Parallel reduced-order modeling for digital twins using high-performance computing workflows

Abstract

The integration of reduced-order models with high-performance computing is critical for developing digital twins, particularly for real-time monitoring and predictive maintenance of industrial systems. This paper presents a comprehensive, high-performance computing-enabled workflow for developing and deploying projection-based reduced-order models for large-scale mechanical simulations. We use PyCOMPSs’ parallel framework to efficiently execute reduced-order model training simulations, employing parallel singular value decomposition algorithms such as randomized singular value decomposition, Lanczos singular value decomposition, and full singular value decomposition based on tall-skinny QR. Moreover, we introduce a partitioned version of the hyperreduction scheme known as the Empirical Cubature Method to further enhance computational efficiency in projection-based reduced-order models for mechanical systems. Despite the widespread use of high-performance computing for projection-based reduced-order models, there is a significant lack of publications detailing comprehensive workflows for building and deploying end-to-end projection-based reduced-order models in high-performance computing 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 projection-based reduced-order model 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 High-Performance Computing Workflow as a Service strategy and Functional Mock-Up Units to ensure compatibility and ease of integration across high-performance computing, edge, and cloud environments. The outcomes illustrate the efficacy of combining projection-based reduced-order models and high-performance computing, establishing a precedent for scalable, real-time digital twin applications in computational mechanics across multiple industries.