Stabilization of the fluidic pinball with gradient-enriched machine learning control

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@Article{cornejo_maceda_li_lusseyran_morzynski_noack_2021,

• author = "Guy Y. {Cornejo Maceda} and Yiqing Li and Francois Lusseyran and Marek Morzynski and Bernd R. Noack",
• title = "Stabilization of the fluidic pinball with gradient-enriched machine learning control",
• journal = "Journal of Fluid Mechanics",
• year = "2021",
• volume = "917",
• pages = "A42",
• month = "25 " # jun,
• keywords = "genetic algorithms, genetic programming, flow control, machine learning, wakes",
• publisher = "Cambridge University Press",
• ISSN = "0022-1120",
• DOI = "doi:10.1017/jfm.2021.301",
• size = "43 pages",
• abstract = "We stabilise the flow past a cluster of three rotating cylinders, the fluidic pinball, with automated gradient-enriched machine learning algorithms. The control laws command the rotation speed of each cylinder in an open- and closed-loop manner. These laws are optimized with respect to the average distance from the target steady solution in three successively richer search spaces. First, stabilization is pursued with steady symmetric forcing. Second, we allow for asymmetric steady forcing. And third, we determine an optimal feedback controller employing nine velocity probes downstream. As expected, the control performance increases with every generalization of the search space. Surprisingly, both open- and closed-loop optimal controllers include an asymmetric forcing, which surpasses symmetric forcing. Intriguingly, the best performance is achieved by a combination of phasor control and asymmetric steady forcing. We hypothesize that asymmetric forcing is typical for pitchfork bifurcated dynamics of nominally symmetric configurations. Key enablers are automated machine learning algorithms augmented with gradient search: explorative gradient method for the open-loop parameter optimization and a gradient-enriched machine learning control (gMLC) for the feedback optimization. Gradient-enriched machine learning control learns the control law significantly faster than previously employed genetic programming control. The gMLC source code is freely available online.",

}

Genetic Programming entries for Guy Yoslan Cornejo Maceda Yiqing Li Francois Lusseyran Marek Morzynski Bernd R Noack

Citations