ACTA AERONAUTICAET ASTRONAUTICA SINICA >
A generative adversarial network based method for aerodynamic configuration design of general aviation aircraft
Received date: 2025-07-07
Revised date: 2025-07-28
Accepted date: 2025-10-24
Online published: 2025-11-03
Supported by
National Key Research and Development Program of China(2024YFB3310401);Open Foundation of National Key Laboratory of Aircraft Configuration Design(JBGS-202504);Natural Science Basic Research Program of Shaanxi(2025JC-YBQN-087);Fundamental Research Funds for the Central Universities(G2024KY05101)
Mainstream aerodynamic optimization approaches typically commence with the refinement of initial design stages， concentrating on local optimization within predefined configurations， rather than exploring the diversity of aerodynamic layout alternatives during the conceptual design phase. Recently， generative artificial intelligence has offered a novel solution paradigm for aircraft configuration design. This study presents a generative aerodynamic configuration design methodology based on Generative Adversarial Network （GAN）， with application to a small general aviation aircraft. First， the GAN and its variants are evaluated for their capability in representing parametric space and generating aerodynamic configurations. Based on this analysis， a Conditional Wasserstein GAN with Gradient Penalty （CWGAN-GP） is established for efficient generation of aerodynamic configurations. Second， for wing design under low-speed cruise conditions （Mach number 0.2， angle of attack of 2°）， the representation capability of different generative models in the parameter space and their ability to generate configurations under given conditions are analyzed， demonstrating the advantages of CWGAN-GP in generative design. Finally， for the conceptual aerodynamic configuration design of a general aviation aircraft under typical cruise conditions （Mach number 0.6， angle of attack of 2°）， the CWGAN-GP model successfully generates a variety of aerodynamic configurations using the lift coefficient and lift-to-drag ratio as conditional parameters. By extending beyond the training parameter ranges， the proposed method exhibits strong generalization and extrapolation capabilities， capable of producing different aerodynamic shapes outside the original condition space. These results indicate that the generative design methodology presented offers a promising new paradigm for next-generation aircraft aerodynamic configuration exploration and innovation.
Youzheng YU , Jiaqing KOU , Weiwei ZHANG . A generative adversarial network based method for aerodynamic configuration design of general aviation aircraft[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2026 , 47(6) : 132518 -132518 . DOI: 10.7527/S1000-6893.2025.32518
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