Construction on Aerodynamic Surrogate Model of Stratospheric Airship  

作　　者：QIN PENGFEI WANG XIAOLIANG 秦鹏飞;王晓亮(School of Aeronautics and Astronautics,Shanghai Jiao Tong University,Shanghai 200240,China)

机构地区：[1]School of Aeronautics and Astronautics,Shanghai Jiao Tong University,Shanghai 200240,China

出　　处：《Journal of Shanghai Jiaotong university(Science)》2022年第6期768-779,共12页上海交通大学学报（英文版）

基　　金：the National Natural Science Foundation of China(Nos.61733017 and 52175103);the Natural Science Foundation of Shanghai(No.18ZR1419000)。

摘　　要：Stratospheric airship can stay at an altitude of 20 km for a long time and carry various loads to achieve long-term stable applications.Conventional stratospheric airship configuration mainly includes a low-resistance streamline hull and inflatable"X"-layout fins that realize the self-stabilization.A fast aerodynamic predictive method is needed in the optimization design of airship configuration and the flight performance analysis.In this paper,a predictive surrogate model of aerodynamic parameters is constructed for the stratospheric airship with"X"fins based on the neural network.First,a geometric shape parameterized model,and a flow field parameterized model were established,and the aerodynamic coefficients of airships with different shapes used as the training and test samples were calculated based on computational fluid dynamics(SA turbulence model).The improved Bayesian regularized neural network was used as the surrogate model,and 20 types of airships with different shapes were used to test the effectiveness of network.It showed that the correlation coefficients of C_(x),C_(y),C_(z),C_(M,x),C_(M,y),C_(M,z) were 0.9287,0.9917,0.9919,0.9582,0.9861,0.9842,respectively.The aerodynamic coefficient distribution contour at different angles of attack and sideslip angles is used to verify the reliability of the method.The method can provide an effective way for a rapid estimation of aerodynamic coefficients in the airship design.

关 键 词：AIRSHIP aerodynamic coefficient neural network surrogate model 

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]