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.

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.