学术报告

　　Dr. Xiang Yang is an Assistant Professor in the Department of Mechanical Engineering at Pennsylvania State University. He earned his Ph.D. in Mechanical Engineering from Johns Hopkins University in 2016. Subsequently, Yang joined the Center for Turbulence Research in 2016 as a Postdoctoral Research Fellow before becoming a member of the Mechanical Engineering Department at Penn State in 2018, where he has remained since. Yang was honored with the American Physical Society Division of Fluid Dynamics Best Thesis Award in 2017. He was one of the recipients of the Air Force Office of Scientific Research Young Investigator Award in 2022. Dr. Yang's research is centered around high-fidelity numerical simulations of turbulent flows, turbulence modeling based on physics and data, and the exploration of turbulence theories. With a prolific academic record, he has authored over 90 journal articles.

　　杨翔博士是宾夕法尼亚州立大学机械工程系的助理教授。他于2016年获得约翰霍普金斯大学的机械工程博士学位。随后，他于2016年加入斯坦福大学湍流研究中心，担任博士后研究员。2018年成为宾夕法尼亚州立大学机械工程系的一员，并一直在那里工作。2017年，杨翔获得美国物理学会流体动力学学部最佳博士学位论文奖。他是2022年美国空军科学研究办公室青年研究员奖获得者之一。杨翔博士的研究主要集中在湍流的高精度数值模拟，基于物理和数据的湍流建模，以及湍流理论的探索。他有丰富的学术经历，撰写了90多篇期刊文章。

　　This talk focuses on our recent work on the topic of rough-wall boundary-layer flows. We will first present a survey of the existing rough wall models. We consider three correlation-type rough-wall models, two physics-based models, and one data-driven machine-learning model, and we make use of the rough surfaces in the Roughness Database for testing and re-training purposes. We see that the correlation-type and the machine-learning models do not extrapolate outside the training/calibration dataset, whereas the physics-based model generalize outside its calibration set. Having underscored the importance of flow physics in rough-wall modeling, we proceed by studying flow over tall, slender surface roughness and roughness with spanwise heterogeneity via direct numerical simulation (DNS). Our data provide support to the so-called "mixing layer analogy," according to which the flow within the roughness sublayer is analogous to a plane mixing layer. We further dive into the mechanisms responsible for generating the rough-wall drag by considering the integral methods. By reformulating existing integrals and extending them to flows over rough walls, we arrive at distinct decompositions for the bottom-wall skin friction coefficient and the roughness drag coefficient. These decompositions comprise a viscous term, a dissipation term, and a turbulent term. Our analyses reveal that, with increasing surface coverage density, the magnitude of the viscous term diminishes, while the magnitude of the turbulent term remains approximately constant. Lastly, our DNSs of flow over spanwise heterogeneous roughness lead to the observation of asymmetry mean flow. This asymmetry becomes most pronounced when the surface coverage density is approximately 0.59%, and diminishes as the coverage density approaches either a low or a high value.