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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 [1]. 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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Guy Cornejo Maceda, Bernd Noack, François Lusseyran, Nan Deng, Luc Pastur, et al.. Artificial intelligence control applied to drag reduction of the fluidic pinball. PAMM, Wiley-VCH Verlag, 2019, 90th Annual Meeting of the International Association of Applied Mathematics and Mechanics (GAMM), 19 (1), pp.e201900268. ⟨10.1002/pamm.201900268⟩. ⟨hal-02398649⟩

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