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