Ship-D is a Dataset of 30,000 ship hulls for machine learning applications to ship design. Here is a link to the Dataset, Sample Code, and Documentation: https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/MMGAUS

Design Representation

The Ship-D Hulls are represented by a parametric design scheme that allows for a large diversity of hull shapes to be constructed. There are 45 parameters that define:

• Principal Dimensions (7 terms)

• Midship shape (4 terms)

• Bow shape (9 terms)
• Stern shape (11 terms)

• Bulb Geometries (14 Terms)

The parameters feed into a set of equations that define the surface of the hull. In addition to 45 parameters, there are also a set of 49 algebraic design constraints that ensure two conditions for the hulls:

1. The hull is watertight
2. The hull is not self-intersecting.

The algebraic design constraints are much faster at computing hull feasibility than constructing a mesh and checking these conditions by analyzing the mesh.

Performance Metrics

The dataset containts twelve measures of each hull's geometry. Many are measured at 10 different drafts along the depth of the hull.

1. Displaced Volume
2. Surface Area
3. Waterplane Area
4. Area Moments of Intertia in Roll Direction (