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Closed-loop separation control using machine learning

Published online by Cambridge University Press:  10 April 2015

N. Gautier*
Laboratoire de Physique et Mécanique des Milieux Hétérogènes (PMMH), UMR 7636 CNRS, École Supérieure de Physique et Chimie Industrielles de la ville de Paris, 10 rue Vauquelin, 75005 Paris, France
J.-L. Aider
Laboratoire de Physique et Mécanique des Milieux Hétérogènes (PMMH), UMR 7636 CNRS, École Supérieure de Physique et Chimie Industrielles de la ville de Paris, 10 rue Vauquelin, 75005 Paris, France
T. Duriez
Institut PPRIME, CNRS – Université de Poitiers – ENSMA, UPR 3346, Département Fluides, Thermique, Combustion CEAT, 43 rue de l’Aérodrome, F-86036 Poitiers CEDEX, France Laboratorio de FluidoDinámica, CONICET/Universidad de Buenos Aires, Facultad de Ingeneria, Paseo Colon 850, Ciudad Autonoma de Buenos Aires, Argentina
B. R. Noack
Institut PPRIME, CNRS – Université de Poitiers – ENSMA, UPR 3346, Département Fluides, Thermique, Combustion CEAT, 43 rue de l’Aérodrome, F-86036 Poitiers CEDEX, France
M. Segond
Ambrosys GmbH, Albert-Einstein-Str. 1-5, D-14469 Potsdam, Germany
M. Abel
Ambrosys GmbH, Albert-Einstein-Str. 1-5, D-14469 Potsdam, Germany
Email address for correspondence:


We present the first closed-loop separation control experiment using a novel, model-free strategy based on genetic programming, which we call ‘machine learning control’. The goal is to reduce the recirculation zone of backward-facing step flow at $\mathit{Re}_{h}=1350$ manipulated by a slotted jet and optically sensed by online particle image velocimetry. The feedback control law is optimized with respect to a cost functional based on the recirculation area and a penalization of the actuation. This optimization is performed employing genetic programming. After 12 generations comprised of 500 individuals, the algorithm converges to a feedback law which reduces the recirculation zone by 80 %. This machine learning control is benchmarked against the best periodic forcing which excites Kelvin–Helmholtz vortices. The machine learning control yields a new actuation mechanism resonating with the low-frequency flapping mode instability. This feedback control performs similarly to periodic forcing at the design condition but outperforms periodic forcing when the Reynolds number is varied by a factor two. The current study indicates that machine learning control can effectively explore and optimize new feedback actuation mechanisms in numerous experimental applications.

© 2015 Cambridge University Press 

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