MAE Department - PG seminar - Aerodynamic Shape Optimization with Machine Learning
Aerodynamic shape optimization is powerful in automating aircraft design and enhancing design performance. Despite its potential advantages, there are several challenging issues that require more advanced solutions, including robust design, fast interactive design, and design optimization with discontinuous aerodynamic functions. These challenges are primarily caused by the high dimensionality of aerodynamic shapes and the high cost of computational fluid dynamics. In this seminar, we will present our recent research in tackling these fundamental issues using machine learning. We will also discuss the benefits that these approaches in aerodynamic design optimization of airfoils, wings, and aircraft.
Dr. Jichao Li is a research scientist at the Institute of High Performance Computing, A*STAR in Singapore. He earned his B.E. degree and Ph.D. degree from Northwestern Polytechnical University in 2013 and 2019, respectively. From 2016 to 2018, he was a visiting Ph.D. student of the MDO Lab at the University of Michigan. Following his doctoral studies, he served as a postdoctoral research fellow at the National University of Singapore from 2019 to 2022. His research interest is aerodynamic shape optimization of aircraft and wind energy systems.