Abstract: Controlling the behavior of flows around air, marine, and ground vehicles can greatly enhance their performance, efficiency, and safety. The challenge in achieving effective control of unsteady flows is caused by their high-dimensionality, strong nonlinearity, and multi-scale properties. The spatial and temporal resolution requirements to fully capture flow physics necessitate these flows to be described with a typical dimension of at least a million. Without the reduction of the state variable dimension and extraction of important dynamics, the application of dynamical systems and control theory for flow control becomes a challenging task. We aim to develop physics-based approaches to model and control complex fluid flows by leveraging high-performance computing, modal analysis methods (POD, DMD, global/parabolized stability, and resolvent analyses), network science, and machine learning. Equipped with these toolsets, we can extract essential dynamics to facilitate the development of sparse and reduced-order models to design flow control techniques for high-dimensional unsteady fluid flows. We discuss some of the challenges and successes our research group has encountered in characterizing, modeling, and controlling unsteady bluff-body wakes and stalled flows over wings. The techniques developed here are tested in full DNS and LES computations for validations. At the end of the talk, we provide some outlook on incorporating emerging techniques for unsteady flow problems with increased complexity.
Bio-sketch: Kunihiko (Sam) Taira is an Associate Professor of Mechanical Engineering at the Florida State University. His research focuses on computational fluid dynamics, flow control, and network science. He received his B.S. degree from the University of Tennessee, and his M.S. and Ph.D. degrees from the California Institute of Technology. He is a recipient of the 2013 U.S. Air Force Office of Scientific Research and 2016 Office of Naval Research Young Investigator Awards.
Date(s) - Feb 20, 2018
2:00 pm - 3:00 pm