THEMED PAPERS ON ARTIFICIAL INTELLIGENCE IN FLUID MECHANICS
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Bayesian optimization for active flow control
Antoine B. Blanchard1 , Guy Y. Cornejo Maceda2 , Dewei Fan3 , Yiqing Li4 , Yu Zhou3 , Bernd R. Noack4 , Themistoklis P. Sapsis1
1 Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA; 2 Universit′e Paris-Saclay, CNRS, Laboratoire Interdisciplinaire des Sciences du Num′erique, 91400 Orsay, France; 3 Center for Turbulence Control, Harbin Institute of Technology, Shenzhen 518058, China; 4 School of Mechanical Engineering and Automation, Harbin Institute of Technology, Shenzhen 518058, China
Abstract A key question in flow control is that of the design of optimal controllers when the control space is high-dimensional and the experimental or computational budget is limited. We address this formidable challenge using a particular flavor of machine learning and 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. The proposed methodology can be used to design open-loop controllers for virtually any complex flow and, therefore, has significant implications for active flow control at an industrial scale.
Corresponding Authors:
Bernd R. Noack, Themistoklis P. Sapsis
E-mail: bernd.noack@hit.edu.cn, sapsis@mit.edu
Cite this article:
Antoine B. Blanchard,Guy Y. Cornejo Maceda,Dewei Fan et al. Bayesian optimization for active flow control[J]. Acta Mechanica Sinica, 2021, 37(12): 1788-1800.
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