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