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 ***,the ANN model is utilized to forecast aerodynamic parameters of the generated *** results indicate that the CGAN model contributes to a high accuracy rate for airfoil design and enables the creation of novel airfoil ***,it demonstrates high accuracy in predicting aerodynamic parameters of these airfoils due to the ANN *** method eliminates the necessity for numerical simulations and experimental testing through the design procedure,showcasing notable *** 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]