Ship-Dhttps://doi.org/10.7910/DVN/MMGAUSBagazinski, NoahAhmed, FaezHarvard Dataverse2024-07-112024-07-12T02:37:51ZShip-D is a dataset of parametric ship hulls to train machine learning models to design hull forms. The dataset contains 82,168 hull forms. 45 geometric parameters define each hull, allowing the large diversity of traditional mono-hull shapes and a larger design space. There are 5 subsets of hull designs in the Ship-D dataset: <br><br>
1) Constrained Randomized Set 1: 10,000 hulls, randomly sampled using the full ranges of all 45 parameters.<br>
2) Constrained Randomized Set 2: 10,000 hulls, randomly sampled, but do not contain bulbous bows or sterns.<br>
3) Constrained Randomized Set 3: 10,000 hulls, randomly sampled, but have strictly positive keel radii and zero deadrise angle. <br>
4) Diffusion Augmented Set 1: 41,752 hulls, sampled with a guided diffusion model to increase the displaced volume and reduce the hydrodynamic drag. (*Generated using ShipGen, citation below)<br>
5) Diffusion Augmented Set 2: 10,416 hulls, subsampled from Diffusion Augmented Set 1, but having randomized bulb parameters.<br><br>
For each hull design, the dataset contains the following:<br><br>
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Geometric measurements at ten different drafts<br>
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1) Displaced Volume<br>
2) Surface Area <br>
3) Waterplane Area <br>
4) Area Moments of Intertia in Roll Direction<br>
5) Area Moments of Intertia in Pitch Direction<br>
6) Longitudinal Center of Flotation (Center of Waterplane Area)<br>
7) Longitudinal Center of Buoyancy (Center of Displaced Volume)<br>
8) Vertical Center of Buoyancy (Center of Displaced Volume)<br>
9) Waterline length<br>
10) Height of draft measurement<br><br>
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Other Geometric Measures<br>
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1) Gaussian Curvature<br>
2) Largest Box that can be vertically lowered into each hull (MaxBox)<br>
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Wave resistance calculations using the Michell Integral<br>
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Thirty-two measurements across 4 drafts and 8 Froude numbers <br><br>
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Visual Data<br>
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1) Code to generate an STL mesh of each hull<br>
2) Blender Files that have code to generate Five Images of each hull (Front, Plan, Profile, Starboard Bow, and Port Stern)<br><br>
More details for the hull parameters, dataset, and papers can be found at
<a href=" https://decode.mit.edu/projects/ShipGen/"> https://decode.mit.edu/projects/ShipGen/</a> <br><br><br>
*Bagazinski, Noah J., and Faez Ahmed. 2023. "ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints" Journal of Marine Science and Engineering 11, no. 12: 2215.
<a href="https://doi.org/10.3390/jmse11122215"> https://doi.org/10.3390/jmse11122215</a>EngineeringBagazinski, Noah J., and Faez Ahmed. "Ship-D: Ship Hull Dataset for Design Optimization Using Machine Learning." In International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, vol. 87301, p. V03AT03A028. American Society of Mechanical Engineers, 2023.Bagazinski, Noah J., and Faez Ahmed. 2023. "ShipGen: A Diffusion Model for Parametric Ship Hull Generation with Multiple Objectives and Constraints" Journal of Marine Science and Engineering 11, no. 12: 2215. https://doi.org/10.3390/jmse111222152024-07-11Bagazinski, Noah2024-07-11CC0 1.0