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Machine learning has recently made significant strides in reducing designcycle time for complex products. Ship design, which currently involves yearslong cycles and small batch production, could greatly benefit from theseadvancements. By developing a machine learning tool for ship design that learnsfrom the design of many different types of ships, tradeoffs in ship designcould be identified and optimized. However, the lack of publicly available shipdesign datasets currently limits the potential for leveraging machine learningin generalized ship design. To address this gap, this paper presents a largedataset of thirty thousand ship hulls, each with design and functionalperformance information, including parameterization, mesh, point cloud, andimage representations, as well as thirty two hydrodynamic drag measures underdifferent operating conditions. The dataset is structured to allow human inputand is also designed for computational methods. Additionally, the paperintroduces a set of twelve ship hulls from publicly available CAD repositoriesto showcase the proposed parameterizations ability to accurately reconstructexisting hulls. A surrogate model was developed to predict the thirty two wavedrag coefficients, which was then implemented in a genetic algorithm case studyto reduce the total drag of a hull by sixty percent while maintaining the shapeof the hulls cross section and the length of the parallel midbody. Our workprovides a comprehensive dataset and application examples for other researchersto use in advancing data driven ship design.

Ship-D: Ship Hull Dataset for Design Optimization using Machine Learning