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Session: Flow Control Applications
Session starts: Tuesday 04 November, 11:40
Presentation starts: 12:00
Room: Commission roon 2

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Sergio Garcia Villasol (Delft University of Technology)
Babak Mohammadikalakoo (Delft University of Technology)
Marios Kotsonis (Delft University of Technology)
Nguyen Anh Khoa Doan (Delft University of Technology)

Abstract:
Controlling the laminar-turbulent transition is essential for reducing skin friction drag, as aerodynamic drag is closely linked to the extent of laminar flow over aerodynamic surfaces. By attenuating the early growth of Tollmien-Schlichting (TS) waves, it is possible to delay transition on unswept wings in low freestream, low turbulence flows. However, controlling TS waves is challenging and requires complex control laws that could be supported by AI-driven optimization methods. This experimental research explores single-step deep reinforcement learning (SDRL) algorithms to control TS waves on a flat plate, using dielectric barrier discharge (DBD) plasma actuators as a real-time active flow control (AFC) system and microphones to monitor the evolution of the waves. In order to complement the pressure measurements, particle image velocimetry (PIV) is also utilized to evaluate the performance of the controller. The experimental campaign is conducted at the anechoic vertical wind tunnel (i.e. A-Tunnel) of the Low Speed Wind Tunnel Laboratory of Delft University of Technology.