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Comparative analysis of machine learning methods for active flow control

Time: Thu 2022-09-22 10.30

Location: Faxén, Teknikringen 8

Participating: Miguel Méndez (VKI)

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Abstract: Machine learning frameworks such as Genetic Programming (GP) and Reinforcement Learning (RL) are gaining popularity in flow control. This talk 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). Three test cases of growing complexity are analyzed. 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. A connection to classic control theory is discussed, along with 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 opens the path towards hybridization of the various methods, and we offer some perspective on their future development in the literature of flow control problems.

M. A. Mendez received his PhD in engineering science from “Université Libre de Bruxelles” in 2018. He is currently an Assistant Professor at the von Karman Institute for Fluid Dynamics, where he teaches courses on modelling and control of fluid flows, measurement techniques, signal processing and machine learning. He has extensively used data-driven methods for post-processing numerical and experimental data. His main research activities include experimental fluid mechanics (particularly image-based velocimetry and image processing), reduced-order modelling, flow control, and machine learning.