基于机器学习的机翼气动载荷重构及传感器优化布置  

Aerodynamic Load Reconstruction of Wing and Optimal Sensor Layout Based on Machine Learning Technique

作　　者：余柏杨 王明振 王婷婷[3] 虞建 刘学军[1,2] 吕宏强 YU Baiyang;WANG Mingzhen;WANG Tingting;YU Jian;LIU Xuejun;LYU Hongqiang(School of Computer Science and Technology/School of Artificial Intelligence,Nanjing University of Aeronautics&Astronautics,Key Laboratory of Mode Analysis and Machine Intelligence,Ministry of Industry and Information Technology,Nanjing 211106,China;Collaborative Innovation Center for New Software Technology and Industrialization,Nanjing 210023,China;School of Aeronautics,Nanjing University of Aeronautics&Astronautics,Nanjing 210016,China;Key Laboratory of High-Speed Hydrodynamic Aviation Science and Technology,China Special Vehicle Research Institute,Jingmen 448035,China)

机构地区：[1]南京航空航天大学计算机科学与技术学院/人工智能学院,模式分析与机器智能工业和信息化部重点实验室,南京211106 [2]软件新技术与产业化协同创新中心,南京210023 [3]南京航空航天大学航空学院,南京210016 [4]中国特种飞行器研究所高速水动力航空科技重点实验室,荆门448035

出　　处：《南京航空航天大学学报》2023年第5期798-807,共10页Journal of Nanjing University of Aeronautics & Astronautics

基　　金：航空科学基金(2018ZA52002,2019ZA052011);空气动力学国家重点实验室基金(SKLA20180102);气动噪声控制重点实验室基金(ANCL20190103)。

摘　　要：风洞实验通过在机翼表面布置传感器来测量相应位置的气动载荷,由于传感器布置数量有限,难以直接得到整个机翼全息气动载荷分布。本文采用机器学习方法通过有限传感器数据重构机翼表面全息气动载荷,并提出了利用仿真数据对传感器进行优化布置的方法。从计算流体力学(Computational fluid dynamics,CFD)计算所得的机翼全息气动数据中选取有限位置数据模拟传感器实验数据,对比深度学习模型、高斯过程回归(Gaussian process regression,GPR)、支持向量回归(Support vector regression,SVR)与BP神经网络(Neural network,NN)对气动载荷的重构精度。通过评估由传感器数据重构的全息载荷精度对传感器布置方式进行优化设计。以M6机翼为例在给定的两个工况条件下验证本文所提出的方法。实验结果表明,GPR模型获得了最高气动载荷重构精度;给出了M6机翼在不同传感器总数下最优的截面数和单个截面布点数,最低传感器布置数下的最优布置方式,以及流场变化相对剧烈的前缘区域与展向截面的传感器布置方式。In wind tunnel experiments,sensors are normally placed on wing surfaces to measure the aerodynamic load at the corresponding positions.Due to the limited number of the placed sensors,it is difficult to directly obtain the holographic aerodynamic load distribution of the whole wing.In this paper,we use machine learning methods to reconstruct the holographic aerodynamic load on the wing surface from the limited sensor data,and propose a method to optimize the placement of sensors using the simulation data.We select limited position data from the wing holographic aerodynamic data calculated by computational fluid dynamics(CFD)to simulate the sensor experimental data,and compare the reconstruction accuracy of the deep learning model,Gaussian process regression(GPR),support vector regression(SVR)and BP neural network(NN)to the aerodynamic load.The sensor layout is optimized by evaluating the holographic load accuracy reconstructed by the sensor data.The M6 wing is taken as an example to verify the proposed method under two given working conditions.The experimental results show that the GPR model achieves the best accuracy of aerodynamic load reconstruction.The optimal section number and single section number of M6 wing under different total number of sensors,the optimal arrangement under the lowest number of sensors,and the arrangement of sensors in the leading edge area and spanwise section where the flow field changes greatly are all given.

关 键 词：计算流体力学 机翼气动载荷重构 压强系数 风洞实验 机器学习 

分 类 号：V19[航空宇航科学与技术—人机与环境工程]