MMAE Seminar by Clancy Rowley: Data-Driven Modeling of Fluids

Armour College of Engineering’s Department of Mechanical, Materials, and Aerospace Engineering will welcome Clancy Rowley, the Sin-I Cheng Professor of Engineering Science in the Department of Mechanical and Aerospace Engineering at Princeton University, to present a lecture, “Data-Driven Modeling of Fluids.”

This in-person seminar is a part of the Midwest Seminar Series. It will take place on Wednesday, October 20, 2021, from 12:45–1:45 pm in the John T. Rettaliata Engineering Center. Contact Elena Magnus at magnus@iit.edu for the seminar details.

Abstract

Fluid flows can be extraordinarily complex, and even turbulent, yet often there is structure lying within the apparent complexity. Understanding this structure can help explain observed physical phenomena, and can help with the design of control strategies in situations where one would like to change the natural state of a flow. This talk addresses techniques for obtaining simple, approximate models for fluid flows, using data from simulations or experiments. We discuss a number of methods, including principal component analysis, balanced truncation, and Koopman operator methods, and focus on a new method for optimizing projections of nonlinear systems. We apply these techniques to several flows with complex behavior, including a transitional channel flow and an axisymmetric jet.

Biography

Clancy Rowley is the Sin-I Cheng Professor of Engineering Science in the Department of Mechanical and Aerospace Engineering at Princeton University, and is an associated faculty in the program in applied and computational mathematics. He received his undergraduate degree from Princeton and his doctoral degree from Caltech, both in mechanical engineering. He has received several awards, including an NSF CAREER Award and an AFOSR Young Investigator Award, and he is a fellow of the American Physical Society. His research interests lie at the intersection of dynamical systems, control theory, and fluid mechanics, and focus on reduced-order models suitable for analysis and control design.