: Integration of Extended Magnetohydrodynamic (MHD) and Kinetic Effects in Global Magnetosphere Models

合作研究：全球磁层模型中扩展磁流体动力学 (MHD) 和动力学效应的集成

• 批准号: 1338863
• 负责人: Amitava Bhattacharjee
• 金额: $ 62万
• 依托单位: University of California-San Diego
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-06-15 至 2015-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1338863&HistoricalAwards=false
• 关键词: Collaborative; Research; Integration; Extended; Magnetohydrodynamic

This grant is for partial support of a project selected and funded under the 2012 NASA-NSF partnership for Space Weather Modeling Collaborations. It is a collaborative effort, led by Princeton University and with participation also from University of New Hampshire, University of California-San Diego, NASA Goddard Space Flight Center, and Los Alamos National Laboratory. The objective is to undertake what amounts to a theoretical and computational grand challenge, namely to develop a state-of-the-art global magnetosphere model that goes beyond the standard framework of the resistive MHD model to include multi-fluid physics, including a generalized Ohms' law, enhanced equations of state for electrons and ions that incorporate kinetic effects, and multiple charged particle species. To this end, the project will assimilate and represent many of the remarkable advances made over the last almost 15 years in our understanding of reconnection, instabilities of current sheets, multiple fluids, and turbulence in collisionless plasmas within the framework of a global magnetosphere code, the OpenGGCM, a traditional version of which is presently used extensively in the community. A suite of mature physics-based modules, based on high-Lundquist-number MHD, Hall MHD, hybrid, multi species, and fully kinetic particle-in-cell codes exist amongst the team members and will be brought together to produce a comprehensive, next-generation global magnetosphere code that will include for the first time extended MHD and kinetic effects. When completed, the resulting enhanced global magnetosphere model will be delivered to the Community Coordinated Modeling Center (CCMC) for easy and full access to the scientific community and eventual transition to use for operational space weather forecasting. This will enable the accurate modeling of the kinetic scale processes (e.g., magnetic reconnection) responsible for extreme space weather events with direct impact on human society. While the application is geared to the Earth's magnetosphere, the core science module of the model has much broader applicability, and has potentially transformative implications for our ability to understand and predict a broad range of space weather phenomena in the heliosphere that involve magnetic reconnection, instabilities of current sheets, and turbulence. The team, which is a partnership between academia and national laboratories, consists of several junior scientists, postdoctoral fellows, and graduate students who will be educated in this broadly interdisciplinary subject, involving theoretical as well as experimental space plasma physics, applied mathematics, and high-performance computing.

该赠款是为了部分支持一个根据2012年NASA-NSF合作伙伴而用于太空天气建模合作的项目。这是由普林斯顿大学（Princeton University）领导的合作努力，还参加了新罕布什尔大学，加利福尼亚大学迭戈大学，NASA Goddard太空飞行中心和Los Alamos National Laboratory的参与。 目的是进行理论和计算盛大的挑战，即开发最先进的全球磁层模型，该模型超出了电阻性MHD模型的标准框架，其中包括包括多流体物理学的多流氟化物理学，包括广义欧姆的定律，增强的国家和离子的增强的电子和kinate Kinetic Entiment extiment和多重电信粒子的离子。为此，该项目将在过去的近15年中吸收并代表了我们对重新连接的理解，当前床单的不稳定性，多种流体和无碰撞等离程度的湍流的许多显着进步，在全球磁层代码的框架内，无碰撞的等离子体，OpenGGCM的openGGCM，该版本的传统版本是在社区中广泛使用的传统版本。一套基于高额物理学的模块，基于高额Quist-number MHD，Hall MHD，Hall，混合动力，多种物种和完全动力学的粒子粒子代码，并将在团队成员之间进行，并将聚集在一起，以制作全面的下一代全球磁层代码，其中包括首次扩展MHD和Kinetic MHD和Kinetic效应。完成后，由此产生的增强的全球磁层模型将交付给社区协调的建模中心（CCMC），以便于对科学界的易于访问，并最终过渡以用于操作空间天气预测。 这将使动力学量表过程（例如，磁重新连接）的准确建模负责直接影响人类社会的极端空间天气事件。尽管该应用适用于地球磁层，但该模型的核心科学模块具有更广泛的适用性，并且对我们理解和预测涉及磁性重新连接，电流板和湍流的磁性重新连接，不稳定性和湍流的地球层中广泛的太空天气现象的能力具有潜在的变革意义。该团队是学术界与国家实验室之间的合作伙伴关系，由几位初级科学家，博士后研究员和研究生组成，他们将在这个广泛的跨学科学科中受过教育，涉及理论上以及实验性太空等离子体物理学，应用数学和高性能计算。