MRI: Acquisition of a Computer Cluster for the Study of Fluids, Heliophysics, and Astrophysical Plasmas

MRI：购买用于研究流体、太阳物理学和天体物理等离子体的计算机集群

• 批准号: 1919310
• 负责人: Francois Foucart
• 金额: $ 60.29万
• 依托单位: University of New Hampshire
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1919310&HistoricalAwards=false
• 关键词: MRI; Acquisition; Computer; Cluster; Study

A homogeneous high-performance computing cluster for the simulation of fluids, heliophysics, and astrophysical plasmas will be built at the University of New Hampshire (UNH). The fluid and plasma simulations performed at UNH share very similar hardware requirements, making it possible to design an instrument with a high expected usage rate and broad scientific applications. The cluster will play a critical role in the formation of UNH graduate students and postdoctoral fellows to modern computational methods and parallel computing, as well as for the recruitment and retention of a diverse workforce.Numerical simulations of fluids and plasmas play a critical role in the study of a wide range of problems in astrophysics, heliophysics, and geophysics. Modeling nearly-collisionless plasmas is necessary to understand the generation of the solar wind, its propagation, its interaction with the Earth's magnetosphere, and its effects on, for example, the Earth's climate or artificial satellites. The same physics governs the evolution of accretion disks around many of the supermassive black holes at the center of galaxies, including the soon-to-be imaged black hole at the center of our own Milky Way. Magnetohydrodynamics simulations, on the other hand, help us understand the evolution of the Earth's atmosphere and its oceans, as well as some of the highest-energy events observable in the Universe: the collisions of black holes and neutron stars that power gravitational wave signals. This grant will enable new understanding in all of these research areas through building a new computer cluster for numerical simulations of fluids and plasmas. The cluster will have 1760 compute nodes with Infiniband communication, and 283+ Tb of storage/archival space. This award was co-funded between the Division of Atmospheric and Geospace Sciences and the Division of Physics.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.

新汉普郡大学(UNH)将建立一个用于模拟流体、太阳物理和天体物理等离子体的同质高性能计算集群。在北卡罗来纳大学进行的流体和等离子体模拟具有非常相似的硬件要求，这使得设计一种具有高预期使用率和广泛科学应用的仪器成为可能。该星系团将在形成现代计算方法和并行计算的UNH研究生和博士后研究员以及招募和保留不同的劳动力方面发挥关键作用。流体和等离子体的数值模拟在天体物理、太阳物理和地球物理的广泛问题的研究中发挥着关键作用。要了解太阳风的产生、传播、它与地球磁层的相互作用，以及它对地球气候或人造卫星的影响，对几乎没有碰撞的等离子体进行建模是必要的。同样的物理原理支配着星系中心许多超大质量黑洞周围吸积盘的演化，包括即将被成像的我们银河系中心的黑洞。另一方面，磁流体模拟有助于我们了解地球大气和海洋的演化，以及宇宙中一些可观察到的最高能量事件：为引力波信号提供动力的黑洞和中子星的碰撞。这笔赠款将通过建立一个新的计算机集群来进行流体和等离子体的数值模拟，从而使人们能够对所有这些研究领域有新的了解。该群集将拥有1760个具有Infiniband通信的计算节点，以及283 TB以上的存储/归档空间。该奖项是由大气和地球空间科学部和物理部共同资助的。这一奖项反映了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。

项目成果

Global Simulations, chapter 37

全球模拟，第 37 章

• DOI: 10.1002/9781119815624
• 发表时间: 2021
• 期刊: Geography monograph series
• 作者: Joachim Raeder, Kai Germaschewski

Domain of Influence Analysis: Implications for Data Assimilation in Space Weather Forecasting

• DOI: 10.3389/fspas.2020.571286
• 发表时间: 2020-09
• 作者: D. Millas;M. Innocenti;B. Laperre;J. Raeder;S. Poedts;G. Lapenta

Unsupervised classification of simulated magnetospheric regions

模拟磁层区域的无监督分类

• DOI: 10.5194/angeo-39-861-2021
• 发表时间: 2021
• 期刊: Annales Geophysicae
• 影响因子: 1.9
• 作者: Innocenti, Maria Elena;Amaya, Jorge;Raeder, Joachim;Dupuis, Romain;Ferdousi, Banafsheh;Lapenta, Giovanni
• 通讯作者: Lapenta, Giovanni

Observational Analysis and Numerical Modeling of the Solar Wind Fluctuation Spectra during Intervals of Plasma Instability

等离子体不稳定期间太阳风脉动光谱的观测分析和数值模拟

• DOI: 10.3847/1538-4357/ac9f42
• 发表时间: 2022
• 期刊: The Astrophysical Journal
• 作者: Markovskii, S. A.;Vasquez, Bernard J.
• 通讯作者: Vasquez, Bernard J.

Inference and De-Noising of Non-Gaussian Particle Distribution Functions: A Generative Modeling Approach

• DOI: 10.1007/978-3-030-95467-3_25
• 发表时间: 2021-10
• 期刊: ArXiv
• 作者: J. Donaghy;K. Germaschewski