🤖 AI Summary
To address real-time monitoring and predictive maintenance requirements in digital twin applications, this work proposes an end-to-end high-performance computing (HPC)-enabled projection-based reduced-order modeling (PROM) framework for multiphysics simulation of large-scale mechanical systems. Methodologically: (i) a parallelized workflow is designed using PyCOMPSs to enable distributed singular value decomposition (SVD) via randomized, Lanczos, or TSQR algorithms; (ii) a novel partitioned empirical collocation method (ECM) is introduced to significantly accelerate model reduction for ultra-large-scale systems; (iii) Workflow-as-a-Service (WaaS) and Functional Mock-up Units (FMUs) are integrated to support cross-platform deployment across HPC, cloud, and edge environments. Evaluated on a motor thermo-mechanical coupling case, the resulting reduced model achieves millisecond-level real-time prediction, enabling safe and rapid restart after emergency shutdown. This work demonstrates a scalable, high-fidelity, and industrially deployable pathway for digital twin implementation.
📝 Abstract
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.