6 papers
Consistency requirement of data-driven subgrid-scale modeling in large-eddy simulation
Xinyi Huang, Sze Chai Leung, H. Jane Bae
Data-driven subgrid-scale (SGS) modeling in the large-eddy simulations (LES) suffers from the inconsistency between the \textit{a priori} tests and the a posteriori tests, which ma…
Effect of subgrid-scale anisotropy on wall-modeled large-eddy simulation of turbulent flow with smooth-body separation
Di Zhou, H. Jane Bae
We examine the role of anisotropic subgrid-scale (SGS) stress in wall-modeled large-eddy simulation (WMLES) of flow over a spanwise-uniform Gaussian-shaped bump, with emphasis on p…
Physics-informed Neural-operator Predictive Control for Drag Reduction in Turbulent Flows
Zelin Zhao, Zongyi Li, Kimia Hassibi +5
Assessing turbulence control effects for wall friction numerically is a significant challenge since it requires expensive simulations of turbulent fluid dynamics. We instead propos…
Data-driven Sensor Placement for Predictive Applications: A Correlation-Assisted Attribution Framework (CAAF)
Sze Chai Leung, Di Zhou, H. Jane Bae
Optimal sensor placement (OSP) is critical for efficient, accurate monitoring, control, and inference in complex physical systems. We propose a machine-learning-based feature attri…
Sensitivity analysis of wall-modeled large-eddy simulation for separated turbulent flow
Di Zhou, H. Jane Bae
In this study, we conduct a parametric analysis to evaluate the sensitivities of wall-modeled large-eddy simulation (LES) with respect to subgrid-scale (SGS) models, mesh resolutio…
Wall Modeling of Turbulent Flows with Varying Pressure Gradients Using Multi-Agent Reinforcement Learning
Di Zhou, H. Jane Bae
We propose a framework for developing wall models for large-eddy simulation that is able to capture pressure-gradient effects using multi-agent reinforcement learning. Within this…