Cisco Webex, Online seminar
(線上演講 Cisco Webex)
Data-Driven Large Eddy Simulation Reduced Order Models (LES-ROMs) for Turbulent Flows
Ping-Hsuan Tsai (Virginia Tech)
Abstract
Fluid-thermal analysis plays a critical role in understanding and predicting many important phenomena in engineering and scientific applications. Advances in high-performance computing and numerical algorithms have enabled scientists and engineers to perform detailed simulations of complex fluid-thermal processes, providing powerful tools to guide scientific discovery and prototype new technologies before costly manufacturing and deployment. However, despite these advances, the computational cost of high-fidelity simulations remains prohibitive for multi-query tasks such as uncertainty quantification, design optimization, and routine analysis.
Reduced-order modeling (ROM) offers a promising alternative for such scenarios by providing surrogate models that approximate the governing equations with significantly lower computational cost than full-order models (FOMs) while maintaining acceptable accuracy. Classical Galerkin ROMs (G-ROMs) have demonstrated success in modeling laminar flows. However, for turbulent flows, G-ROMs often suffer from spurious numerical oscillations due to the truncated reduced space, which lacks the high-wavenumber modes necessary for proper energy dissipation.
In this talk, I will introduce large-eddy simulation reduced-order models (LES-ROMs), which leverage closure and stabilization techniques inspired by LES to construct accurate and efficient ROMs for turbulent flows. I will highlight the role of ROM spatial filtering, the key tool in LES-ROM development, which enables the modeling of large spatial structures in turbulent flows. I will also present results that illustrate the success of LES-ROMs across several challenging flow problems. In addition, I will discuss numerical analysis results proven for the LES ROMs, including fundamental properties such as stability, convergence, and parameter scalings that are important for practical use. To conclude, I will highlight recent efforts to integrate data-driven modeling strategies, such as symbolic regression and machine learning, into the LES-ROM framework to enhance its accuracy and robustness, and I will close with an outlook on future directions for advancing reduced-order modeling of turbulent flows.
Meeting number (access code): 2517 787 3138
Meeting password: z2UmUXUpA43
Organizer: Te-Sheng Lin (NYCU)
