The DOI displayed above is for this specific version of this dataset, which is currently the latest. Newer versions may be published in the future. For a link that will always point to the latest version, please use
DOI: 10.4121/bead3ea0-2238-456a-826b-35fe7bcab2af

Walker, J. M.; Coraddu, A.; Oneto, L. (2025): Data underlying the conference proceeding: Decoupled Hull Resistance Prediction: A Computationally Aware and Physically Plausible Approach for Data-Driven Surrogates. Version 1. 4TU.ResearchData. dataset. https://doi.org/10.4121/bead3ea0-2238-456a-826b-35fe7bcab2af.v1

Categories

• Maritime Engineering
• Engineering

Keywords

Data-driven Surrogates Hull-Form Optimization Resistance Prediction Surrogate Modeling

Licence

CC BY-NC-ND 4.0

Interoperability

• RO-Crate Metadata

Collection

• Data Collection underlying the doctoral thesis: Data-Driven Ship Design: with Computational Fluid Dynamics Data-Driven Surrogate-based Hull-Form Optimization

Export as...

RefWorks BibTeX Reference Manager Endnote DataCite NLM DC CFF

by J. M. Walker , A. Coraddu , L. Oneto

This dataset supports the publication Decoupled Hull Resistance Prediction: A Computationally Aware and Physically Plausible Approach for Data-Driven Surrogates. It contains simulated hydrodynamic resistance data for systematically varied ship hull forms—created using Free-Form Deformation (FFD)—and generated via high-fidelity Computational Fluid Dynamics (CFD) simulations. The objective of the research is to develop and benchmark surrogate models that accurately predict total resistance across different hull geometries and operating conditions, while ensuring physical plausibility and generalization. The dataset includes geometric, hydrostatic, and stability parameters, operating conditions (e.g., Froude number), and the corresponding resistance outputs. It is intended to support studies in surrogate modeling, generalization under data scarcity, and shape–performance relationship analysis in marine hydrodynamics.

History

• 2025-07-04 first online, published, posted

Publisher

4TU.ResearchData

Format

MATLAB/.m MATLAB/.mat

References

• https://marine2025.cimne.com/event/contribution/2c9b6d53-b15b-11ef-9e02-000c29ddfc0c#:~:text=We%20propose%20a%20novel%20surrogate%20modeling%20approach%20that,input%2C%20enabling%20transfer%20across%20parametrizations%20and%20parent%20hulls.

Organizations

TU Delft, Faculty of Mechanical Engineering, Department of Marine and Transport Technology, Ship Design, Production and Operations;
University of Genoa, Department of Informatics, Bioengineering, Robotics and Systems Engineering