In this talk, some recent applications of deep reinforcement learning (DRL) in active flow control (AFC) on a toy model will be introduced. Here the term AFC means that the control is realized by injecting a small amount of energy into existing flow systems. Compared to its counterpart, i.e., passive flow control, AFC is adaptive and on-demand, and hence has a much wider operating range. First, the use of DRL for AFC is demonstrated through an interesting hydrodynamic problem - hydrodynamic stealth, where the actuation is realized using a group of windward-suction-leeward-blowing (WSLB) actuators. Second, what the DRL agent “thinks” during the learning is interpreted through a vortex-induced vibration control problem. Last, the control is pushed towards the turbulent flow regime. Through these DRL-guided AFC studies, some new and unexpected control strategies have been revealed.
 

Dr. Hui Tang is Associate Professor, Director of Consortium for Multiphysics Fluid Dynamics, and Associate Head (Research) of Department of Mechanical Engineering, The Hong Kong Polytechnic University. He received his BEng and MEng degrees from Tsinghua University and PhD degree from University of Manchester. His research interests include aero/hydrodynamics, active flow control, fluid-structure interaction, and heat and mass transfer. In these fields he has published 90+ peer reviewed papers in prestigious journals in the field including Physical Review Letters, Journal of Fluid Mechanics, Physics of Fluids, and Physical Review Fluids. He is ranked among the world’s top 2% most-cited scientists. He is now an executive committee member of Hong Kong Society of Theoretical and Applied Mechanics (HKSTAM), and serving in editorial board for several journals including Actuators, Fluid and Structure, and Journal of Hydrodynamics.

Please contact phweb@ust.hk should you have questions about the talk.