Comparative analysis of machine learning methods for active flow control

TL;DR

This paper compares machine learning methods like GP and RL with global optimization techniques for active flow control, analyzing their performance across diverse flow control problems to guide future hybrid approaches.

Contribution

It provides a comprehensive comparison of ML-based control algorithms and global optimization methods applied to flow control, highlighting their strengths and differences.

Findings

GP and RL show different exploration-exploitation balances.

Global optimization methods perform competitively in flow control tasks.

Insights suggest potential for hybridizing methods for improved flow control.

Abstract

Machine learning frameworks such as Genetic Programming (GP) and Reinforcement Learning (RL) are gaining popularity in flow control. This work presents a comparative analysis of the two, bench-marking some of their most representative algorithms against global optimization techniques such as Bayesian Optimization (BO) and Lipschitz global optimization (LIPO). First, we review the general framework of the model-free control problem, bringing together all methods as black-box optimization problems. Then, we test the control algorithms on three test cases. These are (1) the stabilization of a nonlinear dynamical system featuring frequency cross-talk, (2) the wave cancellation from a Burgers' flow and (3) the drag reduction in a cylinder wake flow. We present a comprehensive comparison to illustrate their differences in exploration versus exploitation and their balance between `model capacity' in the control law definition versus `required complexity'. We believe that such a comparison paves the way toward the hybridization of the various methods, and we offer some perspective on their future development in the literature on flow control problems.