AI is increasingly used to accelerate engineering design by improving decision-making and shortening iteration cycles. Application to marine propeller design, however, remains challenging due to scarce training data and the lack of widely available pretrained models. We address this gap with a physics-based data generation pipeline and a generative-AI framework for direct performance-to-design generation tailored to marine propellers. First, we build a database of over 20,000 four- and five-bladed propeller geometries, each accompanied by simulated open-water performance curves. On top of this dataset, we develop a three-module design framework: (1) A Conditional Generation Model that proposes candidate geometries conditioned on design specifications such as target thrust, power, and diameter. (2) A Performance Prediction Model, implemented as a neural-network surrogate, that predicts thrust, torque, and efficiency in milliseconds, enabling rapid evaluation of generated designs. (3) A design refinement stage that applies evolutionary optimization to enforce practical constraints such as required thrust under power limits and bounds on blade-area ratio and thickness. Experimental results over a range of operating conditions show that the framework can generate hydrodynamically plausible propeller designs that match prescribed performance targets while substantially reducing design-iteration time relative to the traditional expert-guided refinement. Latent diffusion-based generator produces more diverse designs under the same conditions than the conditional variational autoencoder, suggesting a stronger capacity for design-space exploration with diffusion models. By coupling physics-based data synthesis with modular AI models, the proposed approach streamlines the propeller design cycle and reduces reliance on expensive high-fidelity simulations to final validation stages.

翻译：人工智能正越来越多地用于加速工程设计，通过改进决策和缩短迭代周期。然而，由于训练数据稀缺且缺乏广泛可用的预训练模型，其应用于船用螺旋桨设计仍具挑战性。我们通过基于物理的数据生成流程和专为船用螺旋桨设计的直接性能到设计生成的生成式AI框架来填补这一空白。首先，我们构建了一个包含超过20,000种四叶和五叶螺旋桨几何形状的数据库，每个几何形状都附带模拟的敞水性能曲线。在此数据集基础上，我们开发了一个三模块设计框架：（1）条件生成模型，根据目标推力、功率和直径等设计规范提出候选几何形状。（2）性能预测模型，实现为神经网络代理，可在毫秒内预测推力、扭矩和效率，从而快速评估生成的设计。（3）设计优化阶段，应用进化优化来强制执行实际约束，例如功率限制下的所需推力以及叶面积比和厚度的界限。在一系列运行条件下的实验结果表明，该框架能够生成流体力学上合理的螺旋桨设计，匹配规定的性能目标，同时相比传统的专家引导优化，大幅减少设计迭代时间。在相同条件下，基于潜在扩散的生成器比条件变分自编码器产生更多样化的设计，表明扩散模型具有更强的设计空间探索能力。通过将基于物理的数据合成与模块化AI模型相结合，所提出的方法简化了螺旋桨设计流程，并将对昂贵高保真模拟的依赖减少到最终的验证阶段。