Closed-loop control of an experimental mixing layer using MLC

A novel framework for closed-loop control of turbulent flows is tested for an experimental mixing layer flow. This framework, called Machine Learning Control (MLC), provides a model-free method of searching for the best control law (see talk of B.~R.\ Noack). Here, MLC is benchmarked against classical open-loop actuation of the mixing layer. Results show that this method is capable of producing sensor-based control laws which can rival or surpass the best open-loop forcing, and be robust to changing flow conditions. Additionally, MLC can detect non-linear mechanisms present in the controlled plant, and exploit them to find a better type of actuation than the best periodic forcing. Other experimental shear-flow control studies with MLC will be presented in a talk by T.\ Duriez.