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

This dataset supports the doctoral thesis Data-Driven Ship Design: with Computational Fluid Dynamics Data-Driven Surrogate-based Hull-Form Optimization. It contains simulation data collected for the development, training, and evaluation of surrogate models aimed at predicting hydrodynamic resistance for parametrically varied ship hull forms. Hull geometries (based on cargo vessel and sailing hull topologies) were modified using parametric models and Free-Form Deformation (FFD) techniques and analyzed through high-fidelity Computational Fluid Dynamics (CFD) simulations under varying operational conditions (e.g., Froude numbers). The datasets include geometric parameters, hydrostatic and stability properties, operating conditions, and resistance outcomes. It was constructed to enable studies in surrogate modeling, shape optimization, and generalization across hull form design spaces, forming the empirical foundation of the thesis research.