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Efficient flow separation control represents significant economic benefit.This study applies a machine learning algorithm to minimize flow separation inSmart Skin, a flow control device that features distributed-input anddistributed-output (DIDO). Smart Skin comprises 30 hybrid actuator units, eachintegrating a height-adjustable vortex generator and a mini-jet actuator. Theseunits are deployed on a backward-facing ramp to reduce flow separation in adistributed manner. To monitor the flow state, distributed pressure taps aredeployed around the multi-modal actuators. Parametric studies indicate that themapping between control parameters and separation control performance iscomplex. To optimize separation control, a cutting-edge variant of the particleswarm optimization (PSO-TPME) is used for the control parameters in the SmartSkin. This algorithm is capable of achieving fast optimization inhigh-dimensional parameter spaces. The results demonstrate the efficiency ofPSO-TPME, and the optimized solution significantly outperforms the best resultfrom the parametric study. These findings represent a promising future ofmachine learning-based flow control using distributed actuators and sensors.

Smart Skin separation control using distributed-input distributed-output, multi-modal actuators, and machine learning