C-ShipGen: A Conditional Guided Diffusion Model for Parametric Ship Hull Design

TL;DR

This paper introduces a conditional diffusion model for ship hull design that generates diverse, low-resistance hulls meeting specific constraints, significantly reducing design cycle time and improving customization.

Contribution

It presents a novel conditional diffusion approach that incorporates design constraints and resistance gradients, enabling efficient generation of optimized ship hulls without retraining.

Findings

Diffusion model produces hulls with over 25% resistance reduction.

Model generates diverse designs meeting specific constraints.

No retraining needed for different design cases.

Abstract

Ship design is a complex design process that may take a team of naval architects many years to complete. Improving the ship design process can lead to significant cost savings, while still delivering high-quality designs to customers. A new technology for ship hull design is diffusion models, a type of generative artificial intelligence. Prior work with diffusion models for ship hull design created high-quality ship hulls with reduced drag and larger displaced volumes. However, the work could not generate hulls that meet specific design constraints. This paper proposes a conditional diffusion model that generates hull designs given specific constraints, such as the desired principal dimensions of the hull. In addition, this diffusion model leverages the gradients from a total resistance regression model to create low-resistance designs. Five design test cases compared the diffusion model to a design optimization algorithm to create hull designs with low resistance. In all five test cases, the diffusion model was shown to create diverse designs with a total resistance less than the optimized hull, having resistance reductions over 25%. The diffusion model also generated these designs without retraining. This work can significantly reduce the design cycle time of ships by creating high-quality hulls that meet user requirements with a data-driven approach.