学术报告

　　杨翔，先后于2012年、2016年在北京大学工学院、约翰霍普金斯大学机械工程系取得本科、博士学位，2016年加入斯坦福大学湍流研究中心，2018年加入宾州州立大学机械工程系，现任助理教授。其研究领域包括直接数值模拟，大涡模拟，湍流建模，计算流体力学建模，机器学习等。现已发表近60篇SCI论文。

　　Direct numerical simulation of turbulence at high, practically relevant Reynolds numbers is prohibitively expensive, making turbulence modeling a necessity. This talk focuses on large-eddy simulation (LES) wall modeling. We will discuss the best practice for wall-modeled LES, exploring both physics-based and data-based wall models. We will cover applications that involve flow separation, heat transfer, buoyancy, and system rotation. We will showcase how one would be able to incorporate physics in a machine learning model and how one may apply machine learning model trained at one condition at other conditions.