Closed-loop turbulence control with machine learning methods

We propose a machine learning control strategy for arbitrary turbulent flow configurations with finite number of actuators and sensors. This method designs and optimizes closed-loop control laws automatically detecting and exploiting linear to strongly non-linear actuation mechanisms. Presented examples range from a simple analytical model to the TUCOROM mixing layer control demonstrator.