Mixing layer manipulation experiment: From open-loop forcing to closed-loop machine learning control

Vladimir Parezanović, Jean Charles Laurentie, Carine Fourment, Joël Delville, Jean Paul Bonnet, Andreas Spohn, Thomas Duriez, Laurent Cordier, Bernd R. Noack, Markus Abel, Marc Segond, Tamir Shaqarin, Steven L. Brunton

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

Open- and closed-loop control of a turbulent mixing layer is experimentally performed in a dedicated large scale, low speed wind-tunnel facility. The flow is manipulated by an array of fluidic micro-valve actuators integrated into the trailing edge of a splitter plate. Sensing is performed using a rake of hot-wire probes downstream of the splitter plate in the mixing layer. The control goal is the manipulation of the local fluctuating energy level. The mixing layer's response to the control is tested with open-loop forcing with a wide range of actuation frequencies. Results are discussed for different closed-loop control approaches, such as: adaptive extremum-seeking and in-time POD mode feedback control. In addition, we propose Machine Learning Control (MLC) as a model-free closed-loop control method. MLC arrives reproducibly at the near-optimal in-time control.

Original languageBritish English
Pages (from-to)155-173
Number of pages19
JournalFlow, Turbulence and Combustion
Volume94
Issue number1
DOIs
StatePublished - 1 Jan 2015

Keywords

  • Active flow control
  • Extremum seeking
  • Genetic programming
  • Machine learning
  • POD
  • Shear flow
  • Turbulence

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