OAC Core: Small: Enabling High-fidelity Turbulent Reacting-Flow Simulations through Advanced Algorithms, Code Acceleration, and High-order Methods for Extreme-scale Computing

OAC 核心：小型：通过高级算法、代码加速和超大规模计算的高阶方法实现高保真湍流反应流模拟

• 批准号: 1909379
• 负责人: Matthias Ihme
• 金额: $ 50万
• 依托单位: Stanford University
• 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: 2019-10-01 至 2023-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1909379&HistoricalAwards=false
• 关键词: OAC; Core; Small; Enabling; fidelity

Accurate numerical simulations of turbulent flows are of practical importance for several applications, including gas turbines and internal-combustion engines for power generation and transportation, the risk mitigation associate with reactor safety, and for scientific discovery of novel energy-conversion strategies. However, commonly employed software employ simplifications and exhibit deficiencies in accurately representing the underlying physical processes. The so-called discontinuous Galerkin (DG) methods have been identified as a promising alternative. These methods are characterized by utilizing a formulation that significantly improves fidelity. Other advantages are the flexibility in representation complex physical processes and the excellent performance on high-performance computing systems. While the potential of these DG-methods has been recognized, major roadblocks to adoption include the lack of suitable cyberinfrastructure (CI) methods and tools for scientific discovery and engineering analysis as well as the need for innovative programming techniques to enable scalable simulations on modern machines. This project addresses these research challenges and develops novel numerical methods and advanced programming paradigms for high-performance simulations of turbulent reacting flows. Integrated into this research are several education and outreach activities that address the need for training the next generation of interdisciplinary scientists and engineers. High-school students participate in several research activities, and a mentoring program is established that brings together students from engineering and computer science to work together on interdisciplinary research problems. This project, thus, serves the national interest, as stated by NSF's mission: to promote the progress of science and to secure the national defense.The long runtime costs of simulating turbulent flows inhibit explorations and studies of realistic flames and the engineering analysis of complex combustion geometries. The approach to improving the quality and performance of turbulent flow simulations is to use high-order discontinuous Galerkin (DG) methods backed by high-performance algorithmic implementations suitable for execution on heterogeneous compute platforms. The work specifically uses task level parallelism coupled with load-balancing and adaptive techniques to achieve high throughput simulation capabilities on heterogeneous hardware. Research on advanced CI-ecosystems is conducted to develop task-based programming techniques for accelerating multi-physics flow simulations on heterogeneous computing systems. To this end, Legion is employed for the dynamic runtime mapping of compute-intense kernel functions to heterogeneous processors under consideration of computational load, data complexity, and heterogeneity of the computing system. Novel integration schemes and advanced adaptation techniques are developed to enable efficient simulations of turbulent reacting flows. These techniques are incorporated into a multi-physics DG-method that is made available to the research community as an open-source software platform for scientific discovery and engineering analysis. The close collaboration of graduate students with national laboratories and industrial partners facilitates an effective transition of the numerical methods and programming techniques that are developed in this project into other software environments.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.

湍流的精确数值模拟对于多种应用具有实际重要性，包括用于发电和运输的燃气涡轮机和内燃机，与反应堆安全相关的风险缓解，以及新的能量转换策略的科学发现。然而，通常采用的软件采用简化，并在准确地表示底层物理过程中表现出缺陷。所谓的间断伽辽金（DG）方法已被确定为一个有前途的替代方案。这些方法的特征在于利用显著提高保真度的制剂。其他优点是表示复杂物理过程的灵活性和在高性能计算系统上的优异性能。虽然这些DG方法的潜力已经得到认可，但采用的主要障碍包括缺乏合适的网络基础设施（CI）方法和工具用于科学发现和工程分析，以及需要创新的编程技术来实现现代机器上的可扩展模拟。该项目解决了这些研究挑战，并开发了新的数值方法和先进的编程范式，用于湍流反应流的高性能模拟。这项研究还包括几项教育和外联活动，以满足培训下一代跨学科科学家和工程师的需要。高中学生参加了几项研究活动，并建立了一个指导计划，将工程和计算机科学的学生聚集在一起，共同研究跨学科的研究问题。因此，该项目符合国家利益，正如NSF的使命所述：促进科学进步，保障国防安全。模拟湍流的长期运行成本抑制了对真实火焰的探索和研究以及复杂燃烧几何形状的工程分析。提高湍流模拟质量和性能的方法是使用高阶不连续Galerkin（DG）方法，该方法由适合在异构计算平台上执行的高性能算法实现支持。这项工作专门使用任务级并行加上负载平衡和自适应技术，以实现异构硬件上的高吞吐量仿真能力。先进的CI生态系统的研究进行开发基于任务的编程技术，加速多物理场流模拟异构计算系统。为此，军团采用的计算密集型内核功能的动态运行时映射到异构处理器的计算负载，数据的复杂性，和计算系统的异构性的考虑下。新的集成方案和先进的适应技术的开发，使有效的模拟湍流反应流。这些技术被纳入多物理DG方法，该方法作为科学发现和工程分析的开源软件平台提供给研究界。研究生与国家实验室和工业合作伙伴的密切合作促进了本项目中开发的数值方法和编程技术有效地过渡到其他软件环境中。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

项目成果

Quail: A lightweight open-source discontinuous Galerkin code in Python for teaching and prototyping

Quail：Python 中的轻量级开源不连续 Galerkin 代码，用于教学和原型设计

• DOI: 10.1016/j.softx.2022.100982
• 发表时间: 2022
• 期刊: SoftwareX
• 影响因子: 3.4
• 作者: Ching, Eric J.;Bornhoft, Brett;Lasemi, Ali;Ihme, Matthias
• 通讯作者: Ihme, Matthias

Development of a discontinuous Galerkin solver using Legion for heterogeneous high-performance computing architectures

使用 Legion 开发异构高性能计算架构的不连续 Galerkin 求解器

• DOI: 10.2514/6.2021-0140
• 发表时间: 2021
• 期刊: AIAA Scitech 2021 Forum
• 作者: Bando, Kihiro;Brill, Steven;Slaughter, Elliott;Sekachev, Michael;Aiken, Alex;Ihme, Matthias
• 通讯作者: Ihme, Matthias