摘 要：针对飞行器设计初期面临的基于稀疏性能指标进行外形设计的需求，提出一种基于扩散Transformer（DiT）的多约束生成式设计方法。该方法以升阻比、弹径和弹翼根弦长等气动力与几何参数为输入，构建生成连续参数化外形的扩散去噪网络。在条件引入机制上，设计多频傅里叶特征嵌入器处理连续型标量，并将提取的条件特征与初始噪声序列在长度维度上进行拼接，直接参与自注意力计算。对多约束输入，设计多条件联合随机丢弃策略，在训练过程中对输入条件实施全局与独立的掩码操作。实验结果表明，条件特征拼接引入机制规避了传统时间嵌入相加方式中归一化条件对时间步特征的干扰；在联合随机丢弃策略的作用下，模型在推理阶段能够自适应接收任意数量与组合的条件输入，并输出满足对应设定指标约束的飞行器外形参数。该生成式设计模型具备在不完整约束输入下的概率推断能力，为多约束场景下的飞行器概念设计与气动布局快速探索提供了一种可行的新途径。

Abstract: To address the requirements of shape design based on sparse performance indicators in the early stages of aircraft conceptual design, a multi-constraint generative design method based on the Diffusion Trans-former (DiT) is proposed. Taking aerodynamic and geometric parameters such as lift-to-drag ratio, missile diameter, and wing root chord length as inputs, this method constructs a diffusion denoising network to generate continuous parameterized shapes. Regarding the condition introduction mechanism, a multi-frequency Fourier feature em-bedder is designed to process continuous scalars, and the extracted conditional features are concatenated with the initial noise sequence along the sequence length dimension to directly participate in the self-attention computation. For multi-constraint inputs, a multi-condition joint random dropout strategy is developed to apply global and inde-pendent masking operations to the input conditions during the training process. Experimental results demonstrate that the conditional feature concatenation mechanism avoids the interference of normalized conditions on timestep features inherent in traditional timestep embedding addition methods. Regulated by the joint random dropout strat-egy, the model can adaptively accept any number and combination of conditional inputs during the inference stage, outputting aircraft shape parameters that satisfy the corresponding set of constraints. This generative design model exhibits the capability of probabilistic inference under incomplete constraint inputs, providing a viable new ap-proach for aircraft conceptual design and the rapid exploration of aerodynamic layouts in multi-constraint scenarios.