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We stabilize an open cavity flow experiment to 1% of its original fluctuationlevel. For the first time, a multi-modal feedback control is automaticallylearned for this configuration. The key enabler is automatic in-situoptimization of control laws with machine learning augmented by a gradientdescent algorithm, named gradient-enriched machine learning control (CornejoMaceda et al. 2021, gMLC). gMLC is shown to learn one order of magnitude fasterthan MLC (Duriez et al. 2017, MLC). The physical interpretation of the feedbackmechanism is assisted by a novel cluster-based control law visualization forflow dynamics and corresponding actuation commands. Starting point of thecontrol experiment are two unforced open cavity benchmark configurations: anarrow-bandwidth regime with a single dominant frequency and a mode-switchingregime where two frequencies compete. The feedback control commands the DBDactuator located at the leading edge. The flow is monitored by a downstreamhot-wire sensor over the trailing edge. The feedback law is optimized withrespect to the monitored fluctuation level. As reference, the self-oscillationsof the mixing layer are mitigated with steady actuation. Then, a feedbackcontroller is optimized with gMLC. As expected, feedback control outperformssteady actuation by achieving both, a better amplitude reduction and asignificantly smaller actuation power, about about 1% of the actuation energyrequired for similarly effective steady forcing. Intriguingly, optimized lawslearned for one regime performs well for the other untested regime as well. Theproposed control strategy can be expected to be applicable for many other shearflow experiments.

Stabilization of a multi-frequency open cavity flow with gradient-enriched machine learning control