使用高性能计算工作流的数字孪生并行降阶建模
Parallel Reduced Order Modeling for Digital Twins using High-Performance Computing Workflows
AI总结：
本文提出了一种支持HPC的端到端PROM工作流，结合PyCOMPSs并行框架、多种SVD算法及分区ECM超降阶方案，用于大规模机械仿真，并通过电机热力学案例验证了其在跨环境实时数字孪生应用中的有效性。
摘要：
将降阶模型（ROMs）与高性能计算（HPC）相结合对于开发数字孪生至关重要，特别是用于工业系统的实时监控和预测性维护。本文提出了一种全面的、支持HPC的工作流，用于大规模机械仿真的基于投影的降阶模型（PROMs）的开发和部署。我们使用PyCOMPSs的并行框架来高效执行ROM训练仿真，采用并行奇异值分解（SVD）算法，如随机SVD、Lanczos SVD和基于高瘦QR（TSQR）的全SVD。此外，我们引入了称为经验配置法（ECM）的超降阶方案的分区版本，以进一步提高机械系统PROMs的计算效率。尽管HPC被广泛用于PROMs，但显著缺乏详细说明在HPC环境中构建和部署端到端PROMs的综合工作流的出版物。我们的工作流通过一个案例研究得到了验证，该研究关注电机的热力学，这是一个涉及对流换热和机械组件的多物理场问题。PROM旨在提供一个实时预测工具，能够在不同操作条件下实现紧急停机后的快速安全电机重启，展示了其对工程力学仿真实践的潜在影响。为了便于部署，我们使用工作流即服务（WaaS）策略和功能模型单元（FMUs）以确保跨HPC、边缘和云环境的兼容性和易于集成。结果说明了结合PROMs和HPC的有效性，为跨多个行业的计算力学中可扩展的实时数字孪生应用树立了先例。
英文摘要：
The integration of reduced-order models (ROMs) with high-performance computing (HPC) is critical for developing digital twins, particularly for real-time monitoring and predictive maintenance of industrial systems. This paper presents a comprehensive, HPC-enabled workflow for developing and deploying projection-based reduced-order models (PROMs) for large-scale mechanical simulations. We use PyCOMPSs' parallel framework to efficiently execute ROM training simulations, employing parallel singular value decomposition (SVD) algorithms such as randomized SVD, Lanczos SVD, and full SVD based on tall-skinny QR (TSQR). Moreover, we introduce a partitioned version of the hyper-reduction scheme known as the Empirical Cubature Method (ECM) to further enhance computational efficiency in PROMs for mechanical systems. Despite the widespread use of HPC for PROMs, there is a significant lack of publications detailing comprehensive workflows for building and deploying end-to-end PROMs in HPC environments. Our workflow is validated through a case study focusing on the thermal dynamics of a motor, a multiphysics problem involving convective heat transfer and mechanical components. The PROM is designed to deliver a real-time prognosis tool that could enable rapid and safe motor restarts post-emergency shutdowns under different operating conditions, demonstrating its potential impact on the practice of simulations in engineering mechanics. To facilitate deployment, we use the Workflow as a Service (WaaS) strategy and Functional Mock-Up Units (FMUs) to ensure compatibility and ease of integration across HPC, edge, and cloud environments. The outcomes illustrate the efficacy of combining PROMs and HPC, establishing a precedent for scalable, real-time digital twin applications in computational mechanics across multiple industries.
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