Machine learning has recently made significant strides in reducing design cycle time for complex products. Ship design, which currently involves years long cycles and small batch production, could greatly benefit from these advancements. By developing a machine learning tool for ship design that learns from the design of many different types of ships, tradeoffs in ship design could be identified and optimized. However, the lack of publicly available ship design datasets currently limits the potential for leveraging machine learning in generalized ship design. To address this gap, this paper presents a large dataset of thirty thousand ship hulls, each with design and functional performance information, including parameterization, mesh, point cloud, and image representations, as well as thirty two hydrodynamic drag measures under different operating conditions. The dataset is structured to allow human input and is also designed for computational methods. Additionally, the paper introduces a set of twelve ship hulls from publicly available CAD repositories to showcase the proposed parameterizations ability to accurately reconstruct existing hulls. A surrogate model was developed to predict the thirty two wave drag coefficients, which was then implemented in a genetic algorithm case study to reduce the total drag of a hull by sixty percent while maintaining the shape of the hulls cross section and the length of the parallel midbody. Our work provides a comprehensive dataset and application examples for other researchers to use in advancing data driven ship design.

翻译：机器学习近期在缩短复杂产品设计周期方面取得了显著进展。船舶设计目前需要长达数年的周期且属于小批量生产，有望从这些技术进步中极大受益。通过开发一种能够从多种类型船舶设计中学习的机器学习工具，可以识别并优化船舶设计中的权衡关系。然而，目前缺乏公开可用的船舶设计数据集，这限制了机器学习在通用船舶设计中的应用潜力。为解决这一空白，本文提出包含三万条船体的大规模数据集，每条船体均包含设计与功能性能信息，包括参数化方案、网格模型、点云数据、图像表示，以及三十二种不同运行工况下的水动力阻力测量值。该数据集既支持人工输入，也为计算方法而设计。此外，本文引入了一套来自公开CAD资源库的十二艘船体，用以展示所提参数化方法对现有船体重构的准确性。研究开发了预测三十二种波浪阻力系数的代理模型，并将其应用于遗传算法案例研究，成功使船体总阻力降低60%，同时保持船体横截面形状与平行中体长度不变。本工作为其他研究者推进数据驱动型船舶设计提供了全面的数据集与应用实例。