Abstract:  Small flight vehicles, targeted for many emerging applications, are more agile but also more strongly affected by unexpected disturbances (‘gusts’) than larger vehicles. The non-linear aerodynamics of these gust encounters remains a principal challenge in controlling the vehicle’s flight. In particular, any such flight control strategy must rely on an estimation of the vehicle’s current state from available sensors. In this talk, I will discuss the dynamic estimation of the flow state from limited sensor data. In the first part, I will discuss the use of deep learning tools to train a model to predict flow disturbance parameters from surface pressure sensors. This machine learning architecture also provides a means of systematically optimizing the placement of the sensors. In the second part of the talk, I will discuss the opportunities provided by the Ensemble Kalman Filter, which allows us to easily combine physics-based 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 an aggregated vortex model to predict the fluid dynamics of the separated flow, and rely on the surface pressure measurements to inform the model of disturbances to that flow. The overall estimation algorithm is applied to several scenarios of an impulsively started flat plate, in which measurement data are obtained from a high-fidelity Navier–Stokes simulation. The assimilated vortex model efficiently and accurately predicts the evolving flow as well as the normal force in both the undisturbed case (a separated flow) as well as in the presence of one or more strong disturbances, without any knowledge of the disturbance characteristics.

Biosketch:  Jeff Eldredge is Professor of Mechanical & Aerospace Engineering at the University of California, Los Angeles, where he has served on the faculty since 2003. Prior to this, he received his Ph.D. from Caltech, followed by post-doctoral 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 a Fellow of the American Physical Society and an Associate Fellow of AIAA. He serves on the Editorial Board of Physical Review Fluids and as an Associate Editor for the journal Theoretical and Computational Fluid Dynamics. He is a recipient of the NSF CAREER award.