MAE 298 SEMINAR: Stochastic estimation and reinforcement learning control of disturbed aerodynamic flows

Abstract: There is a wide variety of applications in which we may need knowledge of a transient fluid flow, but we only have information from a few noisy sensors. For example, small flight vehicles, targeted for many emerging applications, are more agile but also more strongly affected by unexpected disturbances (gusts) than larger vehicles. The nonlinear aerodynamics of these gust encounters remains a principal challenge in controlling the vehicle’s flight. In particular, any such flight control strategy is generally more effective if it can rely on an estimation of the vehicle’s current flow state from available sensors. In this talk, I will discuss the dynamic estimation of flows from limited sensor data, and the control of the flow with deep learning strategies. In the first part, I will discuss aspects of the flow estimation problem within the context of Bayesian inference and ensemble Kalman filters, which allow us to easily combine physics-based and/or data-driven models of the flow with measurement data from sensors. The assimilation of these measurements can compensate for the physics that are unrepresented in the model. In the examples I will show, we use the estimation framework to predict the fluid dynamics of a separated aerodynamic flow subjected to a gust, relying on the surface pressure measurements to inform the model of the gust. Then, I will discuss the use of deep reinforcement learning to develop strategies for the mitigation of gust encounters, based on available sensor data.

Bio: Jeff Eldredge is professor and chair of the Department of Mechanical & Aerospace Engineering at UCLA, where he has served on the faculty since 2003. Prior to this, he received his Ph.D. from Caltech, followed by postdoctoral research at Cambridge University. His research interests lie in computational and theoretical studies of fluid dynamics, including numerical simulation and low-order modeling of unsteady aerodynamics; investigations of aquatic and aerial locomotion in biological and bioinspired systems; and investigations of biomedical and biomedical device flows. He is the author of numerous papers, as well as the book Mathematical Modeling of Unsteady Inviscid Flows. He is a fellow of the American Physical Society, an associate fellow of AIAA, and a recipient of the NSF CAREER award and the UCLA Distinguished Teaching award.