未来飞行器复杂多变的设计需求对气动外形参数化方法的灵活性、有效性提出了更高的要求。针对传统飞行器几何参数化方法气动外形约束过强导致设计优化空间受限及复杂外形设计变量冗余导致“维度灾难”等问题，提出了一种基于生成式模型的三维飞行器气动外形泛化表征方法。该方法通过提取气动外形剖面图像几何特征，约束全局外形与局部点云扩散模型，实现三维外形点云快速生成，以满足三维复杂外形降维表达和灵活设计的工程需求。该方法通过3个核心模块实现从剖面图像到点云外形，再到表面网格的全流程多视角泛化表征：基于变分自编码器与残差网络的剖面图像几何特征提取、降维重构模型；融合点云变分自编码器网络、条件可控生成扩散模型的三维外形点云生成模型；采用可微泊松求解器的表面网格生成模型。以三维翼身融合乘波体外形为例，通过控制隐变量空间中的泛化外形参数，实现了单个外形剖面图像生成耗时3 s，三维气动外形网格文件生成耗时80 s。所重构外形与初始外形的平均几何误差控制在1 mm以内，实现了飞行器复杂外形的高效泛化表征。此外，直接基于生成的表面网格导入计算流体力学软件，在不同攻角条件下进行了流场与气动特性的预示。相关研究成果为复杂飞行器的优化设计提供了一种智能且灵活的外形泛化表征手段。

The increasingly complex and dynamic design requirements of future hypersonic vehicle have heightened the call for enhanced flexibility and efficacy in aerodynamic shape parameterization methods. This research addresses the constraints imposed by conventional geometric parameterization methods in hypersonic vehicle design， which tend to confine aerodynamic shapes excessively， limiting design optimization and culminating in the “dimensional disaster”. To tackle these challenges， the study introduces a novel approach centered on generative models to generalize the depiction of three-dimensional hypersonic vehicle aerodynamic profiles. This innovative method entails extracting geometric attributes from aerodynamic profile images and imposing constraints on global and local point cloud diffusion models to facilitate the creation of three-dimensional point cloud representations， catering to the intricate demands of three-dimensional shape reduction and adaptable design in engineering contexts. The method achieves a comprehensive multi-view generalized representation pipeline from cross-sectional images to point cloud geometry and surface meshes through three core modules： a geometric feature extraction and dimensionality reduction reconstruction model for cross-sectional images based on Variational Autoencoder （VAE） and residual networks； a 3D geometric point cloud generation model integrating point cloud variational autoencoder networks with conditionally controllable generative diffusion models； a surface mesh reconstruction model employing a differentiable Poisson solver. In the context of the three-dimensional blended wing-body configuration， manipulating generalized shape parameters within latent variable spaces enables rapid generation of single profile images in 3 s and three-dimensional aerodynamic mesh files in 80 s. The reconstructed shapes maintain an average geometric error within 1 mm compared to the initial design， demonstrating efficient and accurate generalization of complex aircraft shapes. Furthermore， the generated surface meshes were directly imported into Computational Fluid Dynamics （CFD） software to predict flow fields and aerodynamic characteristics under different angles of attack. The results highlight the potential of this method as an intelligent and flexible aerodynamic shape generalization tool for optimizing complex aircraft designs.