气动布局设计是飞行器总体设计的关键技术环节，需要高效生成多种方案并筛选出最优构型。然而，传统气动布局设计高度依赖专家经验，导致方案多样性受限，布局突破创新难；与此同时，主流的气动优化方法往往始于精细化设计初期，在确定的布局方案基础上进行局部优化，而不是关注概念设计阶段的多样性气动布局方案探索。近年来，生成式人工智能为飞行器布局设计提供了全新解决思路。本研究提出了基于生成对抗网络GAN（Generative Adversarial Network）的气动布局概念设计方法，以小型通航飞行器为研究对象，开展了气动布局生成式设计研究。首先，基于标准生成对抗网络及其改进模型，建立了面向飞行器气动布局设计的条件Wasserstein生成对抗网络CWGAN-GP（Conditional Wasserstein Generative Adversarial Network with Gradient Penalty），能实现给定气动性能下的布局方案快速生成。其次，面向低速巡航状态（马赫数0.2、2度迎角）的机翼设计，分析了不同生成式模型对参数空间的表征能力和给定条件下的布局生成能力，展示出CWGAN-GP在生成式设计方面的优势。最后，面向常规巡航状态（马赫数0.6、2度迎角）的通航飞行器气动布局生成式设计，以升力系数和升阻比两种关键气动参数为条件，生成了符合条件的多样性气动布局方案。通过突破条件参数采样范围，提出的方法能生成给定气动参数以外的不同气动外形，具有较强的泛化能力与外推性能。研究成果为下一代飞行器生成式气动设计提供了新方法。

Aerodynamic configuration design plays a critical role in the conceptual phase of aircraft development, where diverse design options must be efficiently generated and evaluated to identify optimal configurations. However, conventional aerodynamic design methodologies heavily rely on expert experience, which limits the exploration of innovative configurations and restricts the diversity of feasible solutions. Meanwhile, 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 2 degree), the representation capability of different generative models in the parameter space and their ability to generate configurations under given conditions were 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 2 degree), the CWGAN-GP model successfully generates a variety of aerodynamic configurations using the lift coefficient and lift-to-drag ratio as conditional parameters. Furthermore, 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.