Closed-loop control of experimental shear flows using MLC

We employ a novel closed-loop control strategy for turbulent flows using machine learning methods in a model-free manner (see MLC talk of B.~R.\ Noack). MLC yields in-time control of experimental shear flows and has advantages over the state-of-the-art control. In this talk, MLC is applied to four different experimental closed-loop control setups: (1) the TUCOROM mixing layer tunnel (talk of V.\ Parezanovi\'c), (2) the G\"ortler PMMH water tunnel with a backward facing step, (3) the LML Boundary-Layer wind tunnel with a separating turbulent boundary layer, and (4) the Malavard wind tunnel with the SepaCoDe ramp. In all cases, MLC finds a control which yields a significantly better performance with respect to the given cost functional as compared to the best previously tested open-loop actuation.