Machine learning control for experimental shear flows targeting the reduction of a recirculation bubble

Abstract

The goal is to experimentally reduce the recirculation zone of a turbulent flow (ReH = 31500). The flow is manipulated by a row of micro-blowers (pulsed jets) that are able to generate unsteady jets proportional to any variable DC. Already, periodic jet injection at a forcing frequency of StH = 0.226 can effectively reduce the reattachment length and thus the recirculation zone. A model-free machine learning control (MLC) is used to improve performance. MLC optimizes a control law with respect to a cost function and applies genetic programming as regression technique. The cost function is based on the recirculation length and penalizes actuation. MLC is shown to outperform periodic forcing. The current study demonstrates the efficacy of MLC to reduce the recirculation zone in a turbulent flow regime. Given current and past successes, we anticipate numerous experimental MLC applications.