Computational & Applied Math Seminar

High-Order and Reduced-Order Methods in Computational Fluid Dynamics

  • 演讲者:Qian Wang(瑞士洛桑联邦理工学院)

  • 时间:2022-02-17 10:00-11:00

  • 地点:Zoom ID 915 4346 4215, Passcode 123456

Abstract
Computational fluid dynamics (CFD) is being used routinely and with success in many areas, such as aerospace engineering. In particular for time resolved and multi-scale simulations, such as Direct Numerical Simulation (DNS) and Large-Eddy Simulation (LES) of turbulent flows, high-order methods have received considerable attention because of their potential to deliver a higher accuracy at a lower cost as compared to classic second-order methods that have been widely used in open-source and commercial CFD software.
Even with the computational efficiency improvement by using high-order methods, high-fidelity simulations are not feasible for applications such as design, control, optimization and uncertainty quantification that require repeated model evaluations on a potentially large parameter domain. The need for cost reduction in such applications has led to the development of reduced-order modeling (ROM) that seeks to build reliable and efficient low-dimensional models.
High-order accurate and reduced-order methods have been the focus of my doctoral and postdoctoral research, respectively. In this talk, I will discuss my past and prospective future research projects in some detail.


Biography

Dr. Qian Wang obtained a Bachelor of Engineering degree from Huazhong University of Science and Technology in July 2012. Following graduation, he started working as a PhD student focusing on Computational Fluid Dynamcis (CFD) at Tsinghua University. In July 2017, he obtained a PhD in Engineering Mechanics. His thesis entitled "Compact High-Order Finite Volume Method on Unstructured Grids" won Tsinghua Outstanding PhD Thesis Award. From November 2017 to September 2021, Dr. Wang worked as a postdoctoral fellow in the department of mathematics at EPFL, Switzerland. His primary research interests are in high-order methods for flow simulations, reduced-order modeling of unsteady flows and scientific machine learning.