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Artificial intelligence control applied to drag reduction of the fluidic pinball

Abstract : The aim of our work is to advance a self-learning, model-free control method to tame complex nonlinear flows—building on the pioneering work of Dracopoulous. The cornerstone is the formulation of the control problem as a function optimization problem. The control law is derived by solving a nonsmooth optimization problem thanks to an artificial intelligence technique, genetic programming (GP). Metaparameters optimization of the algorithm and complexity penalization have been our main contribution and have been tested on a cluster of three equidistant cylinders immersed in a incoming flow, the fluidic pinball. The means of control is the independent rotation of the cylinders. GP derived a control law associated to each cylinder in order to minimize the net drag power and managed to outperform past open-loop studies with a 46.0 % net drag power reduction by combining two strategies from literature. This success of MIMO control including sensor history is promising for exploring even more complex dynamics.
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https://hal.archives-ouvertes.fr/hal-02387482
Contributor : Limsi Publications <>
Submitted on : Friday, November 29, 2019 - 6:13:15 PM
Last modification on : Wednesday, April 14, 2021 - 3:40:53 AM

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  • HAL Id : hal-02387482, version 1

Citation

Guy Yoslan Cornejo Maceda, Bernd R. Noack, François Lusseyran, Marek Morzynski, Nan Deng, et al.. Artificial intelligence control applied to drag reduction of the fluidic pinball. Proceedings in Applied Mathematics and Mechanics, John Wiley & Sons, Inc. 2019, 19, 2p. ⟨hal-02387482⟩

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