Friction measurement of aircraft wing based on optimized FlowNet2.0

作     者：Hongjiang QIAN Zhiyong HUANG Jian WANG Yeting XU Xiucheng DONG Jiebin SHEN Hongjiang QIAN;Zhiyong HUANG;Jian WANG;Yeting XU;Xiucheng DONG;Jiebin SHEN

作者机构：School of Aeronautics and AstronauticsSichuan UniversityChengdu 610065China School of Electrical Engineering and Electronic InformationXihua UniversityChengdu 610039China 

出 版 物：《Chinese Journal of Aeronautics》 (中国航空学报（英文版）)

年 卷 期：2023年第36卷第11期

页      面：91-101页

核心收录：

学科分类：080103[工学-流体力学] 08[工学] 080104[工学-工程力学] 0801[工学-力学（可授工学、理学学位）] 

基　　金：This work was co-supported by the National Natural Science Foundation of China(No.11872259) the National Science and Technology Major Project of China(No.J2019-IV-0010-0078) 

主　　题：Optical flows Deep learning Friction Wind tunnels Flow velocity Measurement 

摘      要：Few studies have applied the deep optical flows model to global friction measurements of aircraft *** study used an optimized FlowNet2.0 model to measure friction of wing based on fluorescent oil film,which achieved the first integration of deep learning and friction *** input images of the traditional FlowNet2.0 model were extended to multiple images so that more flow features and details could be with *** is the specific part of optimization that will also further improve the measurement accuracy of *** experimental results show that the optimized FlowNet2.0 model reduces the Mean Absolute Percentage Error(MAPE)error by 8.51%and increases Root Mean Square Error(RMSE)by only 0.0138 when compared to the hybrid optical flow method,which indicate that the optimized FlowNet2.0 model has great potential for friction *** in continuous transonic wind tunnel tests demonstrate that FlowNet2.0 can calculate clearer and more accurate flow velocity than hybrid optical flow,and the solved friction magnitude distribution is consistent with the actual flow,which will be of great practical application in wind tunnel engineering.