Smart Skin Separation Control Exemplified for a Smooth Ramp

We perform the first AI-based smart skin experiment which utilizes distributed inputs and outputs (DIDO) to minimize flow separation over a smooth ramp. In this plant, flow separation may happen over a large range of locations. With smart skin deployed on the ramp surface, the flow separation can be progressively delayed via distributed actuation and sensing. We utilize multimodal actuators to delay flow separation. This novel actuator is composed of a height-adjustable vortex generator and an embedded mini-jet actuator. We massively deploy multimodal actuators (currently 30) on the ramp surface to delay flow separation that may happen at any point on the surface. Flow sensing is accomplished via distributed measurements of wall pressure (currently 56) from an array of high accuracy pressure sensors. With this setup, the smart skin is capable to achieve passive, active, and combined flow control strategies. The control efficiency can be optimized via artificial intelligence algorithms. In this work, we optimize the closed-loop active control for the smart skin. The control algorithm adopts the Gradient-enriched Machine Learning Control (gMLC) method proposed in [1]. This method combines exploration and exploitation, enabling a fast optimization in the high-dimensional space. Within 1,000 training periods, the optimized control strategy can effectively increase the pressure recovery on the ramp surface. Measurements of the controlled flow confirm significant reduction of the flow separation. These results will guide future development of flow control experiments with distributed sensing and actuation.