Turbulent flows are a classical example of complex, multi-scale physical systems. First-principle-based scale-resolving models are prohibitively expensive, and industrial flow simulations often rely on turbulence closure models. In light of the decades-long stagnation in traditional turbulence modeling, data-driven methods have been proposed as a promising alternative. We present a comprehensive framework for using data to reduce model uncertainties in turbulent flow simulations. For online, continuously streamed monitoring data, we use data assimilation and Bayesian inference to reduce model-form uncertainties. With offline data from a database of flows, we proposed a physics-informed machine learning approach to reduce model discrepancies. In both cases, we emphasized enforcing physical constraints in the data-driven modeling. Code available at: https://github.com/xiaoh/turbulence-modeling-PIML
Dr. Heng Xiao is an Assistant Professor in the Department of Aerospace and Ocean Engineering at Virginia Tech since 2013. He holds a bachelor’s degree from Zhejiang University, China, and a Ph.D. degree from Princeton University, USA. His current research interests lie in data driven methods and uncertainty quantification in turbulence modeling.