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Artificial intelligence control applied to drag reduction of the fluidic pinball

Abstract : Feedback turbulence control is at the core of engineering challenges and have to face high-dimensionality, time-delays, strong nonlinearities and frequency crosstalks, making modeling and linear control theory impractical. The aim of this project is a general, model-free, self-learning control strategy to tame/stabilize nonlinear dynamics and real world turbulence in the plant. The control problem is solved as a regression problem thanks to machine learning control (MLC) (Duriez et al. 2016 Springer). MLC is based on genetic programming (GP), it is a biological inspired method that mimickes the Darwinian process of natural selection to learn the control. Focus of current efforts is to understand the learning process, to reduce this learning time and to include real-world imperfections. Our genetic programming control has been demonstrated on a direct numerical simulation of the fluidic pinball taken as a drag reduction benchmark. It consists of three cylinders in a two-dimensional flow where the actuators are the spinning cylinders and feedback is provided by sensors downstream. Despite the simple configuration, the fluidic pinball shares characteristics with real flows such a bifurcations, nonlinear frequency crosstalk and multiple input multiple output (MIMO) control. We carried out a parameter optimization study on GP and managed to reduce the learning rate by a factor 5 by avoiding the evaluation of redundant control laws. After 1000 evaluations, GP managed to find a non-trivial solution comprising two distinct actuation mechanisms : boat-tailing (open-loop) and phasor control (closed-loop) reducing even more the net drag power (46%) than the best boat-tailing configuration (43%).
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Submitted on : Friday, November 29, 2019 - 7:27:27 PM
Last modification on : Saturday, June 25, 2022 - 10:41:58 PM


  • HAL Id : hal-02387548, version 1


Guy Yoslan Cornejo Maceda, Bernd R. Noack, François Lusseyran, Marek Morzynski, Luc Pastur, et al.. Artificial intelligence control applied to drag reduction of the fluidic pinball. Journées du GDR Contrôle Des Décollements, Nov 2018, Toulouse, France. ⟨hal-02387548⟩



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