摘要
Under complex flight conditions,such as obstacle avoidance and extreme sea state,wing-in-ground(WIG)effect aircraft need to ascend to higher altitudes,resulting in the disappearance of the ground effect.A design of high-speed WIG airfoil considering non-ground effect is carried out by a novel two-step inverse airfoil design method that combines conditional generative adversarial network(CGAN)and artificial neural network(ANN).The CGAN model is employed to generate a variety of airfoil designs that satisfy the desired lift-drag ratios in both ground effect and non-ground effect conditions.Subsequently,the ANN model is utilized to forecast aerodynamic parameters of the generated airfoils.The results indicate that the CGAN model contributes to a high accuracy rate for airfoil design and enables the creation of novel airfoil designs.Furthermore,it demonstrates high accuracy in predicting aerodynamic parameters of these airfoils due to the ANN model.This method eliminates the necessity for numerical simulations and experimental testing through the design procedure,showcasing notable efficiency.The analysis of airfoils generated by the CGAN model shows that airfoils exhibiting high lift-drag ratios under both flight conditions typically have cambers of among[0.08c,0.105c],with the positions of maximum camber occurring among[0.35c,0.5c]of the chord length,and the leading-edge radiuses of these airfoils primarily cluster among[0.008c,0.025c]
在避障、极端海况等复杂飞行工况下,地效飞行器需爬升至更高空域,导致地面效应消失。本文通过一种融合条件生成对抗网络(Conditional generative adversarial network,CGAN)与人工神经网络(Artificial neural network,ANN)的新型两步逆向翼型设计方法,开展了考虑非地效工况下的高速地效翼型设计研究。CGAN模型用于生成同时满足地效与非地效工况目标升阻比的多样化翼型设计,ANN模型用于预测生成翼型的气动参数。结果表明,CGAN模型能够生成满足条件的新型构型的翼型,ANN模型对翼型气动参数的预测具有较高精度,该方法在设计过程中无需依赖数值模拟与实验测试,具有显著的高效性。对CGAN生成翼型的分析表明,双工况下具有高升阻比的翼型弯度多分布于[0.08c,0.105c]区间,最大弯度位置位于弦长的[0.35c,0.5c]区间,且翼型前缘半径主要集中在[0.008c,0.025c]范围。
基金
supported by the Priority Academic Program Development of Jiangsu Higher Education Institutions,the Fundamental Research Funds for the Central Universities(No.ILA220101A23)
CARDC Fundamental and Frontier Technology Research Fund(No.PJD20200210)
the Aeronautical Science Foundation of China(No.20200023052002).
作者简介
The first author:WANG Chenlu,Ms.,received her M.S.degree in Aeronautical and Astronautical Science and Technology from Nanjing University of Aeronautics and Astronautics(NUAA).Her work focuses on efficient surrogate modeling and physics-informed algorithms for sustainable engineering solutions;The corresponding author:SUN Jianhong,Prof.,received his Ph.D.degree in Mechanical Engineering from Hong Kong University of Science and Technology,and has been a Professor and Doctoral Supervisor at NUAA since 2008.His research field is aircraft design,airworthiness technologies,and aviation safety systems,among others.E-mail address:jhsun@nuaa.edu.cn.
