# Constrained Bayesian Optimization for Automatic Underwater Vehicle Hull Design

## TL;DR

This paper presents a constrained Bayesian optimization framework integrated with CAD and CFD tools for automatic underwater vehicle hull design, demonstrating efficiency and feasibility on real-world cases.

## Contribution

It introduces a novel integration of domain-specific engineering tools with Bayesian optimization for efficient hull design automation.

## Key findings

- Successful automatic hull design optimization for real-world UUVs
- Effective handling of design constraints within Bayesian optimization
- Demonstrated sample efficiency and practical applicability

## Abstract

Automatic underwater vehicle hull Design optimization is a complex engineering process for generating a UUV hull with optimized properties on a given requirement. First, it involves the integration of involved computationally complex engineering simulation tools. Second, it needs integration of a sample efficient optimization framework with the integrated toolchain. To this end, we integrated the CAD tool called FreeCAD with CFD tool openFoam for automatic design evaluation. For optimization, we chose Bayesian optimization (BO), which is a well-known technique developed for optimizing time-consuming expensive engineering simulations and has proven to be very sample efficient in a variety of problems, including hyper-parameter tuning and experimental design. During the optimization process, we can handle infeasible design as constraints integrated into the optimization process. By integrating domain-specific toolchain with AI-based optimization, we executed the automatic design optimization of underwater vehicle hull design. For empirical evaluation, we took two different use cases of real-world underwater vehicle design to validate the execution of our tool.