Learning active flow control strategies of a swept wing by intelligent wind tunnel

An intelligent wind tunnel using an active learning approach automates flow control experiments to discover the aerodynamic impact of sweeping jet on a swept wing. A Gaussian process regression model is established to study the jet actuator's performance at various attack and flap deflection angles. By selectively focusing on the most informative experiments, the proposed framework was able to predict 3,721 wing conditions from just 55 experiments, significantly reducing the number of experiments required and leading to faster and costeffective predictions. The results show that the angle of attack and flap deflection angle are coupled to affect the effectiveness of the sweeping jet. Meanwhile, increasing the jet momentum coefficient can contribute to lift enhancement; a momentum coefficient of 3% can increase the lift coefficient by at most 0.28. Additionally, the improvement effects are more pronounced when actuators are placed closer to the wing root.