Abstract

Fluid-thermal analysis plays a critical role in understanding and predicting many important phenomena in engineering and scientific applications. However, despite advances in high-performance computing and numerical algorithms, the computational cost of high-fidelity simulations remains prohibitive for multi-query tasks such as routine analysis and design, uncertainty quantification, and real-time control.

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. I will also outline future directions.

 

Link Information: TBA 

 

Organizers: Tai-Chia Lin (NTU), Min-Jhe Lu (NTHU)