Machine learning control for experimental turbulent flow targeting the reduction of a recirculation bubble

Abstract : We experimentally control the turbulent flow over backward-facing step (Re H = 31500). The goal is to modify the internal (Xr) and external (Lr) recirculation points and consequently the recirculation zone (Ar). A model-free machine learning control (MLC) is used as control logic. As benchmark, an optimized periodic forcing is employed. MLC generalizes periodic forcing by a multi-frequency actuation. In addition, a sensor-based control and a non-autonomous feedback, open- and closed-loop laws, were use to optimize the control. The MLC multi-frequency forcing outperforms, as expected, periodic forcing. The non-autonomous feedback brings a further improvement. The unforced and actuated flows have been investigated in real-time with a TSI particle image velocimetry (PIV) system. The current study shows that a generalization of multi-frequency forcing and sensor feedback significantly reduces the turbulent recirculation zone, far beyond optimized periodic forcing. The study suggests that MLC can effectively explore and optimize new feedback actuation mechanisms and we anticipate MLC to be a game changer in turbulence control.
Complete list of metadatas

Cited literature [20 references]  Display  Hide  Download

https://hal.archives-ouvertes.fr/hal-01856267
Contributor : Limsi Publications <>
Submitted on : Sunday, March 3, 2019 - 7:43:55 PM
Last modification on : Thursday, February 13, 2020 - 10:21:11 AM
Long-term archiving on: Tuesday, June 4, 2019 - 12:41:14 PM

File

chovet2017.pdf
Files produced by the author(s)

Identifiers

Citation

Camila Chovet, Marc Lippert, Laurent Keirsbulck, Bernd R. Noack, Jean-Marc Foucaut. Machine learning control for experimental turbulent flow targeting the reduction of a recirculation bubble. ASME 2017 Fluids Engineering Division Summer Meeting, Jul 2017, Wiakoloa, United States. ⟨10.1115/FEDSM2017-69272⟩. ⟨hal-01856267⟩

Share

Metrics

Record views

82

Files downloads

190