Machine learning open-loop control of a mixing layer

We develop an open-loop control system using machine learning to destabilize and stabilize the mixing layer. The open-loop control law comprising harmonic functions is explored using the linear genetic programming in a purely data-driven and model-free manner. The best destabilization control law ex...

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Bibliographic Details
Published inPhysics of fluids (1994) Vol. 32; no. 11
Main Author Noack, Bernd R.
Format Journal Article
LanguageEnglish
Published Melville American Institute of Physics 01.11.2020
Subjects
Computational fluid dynamics
Control theory
Destabilization
Fluid dynamics
Fluid flow
Genetic algorithms
Harmonic functions
Machine learning
Physics
Square waves
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Summary:We develop an open-loop control system using machine learning to destabilize and stabilize the mixing layer. The open-loop control law comprising harmonic functions is explored using the linear genetic programming in a purely data-driven and model-free manner. The best destabilization control law exhibits a square wave with two alternating duty cycles. The forced flow presents a 2.5 times increase in the fluctuation energy undergoing early multiple vortex-pairing. The best stabilization control law tames the mixing layer into pure Kelvin–Helmholtz vortices without following vortex-pairing. The 23% reduction of fluctuation energy is achieved under the dual high-frequency actuations.
Bibliography:SourceType-Scholarly Journals-1
ObjectType-Correspondence-1
content type line 14
ISSN:1070-6631
1089-7666
DOI:10.1063/5.0030071