| 项目编号: | 1622306
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| 项目名称: | SHINE: A Vlasov-Maxwell Study of Solar Wind Turbulence Heating and Distribution Function Dynamics |
| 作者: | Jason TenBarge
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| 承担单位: | University of Maryland College Park
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| 批准年: | 2016
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| 开始日期: | 2016-06-01
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| 结束日期: | 2017-11-30
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| 资助金额: | 222063
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| 资助来源: | US-NSF
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| 项目类别: | Continuing grant
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| 国家: | US
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| 语种: | 英语
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| 特色学科分类: | Geosciences - Atmospheric and Geospace Sciences
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| 英文关键词: | project
; turbulence
; distribution function dynamics
; electron heating
; solar probe plus
; non-linear turbulence
; valuable particle distribution function information
; solar surface
; solar corona
; plasma heating
; continuum vlasov-maxwell simulation
; alfvenic turbulence
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| 英文摘要: | The development of a detailed understanding of turbulence in magnetized plasmas has been a long standing goal of the broader scientific community, both as a fundamental physics process and because of its applicability to a wide variety of phenomena. Turbulence in a magnetized plasma is the primary mechanism responsible for transforming energy at large injection scales into small-scale motions, which are ultimately dissipated as heat in systems such as the solar corona and wind. In this regard, the main purpose of this 3-year SHINE project is to improve present understanding of plasma heating and distribution function dynamics in weakly collisional plasmas using both hybrid and fully kinetic, continuum Vlasov-Maxwell simulations.
This 3-year SHINE project aims to accomplish the following three science objectives. The first objective is to develop a better understanding of the distribution function dynamics related to wave damping in fully non-linear turbulence. The second objective is to examine the behavior of Alfvenic turbulence near the proton scale. The third objective is to determine the relative importance of proton and electron heating in large-scale intermittent structures produced as a natural consequence of turbulence. The project is directly relevant to the NSF's SHINE program, because it will provide important knowledge about the nature of turbulence in space and astrophysical plasmas. Such knowledge is critical for accurate modeling and prediction of the space weather conditions from the solar surface to the Earth and beyond.
The research outcome of this project may have implications for weakly collisional fusion experiments, and it could provide additional insight into a myriad of heliospheric domains, and be of interest to the astrophysics community. The parameters chosen and the continuum nature of the simulations will provide valuable particle distribution function information for existing and upcoming spacecraft missions, such as Solar Probe Plus. The knowledge gained from this project will aid in the development of improved models of energy transport, which may aid in global modeling of many disparate, large-scale phenomena. The full simulation data from the final stage of the project will be made available to the community to analyze and compare directly to available spacecraft data. Additionally, the simulation code, Gkeyll, and associated analysis scripts are openly available for community use. The project will support a graduate student at the University of Maryland College Park. Additionally, underrepresented undergraduate students from mid-Atlantic minority-serving institutions will be involved in the research through the Graduate Resources Advancing Diversity with Maryland Astronomy and Physics (GRAD-MAP) program during their annual winter workshops. The students in the workshops will develop input scripts and analyze simulation data to introduce them to high performance computing research, aiding in their preparation for graduate school. The research and EPO agenda of this project supports the Strategic Goals of the AGS Division in discovery, learning, diversity, and interdisciplinary research. |
| 资源类型: | 项目
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| 标识符: | http://119.78.100.158/handle/2HF3EXSE/92309
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| Appears in Collections: | 全球变化的国际研究计划
科学计划与规划
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| Recommended Citation: | |
Jason TenBarge. SHINE: A Vlasov-Maxwell Study of Solar Wind Turbulence Heating and Distribution Function Dynamics. 2016-01-01.
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