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：摘要：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。船舶设计目前仍需要长达数年的周期且属于小批量生产，因此有望从这些技术进步中极大受益。通过开发能够学习多种船型设计的机器学习工具，可以识别并优化船舶设计中的权衡关系。然而，当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化船舶设计中的应用潜力。为填补这一空白，本文提出了一个包含三万条船体的大型数据集，每条船体均包含设计与功能性能信息，包括参数化、网格、点云与图像表征，以及三十二种不同工况下的水动力学阻力测量值。该数据集的设计兼顾了人工输入需求，并且适用于计算方法。此外，本文还从公开CAD资源库中选取了十二个船体样本，以展示所提出的参数化方法对现有船体的精确重构能力。我们开发了一个代理模型用于预测三十二种波浪阻力系数，并将其应用于遗传算法案例研究中，成功将船体总阻力降低百分之六十，同时保持船体横截面形状与平行中体长度不变。本研究为其他研究人员推动数据驱动型船舶设计提供了全面的数据集与应用范例。