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Turbulence Control: From Model-Based to Machine Learned

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Fluids Under Control

Part of the book series: Advances in Mathematical Fluid Mechanics ((AMFM))

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Abstract

Flow analysis, modeling, and control are at the heart of engineering applications: aeronautics, airborne and ground transport, wind power generation, and industrial processes, to cite a few examples (Brunton and Noack (Appl Mech Rev 67(5):050801:01–48, 2015)). Fluid flows are characterized by high dimensionality, nonlinearity, multiscale, and time delays, which pose challenges to existing theories and methods for analysis, modeling, and control. Methods of big data, machine learning, and artificial intelligence are revolutionizing these fields and are going toward full automation.

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under grants 12172109, 12172111, 12202121, and 12302293, by the China Postdoctoral Science Foundation under grants 2023M730866 and 2023T160166, by the Guangdong Basic and Applied Basic Research Foundation under grant 2022A1515011492, and by the Shenzhen Science and Technology Program under grant JCYJ20220531095605012.

We appreciate the support from Marek Morzyński for the numerical simulations, and valuable discussions with Dehui Huang, Chang Hou, Yiqing (Anne) Li, Yanting Liu, François Lusseyran, Luc R. Pastur, Wei Sun, Xin Wang, and last but not least our unforgettable HIT master students (2019–2021) Bingxi Huang, Wenpeng Li, Qixin (Kiki) Lin, Ruixuan (Rick) Shen and Shangyan (Shane) Xie.

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Authors and Affiliations

  1. HIT Shenzhen, Shenzhen, China

    Nan Deng, Guy Y. Cornejo Maceda & Bernd R. Noack

Authors
  1. Nan Deng
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  2. Guy Y. Cornejo Maceda
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  3. Bernd R. Noack
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Correspondence to Bernd R. Noack .

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Editors and Affiliations

  1. Department of Technical Mathematics, Czech Technical University, Prague, Czech Republic

    Tomáš Bodnár

  2. University of Pittsburgh, Pittsburgh, PA, USA

    Giovanni P. Galdi

  3. Institute of Mathematics, Czech Academy of Sciences, Prague, Czech Republic

    Šárka Nečasová

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Deng, N., Cornejo Maceda, G.Y., Noack, B.R. (2024). Turbulence Control: From Model-Based to Machine Learned. In: Bodnár, T., Galdi, G.P., Nečasová, Š. (eds) Fluids Under Control. Advances in Mathematical Fluid Mechanics. Birkhäuser, Cham. https://doi.org/10.1007/978-3-031-47355-5_4

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