C-ShipGen: A Conditional Guided Diffusion Model for Parametric Ship Hull Design
This work addresses the need for efficient and constraint-compliant ship hull design in naval architecture, offering a data-driven approach that can reduce design cycle time, though it is incremental by applying diffusion models to a specific domain with added conditioning.
The paper tackles the problem of generating ship hull designs that meet specific constraints, such as principal dimensions, by proposing a conditional diffusion model that also leverages gradients from a resistance regression model to create low-resistance designs. In five test cases, the model produced diverse designs with total resistance reductions over 25% compared to an optimization algorithm, without requiring retraining.
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.