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Chief Scientist Distinguished Lecture Series Featuring Professor Kunihiko (Sam) Taira

AFRL/AFOSR Chief Scientist cordially invites you to attend the Chief Scientist Distinguished Lectured Series featuring Professor Kunihiko (Sam) Taira. 

We will be hosting a live ZoomGov Webinar on Thursday, October 12, 2023 (1400-1500pm ET)

Register at https://www.zoomgov.com/webinar/register/WN_V-f9PnuISYuHgMaS8RVhRg
Meeting ID: 161 040 8947
Passcode: 54795257

Talk Titled: "Analysis and Control for Extreme Aerodynamic Flows"

Bio: Kunihiko Taira is a Professor of Mechanical and Aerospace Engineering at UCLA working in the areas of unsteady aerodynamics and flow control, leveraging computations and data-driven techniques. Before joining his current institution, he was a faculty member at Florida State University. he received his B.S. from the University of Tennessee, Knoxville, and his M.S. and Ph.D. from the California Institute of Technology. He is the recipient of the AFOSR Young Investigator Award, the ONR Young Investigator Award, and the DoD Vannevar Bush Faculty Fellowship. he is an Associate Fellow of AIAA and serves as an associate editor for the AIAA Journal and the Theoretical and Computational Fluid Dynamics.

Description: An air vehicle trying to operate in adverse weather or wakes of urban canyons and mountainous terrains would be hit by strong large-scale atmospheric disturbances. In such extreme aerodynamic conditions, flight control becomes a great challenge, if not impossible, due to the enormous transient forces that the vehicle experiences. Currently, encounters with these extreme flow phenomena limit operations of fixed and rotating wing aircraft, especially those taht are small to medium in size. The present study is focused on the analysis, modeling, and control of extreme aerodynamic flows, with unsteadiness far larger in amplitudes than those considered in traditional aerodynamics on a time scale comparable to those of the flow instabilities. The high dimensionality, strong nonlinearity, and multi-scale properties of these extreme flows make effective analysis and control a tremendous challenge. Without the reduction of the state variable dimension and extraction of dominant dynamics, the application of dynamical systems and control theory for flow control remains difficult. This talk will present modern approaches to model and control such complex fluid flows by leveraging data-driven techniques and high-performance computing. We in particular will discuss the use of unsupervised and supervised machine learning techniques and how they can be embedded in existing flow analysis techniques. Equipped with these toolsets, we extract essential dynamics to facilitate the development of sparse and reduced-order models to design flow control techniques for high-dimensional unsteady fluid flows. Some of the successes in characterizing, modeling, and controlling extreme aerodynamic flows will be shown. 

 

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