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Communication Dans Un Congrès Année : 2018

Closed-loop turbulence control: From human to machine learning (and retour)

Ruying Li
  • Fonction : Auteur
Jacques Borã©e
  • Fonction : Auteur
Fabien Harambat
  • Fonction : Auteur
D. Fan
  • Fonction : Auteur
Z. Wu
  • Fonction : Auteur
Y. Zhou
  • Fonction : Auteur
Marek Morzynski
  • Fonction : Auteur
Eurika Kaiser
  • Fonction : Auteur
Jean-Christophe Loiseau
  • Fonction : Auteur
Steven L. Brunton
  • Fonction : Auteur

Résumé

Closed-loop turbulence control is a rapidly evolving, interdisciplinary field of research. The range of current and future engineering applications 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 talk will address these nonlinear actuation mechanisms. 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. Recent examples include the stabilization of the fluidic pinball, drag reduction of a car model at high Reynolds numbers and mixing optimization of a turbulent jet. In all studies, MLC has reproduced or outperformed existing optimized control strategies, as will also be elaborated in other talks at this Minisymposium. 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 actuation performance and for physical understanding.
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Dates et versions

hal-01856259 , version 1 (10-08-2018)

Identifiants

  • HAL Id : hal-01856259 , version 1

Citer

Bernd R. Noack, Guy Yoslan Cornejo Maceda, Luc Pastur, François Lusseyran, Ruying Li, et al.. Closed-loop turbulence control: From human to machine learning (and retour). GAMM Annual Meeting, Mar 2018, München, Germany. ⟨hal-01856259⟩
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