报告题目：

A Physics-Informed Machine Learning Framework

for Predictive Turbulence Modeling

报告人:                         Dr. Heng Xiao

Assistant Professor, Virginia Tech, USA

时间：2017年1月4日(周三)  09:00-10:00

地点：中科院力学所1号楼344会议室

报告摘要：

Numerical models based on the Reynolds-averaged Navier-Stokes (RANS) equations are widely used in turbulent flow simulations in support of engineering design and optimization. In these models, turbulence modeling introduces significant uncertainties in the predictions. In light of the decades-long stagnation encountered by the traditional approach of turbulence model development, data-driven methods have been proposed as a promising alternative. We will present a data-driven, physics-informed machine learning framework for predictive turbulence modeling based on RANS models. The framework consists of three components: (1) prediction of discrepancies in RANS modeled Reynolds stresses based on machine learning algorithms, (2) propagation of improved Reynolds stresses to quantities of interests with a modified RANS solver, and (3) quantitative, a priori assessment of predictive confidence based on distance metrics in the mean flow feature space. Merits of the proposed framework are demonstrated in a class of flows featuring massive separations. The favorable results suggest that the proposed framework is a promising path toward RANS-based predictive turbulence in the era of big data.

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 model uncertainty quantification and data-driven modeling 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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