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

作　　者：Yusi Wu Tingwei Ji Xinyu Lv Changdong Zheng Zhixian Ye Fangfang Xie 

机构地区：[1]School of Aeronautics and Astronautics,Zhejiang University,Hangzhou 310027,China [2]Fair Friend Institute of Intelligent Manufacturing,Hangzhou Vocational&Technical College,Hangzhou 310018,China

出　　处：《Theoretical & Applied Mechanics Letters》2024年第5期386-394,共9页力学快报（英文版）

基　　金：supported by the National Natural Science Foundation of China (Grant No.92271107)。

摘　　要：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 3721 wing conditions from just 55experiments, significantly reducing the number of experiments required and leading to faster and cost-effective 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.

关 键 词：Active flow control Sweeping jet Active learning Gaussian process regression 

分 类 号：O355[理学—流体力学] TP273[理学—力学]