Collaborative Research: Extreme-scale Ready High-order Methods for Astrophysical and Laboratory Turbulence

合作研究：天体物理和实验室湍流的极端规模就绪高阶方法

• 批准号: 2204668
• 负责人: Petros Tzeferacos
• 金额: $ 6.14万
• 依托单位: University of Rochester
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-11-15 至 2024-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2204668&HistoricalAwards=false
• 关键词: Collaborative; Research; Extreme; scale; Ready

Astrophysical phenomena inherently involve multi-physics and multi-scales that are nonlinearly coupled. The mathematical accuracy and efficiency of the computational tools used to test theoretical models hugely impact the scientific outcomes. The astrophysics community has thus long focused on developing computationally accurate numerical algorithms that give improved solution accuracy, stability, and efficiency. This project tackles the challenge of developing new approaches to address these problems, and incorporating these methods in the FLASH community code in order to reach a large audience in the astrophysics community. The work lies at the nexus of applied and computational mathematics, statistical Gaussian Process (GP) data predictions, and astrophysics. Involvement in NSF-supported outreach will help to educate under-represented community college students in computational astrophysics. Training young scientists in astrophysical and laboratory turbulence using validated simulations also helps to meet a critical national need.This study will develop high-order accurate, novel algorithms with only modest complexity, using a new high-order GP spatial reconstruction approach and a novel single-step high-order temporal integration method. This powerful predictive tool will run on massively parallel high-performance architectures. The study involves, firstly, using the novel GP approach to overcome the second-order bottleneck in most finite-volume shock-capturing astrophysics codes. The GP algorithms provide a simple mathematical framework for computing highly accurate volume-averaged versus pointwise quantities, and multidimensional spatial reconstructions. The second important development is an efficient high-order temporal integration method in adaptive mesh refinement grid configurations. This requires a single-step time update at a single quadrature point per cell face, which will provide the most efficient algorithmic framework in extreme-scale parallel simulations. Finally, comparing simulation results based on solution accuracy, errors, and computational performance for key astrophysical flow problems, including astrophysical and laboratory turbulence, will demonstrate the impact and benefit of these advances.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.

天体物理现象固有地涉及多物理和多尺度的非线性耦合。 用于测试理论模型的计算工具的数学准确性和效率极大地影响了科学结果。 因此，天体物理学社区长期以来一直致力于开发计算精确的数值算法，从而提高了解决方案的准确性，稳定性和效率。 该项目应对解决这些问题的新方法的挑战，并将这些方法纳入Flash社区代码中，以吸引天体物理学社区中的大量受众。 这项工作在于应用和计算数学，统计高斯过程（GP）数据预测和天体物理学的联系。 参与NSF支持的外展将有助于教育代表性不足的社区大学生从事计算天体物理学。 使用经过验证的模拟在天体物理和实验室湍流中培训年轻科学家也有助于满足至关重要的国家需求。这项研究将使用新的高阶GP空间重建方法和一种新型的单步高级临时整合方法来开发具有适度复杂性的高阶准确，新颖的算法。 这个强大的预测工具将在大规模平行的高性能体系结构上运行。 该研究首先使用新型的GP方法来克服大多数有限体积冲击的天体物理学代码中的二阶瓶颈。 GP算法为计算高度精确的体积平均数量与点数和多维空间重建提供了一个简单的数学框架。 第二个重要的发展是自适应网格改进网格配置中有效的高阶时间整合方法。 这需要在每个细胞面的单个正交点上进行单步时间更新，这将在极端尺度并联模拟中提供最有效的算法框架。 最后，比较基于解决方案的准确性，错误和计算性能的仿真结果，包括天体物理和实验室湍流在内，将证明这些进步的影响和好处。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识功能和广泛的影响来评估CREITERIA CREITERIA的评估，以评估来进行评估。