Stabilization of a multi-frequency open cavity flow with gradient-enriched machine learning control
Résumé
We stabilize an open cavity flow experiment to 1% of its original fluctuation level. For the
first time, a multi-modal feedback control is automatically learned for this configuration.
The key enabler is automatic in situ optimization of control laws with machine learning
augmented by a gradient descent algorithm, named gradient-enriched machine learning
control (Cornejo Maceda et al., J. Fluid Mech., vol. 917, 2021, A42, gMLC). The physical
interpretation of the feedback mechanism is assisted by a novel cluster-based control
law visualization for the flow dynamics and corresponding actuation commands. Starting
points of the control experiment are two unforced open cavity benchmark configurations:
a narrow-bandwidth regime with a single dominant frequency and a mode-switching
regime where two frequencies compete. The flow is forced by a dielectric barrier discharge
actuator located at the leading edge and is monitored by a downstream hot-wire sensor over
the trailing edge. The feedback law is optimized with respect to the monitored fluctuation
level. As reference, the self-oscillations of the mixing layer are mitigated with steady
actuation. Then, a feedback controller is optimized with gMLC. As expected, feedback
control outperforms steady actuation by achieving a better amplitude reduction with
approximately 1% of the actuation energy required for similarly effective steady forcing.
Intriguingly, optimized laws learned for one regime perform well for the other untested
regime as well. The proposed control strategy can be expected to be applicable for many
other shear flow experiments.
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