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Closed-Loop Turbulence Control-From Human to Machine Learning (and Retour)

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Book cover Fluid-Structure-Sound Interactions and Control (FSSIC 2017)

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Abstract

Feedback turbulence control is a rapidly evolving, interdisciplinary field of research. The range of current and future engineering applications of closed-loop turbulence control has truly epic proportions, including cars, trains, airplanes, noise, air conditioning, medical applications, wind turbines, combustors, and energy systems. A key feature, opportunity and technical challenge of closed-loop turbulence control is the inherent nonlinearity of the actuation response. For instance, excitation at a given frequency will affect also other frequencies. This frequency crosstalk is not accessible in any linear control framework. This paper will address these nonlinear actuation mechanisms in three parts. First, success stories of human learning in turbulence control are presented, i.e. cases in which the nonlinear actuation mechanism has been modelled and understood. A large class of literature studies can be categorized in terms of surprisingly few mechanisms. Second, we discuss model-free machine learning control (MLC) and selected applications. MLC detects and exploits the winning actuation mechanisms in the experiment in an unsupervised manner. In all studies MLC has reproduced or outperformed existing optimized control strategies. Finally, future directions of turbulence control are outlined. Methods of machine learning are a disruptive technology will contribute to rapidly accelerating progress in turbulence control—both for performance and for physical understanding.

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Acknowledgements

This material presented here was only possible through the hard and enthusiastic work of my former PhD students Diogo Barros, Eurika Kaiser, Ruiying Li, Mark Luchtenburg and Mark Pastoor, my former Postdocs Thomas Duriez and Vladimir Parezanović, other members of the former TUCOROM Team (Jean-Paul Bonnet, Jacques Borée, Laurent Cordier, and Andreas Spohn) and the fruitful collaborations with Markus Abel, Jean-Luc Aider, Steven Brunton, Camila Chovet, Guy Yoslan Cornejo Maceda, Nan Deng, Hiroaki Fukumoto, Nicolas Gautiers, Rudibert King, Laurent Keirsbulck, Azeddine Kourta, François Lusseyran, Lionel Mathelin, Robert Martinuzzi, Marek Morzyński, Robert Niven, Akira Oyama, Luc Pastur, Cedric Raibaudo, Richard Semaan and Michel Stanislas. Three PhD theses have been supported by the OpenLab Fluidics between PSA Peugeot-Citroën and Institute Pprime (Fluidics@poitiers) and L’Ecole Doctorale SMeMAG at LIMSI-CNRS. This work is also supported by a public grant overseen by the French National Research Agency (ANR) as part of the “Investissement dAvenir” program, through the “iCODE Institute project” funded by the IDEX Paris-Saclay, ANR-11-IDEX-0003-02.

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Authors and Affiliations

  1. LIMSI-CNRS, Rue John von Neumann, Campus Universitaire d’Orsay, Bât 508, 91403, Orsay, France

    Bernd R. Noack

  2. Harbin Institute of Technology, Shenzhen Campus, Shenzhen, People’s Republic of China

    Bernd R. Noack

  3. Technische Universität Braunschweig, Braunschweig, Germany

    Bernd R. Noack

  4. Technische Universität Berlin, Berlin, Germany

    Bernd R. Noack

  5. Institute PPRIME, Chasseneuil-du-Poitou, France

    Bernd R. Noack

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  1. Bernd R. Noack
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Correspondence to Bernd R. Noack .

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Editors and Affiliations

  1. Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, China

    Yu Zhou

  2. Nihon University, Tokyo, Japan

    Motoaki Kimura

  3. Nihon University, Tokyo, Japan

    Guoyi Peng

  4. Curtin University, Perth, Western Autralia, Australia

    A.D. Lucey

  5. The University of Hong Kong, Hong Kong, China

    Lixi Huang

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© 2019 Springer Nature Singapore Pte Ltd.

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Noack, B.R. (2019). Closed-Loop Turbulence Control-From Human to Machine Learning (and Retour). In: Zhou, Y., Kimura, M., Peng, G., Lucey, A., Huang, L. (eds) Fluid-Structure-Sound Interactions and Control. FSSIC 2017. Lecture Notes in Mechanical Engineering. Springer, Singapore. https://doi.org/10.1007/978-981-10-7542-1_3

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  • DOI: https://doi.org/10.1007/978-981-10-7542-1_3

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-10-7541-4

  • Online ISBN: 978-981-10-7542-1

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