The engineering turbulence problem and a log(Re) data-driven multi-fidelity solution
In fluid engineering, the turbulence problem often refers to the challenge of achieving cost-effective yet accurate predictions of engineering quantities such as drag and lift. Existing computational solutions to the turbulence problem represent various levels of compromise between accuracy and computational expense. Computational complexity analysis dictates that practical solutions to the engineering turbulence problem must have a cost scaling no worse than a polynomial function of log(Re). To that end, we propose a multi-fidelity, physics-constrained, data-driven framework that achieves high-fidelity accuracy at a nominal cost scaling of log(Re). The framework consists of: (i) a high-fidelity imulation conducted at a fixed, low Reynolds number, (ii) physics-constrained field inversion and machine learning (FIML) to recalibrate a low-fidelity model against the high-fidelity data at the low Reynolds number, and (iii) application of the augmented low-fidelity model at high Reynolds numbers.
Bio: Xiang Yang is the Kenneth K. & Olivia J. Kuo Early Career Professor in the Department of Mechanical Engineering at The Pennsylvania State University. He received his Ph.D. in mechanical engineering from Johns Hopkins University in 2016. Following his Ph.D., Yang was a postdoctoral research fellow at the Center for Turbulence Research at Stanford University. He joined the Penn State mechanical engineering faculty in 2018, where he continues his research in turbulence and computational fluid dynamics.
Yang was awarded the American Physical Society Division of Fluid Dynamics Best Thesis Award in 2017 and the Air Force Office of Scientific Research Young Investigator Award in 2022. His research focuses on high-fidelity simulations of turbulent flows, turbulence modeling that bridges physics and data, and the development of next-generation turbulence theories. He has authored or co-authored over 100 journal articles and serves as an associate editor for the Journal of Fluids Engineering, Acta Mechanica Sinica, and Theoretical and Applied Mechanics Letters.