Closed-loop separation control using machine learning

TL;DR

This paper introduces a model-free, machine learning-based closed-loop flow control method using genetic programming, demonstrating robustness and effectiveness in controlling separated flow over a backward-facing step.

Contribution

It presents a novel genetic programming approach for automatic generation of control laws in experimental flow control, emphasizing robustness and physical insight.

Findings

Effective control law achieved for separated flow at Re=1350

Control law is robust to major flow state changes

New control law leverages recirculation physics, differing from traditional methods

Abstract

A novel, model free, approach to experimental closed-loop flow control is implemented on a separated flow. Feedback control laws are generated using genetic programming where they are optimized using replication, mutation and cross-over of best performing laws to produce a new generation of candidate control laws. This optimization process is applied automatically to a backward-facing step flow at Re=1350, controlled by a slotted jet, yielding an effective control law. Convergence criterion are suggested. The law is able to produce effective action even with major changes in the flow state, demonstrating its robustness. The underlying physical mechanisms leveraged by the law are analyzed and discussed. Contrary to traditional periodic forcing of the shear layer, this new control law plays on the physics of the recirculation area downstream the step. While both control actions are fundamentally different they still achieve the same level of effectiveness. Furthermore the new law is also potentially easier and cheaper to implement actuator wise.