Bayesian optimization for active flow control
Name
10409_2021_1149_ReferencePDF.pdf
Size
9.33 MB
Format
Adobe PDF
Checksum (MD5)
89112563584d3cce4ffc31db1fdcec86
Author(s)
Blanchard, Antoine B.
Cornejo Maceda, Guy Y.
Fan, Dewei
Li, Yiqing
Zhou, Yu
Noack, Bernd R.
Sapsis, Themistoklis P.
Date Issued
January 10, 2022
Publisher
The Chinese Society of Theoretical and Applied Mechanics; Institute of Mechanics, Chinese Academy of Sciences
Citation
Blanchard, Antoine B., Cornejo Maceda, Guy Y., Fan, Dewei, Li, Yiqing, Zhou, Yu et al. 2022. "Bayesian optimization for active flow control."
Version
Author's final manuscript
Abstract
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.
Graphic Abstract
MIT Department
Massachusetts Institute of Technology. Department of Mechanical Engineering
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1007/s10409-021-01149-0
