Adaptive control pattern for real-time-visual-feedback flow separation control over airfoil with sparse processing particle image velocimetry and plasma actuator
摘要
This paper presents a novel adaptive control pattern (ACP) for real-time feedback control of flow separation over airfoils using sparse processing particle image velocimetry (SPPIV) and dielectric barrier discharge plasma actuators. The study addresses the challenge of suppressing separation in deep stall at low Reynolds numbers, where previous feedback-based methods often fail to maintain effective flow attachment. Unlike conventional feedback control methods such as single-step and multiple-step prediction, where control inputs are directly decided on the basis of state predictions, ACP determines the modulation frequency of the actuation. This is done according to threshold-based flow state detection, enabling the selection of effective actuation patterns for the estimated flow features. Experiments were conducted on a NACA0015 airfoil at an angle of attack of 18 degrees and a Reynolds number of