CAREER: Accelerating Real-time Hybrid Physical-Numerical Simulations in Natural Hazards Engineering with a Graphics Processing Unit (GPU)-driven Paradigm

职业：利用图形处理单元 (GPU) 驱动的范例加速自然灾害工程中的实时混合物理数值模拟

• 批准号: 2310171
• 负责人: Barbara Simpson
• 金额: $ 59.85万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310171&HistoricalAwards=false
• 关键词: CAREER; Accelerating; Real; time; Hybrid

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).This Faculty Early Career Development (CAREER) award will advance fundamental understanding of the risks posed by natural hazards to the built environment by laying the algorithmic foundation for high-fidelity simulations using graphics processing units (GPUs). High-resolution simulation of complex structural systems requires that detailed models be solved in unique ways. For example, tall buildings are so functionally important that they are “too big to fail,” as moderate damage can be difficult to repair and tall building loss of function severely affects post-event recovery. Yet, the influence of soil-structure interaction (SSI), critically important to tall building response during an earthquake, is often neglected due to computational cost and physical testing constraints. To enable more realistic and faster simulations, this research will derive, demonstrate, and facilitate advanced numerical methods that harness the massive parallelism of GPUs, i.e., real-time computer chips originally developed for graphics rendering, to overcome computational bottlenecks in structural simulations, specifically in the real-time hybrid simulation (RTHS) of tall buildings. In parallel, the multi-disciplinary components of this research will be integrated with a larger educational commitment to develop, disseminate, and continuously reflect on an inclusive teaching pedagogy to enhance student persistence and joy in computation, training students with the skills needed for an increasingly technology-driven workforce. This award will contribute to the National Science Foundation (NSF) role in the National Earthquake Hazards Reduction Program (NEHRP). High-resolution simulations of complex structures using RTHS, which couples physical experiments with numerical models in real time, has previously been exceptionally difficult. Refined, high-fidelity models result in greater resolution and accuracy but also suffer increased run time, inhibiting the feasibility of RTHS. Graphics processors will, for the first time, be used to accelerate RTHS to enable higher-fidelity "on-the-fly" simulation of civil structures. The seismic response of civil structures poses unique challenges for full GPU acceleration, including heterogeneous element formulations, varying degrees of nonlinearities, and reliance on implicit integration schemes with direct solvers. To address these challenges, this research will re-formulate approaches to assembling and solving the equations of motion on GPUs with: (i) massively parallel algorithms, (ii) semi-discrete time integration schemes, and (iii) an event-driven GPU-adapted RTHS architecture. This research will culminate in a tiered testing program to simulate realistic tall building response, including SSI. This project will establish multi-disciplinary research and mentorship at the intersection of structural engineering and scientific computing. Mutual collaborations will be used to synthesize expertise across three NSF-supported Natural Hazards Engineering Research Infrastructure (NHERI) facilities, including the Computational Modeling and Simulation Center at the University of California, Berkeley, the experimental facility at Lehigh University, and the DesignSafe cyberinfrastructure at the University of Texas at Austin. Project data will be archived and made publicly available in the NHERI Data Depot (https://www.DesignSafe-ci.org).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.

该奖项的全部或部分资金根据《2021 年美国救援计划法案》（公法 117-2）提供。该教师早期职业发展 (CAREER) 奖项将促进对自然灾害对建筑环境造成的风险的基本了解，为使用图形处理单元 (GPU) 进行高保真模拟奠定算法基础复杂结构系统的高分辨率模拟需要以独特的方式求解详细模型，例如，高层建筑的功能非常重要。 “太大而不能倒塌”，因为中度损坏可能难以修复，高层建筑功能丧失严重影响事后恢复，然而，土壤-结构相互作用（SSI）的影响对于地震期间高层建筑的响应至关重要。由于计算成本和物理测试限制，经常被忽视，为了实现更真实和更快的模拟，本研究将导出、演示和促进利用 GPU（即最初开发的实时计算机芯片）的大规模并行性的先进数值方法。用于图形渲染，克服计算瓶颈与此同时，这项研究的多学科组成部分将与更大的教育承诺相结合，以开发、传播和不断反思包容性教学。该奖项将有助于美国国家科学基金会 (NSF) 在国家地震减灾计划 (NEHRP) 中发挥作用。解决使用 RTHS 进行复杂结构的模拟，将物理实验与数值模型实时结合起来，以前是非常困难的。精细的高保真模型可以提高分辨率和精度，但也会增加运行时间，从而限制了 RTHS 的可行性。处理器将首次用于加速 RTHS，以实现土木结构的更高保真度“即时”模拟。土木结构的地震响应对完整的 GPU 加速提出了独特的挑战，包括不同的异构元素公式。度数为了解决这些挑战，本研究将重新制定在 GPU 上组装和求解运动方程的方法，其中包括：(i) 大规模并行算法，(ii) 半离散时间。集成方案，以及 (iii) 事件驱动的 GPU 适应 RTHS 架构 该研究将最终形成一个分层测试计划，以模拟真实的高层建筑响应，包括 SSI。该项目将建立多学科研究和指导。在结构工程和科学计算的交叉点上，相互合作将用于综合三个 NSF 支持的自然灾害工程研究基础设施 (NHERI) 设施的专业知识，其中包括加州大学伯克利分校的计算建模和模拟中心、实验性设施。里哈伊大学的设施和德克萨斯大学奥斯汀分校的 DesignSafe 网络基础设施项目数据将在 NHERI 数据仓库 (https://www.DesignSafe-ci.org) 中存档并公开提供。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，NSF 的法定使命被认为值得支持。

项目成果

Challenges in GPU-Accelerated Nonlinear Dynamic Analysis for Structural Systems

GPU 加速结构系统非线性动态分析面临的挑战

• DOI: 10.1061/jsendh.steng-11311
• 发表时间: 2023-03-01
• 期刊: Journal of Structural Engineering
• 影响因子: 4.1
• 作者: B. Simpson;Minjie Zhu;Akiri Seki;M. Scott
• 通讯作者: M. Scott