报告内容简介：

Many complex systems are characterized by physics at a wide range of scales, for which first-principle-based high-fidelity models resolving all the scales are prohibitively expensive to run. Consequently, practical simulations have primarily relied on low-fidelity models with approximate closure models, which introduce large model-form uncertainties and diminish their predictive capabilities. Turbulent flows are a classical example of such complex physical systems, where numerical solvers with turbulence closure models are widely used in industrial flow simulations. 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 techniques to reduce model-form uncertainties; For offline data from a database of flows, we proposed a physics-informed machine learning approach. While the focus is on turbulent flows, the framework is general enough for other complex physical systems.

报告人简介：

Dr. Heng Xiao is an Assistant Professor in the Department of Aerospace and Ocean Engineering at Virginia Tech. He holds a bachelor’s degree in Civil Engineering from Zhejiang University, China, a master’s degree in Mathematics from the Royal Institute of Technology (KTH), Sweden, and a Ph.D. degree in Civil Engineering from Princeton University, USA. Before joining Virginia Tech in 2013, he worked as a postdoctoral researcher at the Institute of Fluid Dynamics in ETH Zurich, Switzerland, from 2009 to 2012. His current research interests lie in data-driven modeling and uncertainty quantification in turbulent flow simulations. He is also interested in developing novel algorithms for high-fidelity simulations of particle-laden flows with application to sediment transport problems.

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