CAREER: Simulating Cosmological Galaxy Formation with Adaptive-Mesh Refinement

职业：利用自适应网格细化模拟宇宙星系形成

• 批准号: 0547823
• 负责人: Greg Bryan
• 金额: $ 56.49万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-04-01 至 2012-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0547823&HistoricalAwards=false
• 关键词: CAREER; Simulating; Cosmological; Galaxy; Formation

CAREER: Simulating cosmological galaxy formation with adaptive-mesh refinementABSTRACTAST 0547823 Greg BryanDr. Greg Bryan, at Columbia University, will apply the Adaptive Mesh Refinement (AMR) method to the problem of galaxy formation to investigate the actual physical processes that shape the distribution of baryons inside dark matter halos. Feedback and self-regulation from star formation will be included in the three-dimensional hydrodynamical simulations that will investigate the formation of a wide range of galaxy sizes and types. Additional physical processes will also be included, including magneto-hydrodynamics (MHD) and a simplified treatment of radiative transfer. Dr. Bryan's educational effort will include: (1) the involvement of astronomy and physics undergraduates in the generation of scientific visualizations, and the creation of short movies that incorporate these visualizations; (2) the construction of a stereoscopic visualization system (the "AstroWall") in the Astronomy Department at Columbia University, to facilitate visualization of multi-dimensional datasets; and (3) bringing local New York high-school students and teachers into the Astrophysics Lab to do short-term research projects involving the analysis of large-dimensional datasets

职业：通过自适应网格细化模拟宇宙星系的形成摘要 0547823 Greg Bryan 博士。哥伦比亚大学的格雷格·布莱恩 (Greg Bryan) 将自适应网格细化 (AMR) 方法应用于星系形成问题，以研究塑造暗物质晕内重子分布的实际物理过程。 恒星形成的反馈和自我调节将包含在三维流体动力学模拟中，该模拟将研究各种大小和类型的星系的形成。还将包括其他物理过程，包括磁流体动力学 (MHD) 和辐射传输的简化处理。 布莱恩博士的教育工作将包括：（1）让天文学和物理学本科生参与科学可视化的生成，并制作包含这些可视化的短片； （2）在哥伦比亚大学天文系建设立体可视化系统（“AstroWall”），以促进多维数据集的可视化； （3）邀请纽约当地高中生和教师到天体物理实验室进行涉及大维数据集分析的短期研究项目