Abstract We present the first application of Bayesian optimization to the design of open-loop controllers for fluid flows. We consider a range of acquisition functions, including the recently introduced output-informed criteria of Blanchard and Sapsis (2021), and evaluate performance of the Bayesian algorithm in two iconic configurations for active flow control: computationally, with drag reduction in
the fluidic pinball; and experimentally, with mixing enhancement in a turbulent jet. For these flows, we find that Bayesian optimization identifies optimal controllers at a fraction of the cost of other optimization strategies considered in previous studies. Bayesian optimization also provides, as a by-product of the optimization, a surrogate model for the latent cost function, which can be leveraged to paint a complete picture of the control landscape. Implications for active flow control at an industrial scale are discussed.
Antoine B. Blanchard,Guy Y. Cornejo Maceda,Dewei Fan et al. Bayesian Optimization for Active Flow Control[J]. Acta Mechanica Sinica, 10.1007/s10409-021-01149-0.
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