空气动力学学报2019，Vol.37Issue(3)：470-479,10.DOI:10.7638/kqdlxxb-2019.0033
机器学习方法在气动特性建模中的应用
Applications of machine learning for aerodynamic characteristics modeling
摘要
Abstract
Mathematical modeling of aerodynamic data plays an important role in the performance evaluation of a flight vehicle.There are two kinds of aerodynamic characteristics modeling methods,i.e.,the rational modeling method based on physical mechanism and the"black-box"modeling methods.In this paper,three types of "black-box"modeling methods including the classification and regression tree method (CART),the shallow learning method, and the deep learning method are investigated.The CART method and three shallow learning methods including Kriging method,radial basis function (RBF)neural network method,and support vector machines(SVM)method are applied to the aerodynamic data modeling of rocket, the unsteady aerodynamic characteristics modeling for delta wing with large angle of attack,the aerodynamic heat data fusion of wind tunnel test and CFD.The advantage and shortage of these modeling methods are compared.A deep learning neural network model of airfoil’s aerodynamic coefficients is built.It considers the influence of airfoil picture and flow parameters such as Mach number,angle of attack on the prediction results at the same time through a "synthetic picture". It could significantly expand the applied range of the deep learning modeling method in aerodynamic research field.关键词
气动特性建模/机器学习/分类与回归树/浅层学习/深度学习Key words
aerodynamic characteristics modeling/ machine learning/ CART/ shallow learning/deep learning分类
信息技术与安全科学引用本文复制引用
HE Lei,QIAN Weiqi,WANG Qing,CHEN Hai,YANG Jun..机器学习方法在气动特性建模中的应用[J].空气动力学学报,2019,37(3):470-479,10.基金项目
国家自然科学基金(11802325) （11802325）
