基于RBF的替代模型在翼型稳健设计中的应用  

Application of Alternative Model Based on RBF in Airfoil Robust Design

作　　者：蔡文杰 黄俊[1] 毕国堂[1] 刘志勤[1] 黎茂锋[1] Cai Wen-jie;Huang Jun;Bi Guo-tang;Liu Zhi-qin;Li Mao-feng(Southwest University of Science and Technology,Mianyang,621000)

机构地区：[1]西南科技大学

出　　处：《导弹与航天运载技术》2019年第6期31-36,共6页Missiles and Space Vehicles

基　　金：国家自然科学基金面上项目（61672438）;四川省教育厅科研项目（18TD0021）;四川省军民融合研究院开放基金资助项目（2017SCII0219,2017SCII0220）

摘　　要：为了克服飞行器翼型受环境中不确定因素影响带来的性能恶化问题,需要对飞行器翼型进行相关的稳健性设计,使其对环境中的不确定因素不敏感。采用径向基(Radial Basis Function,RBF)插值和RBF神经网络拟合方法进行替代模型的建立,来获取一个稳健翼型的设计。通过与原始RAE2822翼型的阻力系数和Kriging模型方法获得的稳健翼型的阻力系数进行对比,证明了使用RBF建立的两种替代模型在翼型稳健设计应用中都具有良好的效果。In order to overcome the performance degradation caused by the uncertainty of the aircraft airfoil in the environment,it is necessary to design the aircraft airfoil with relevant robustness,so that it is not sensitive to uncertainties in the environment.The RBF radial basis interpolation and RBF neural network fitting method are used to establish the surrogate model to obtain a robust airfoil design.By comparing with the resistance coefficient of the original RAE2822 airfoil and the resistance coefficient of the robust airfoil obtained by the Kriging model method,it is proved that the two alternative models established by RBF have good effects in the airfoil robust design application.

关 键 词：径向基插值 RBF神经网络 替代模型 翼型稳健设计 

分 类 号：V211.3[航空宇航科学与技术—航空宇航推进理论与工程]