A Bayesian algorithm coupled simulation-driven optimization framework for shape design of autonomous underwater vehicles

Yuhao Gao

doi:10.1007/s00158-026-04390-y

Return

A Bayesian algorithm coupled simulation-driven optimization framework for shape design of autonomous underwater vehicles

2026-08-17

PRE

DOI：10.1007/s00158-026-04390-y

Original

Original request for help

Save

Abstract

En 中文

The optimization methods based on computational fluid dynamics play an important role in the shape design of autonomous underwater vehicles (AUVs). However, existing frameworks face two major challenges: the iterative process entails remeshing operations and there is a primary reliance on metaheuristic algorithms. This paper describes a Bayesian algorithm coupled simulation-driven optimization framework that can be employed to assist in the shape design of AUVs. First, a computational domain mesh morphing control method, based on a linear transformation and radial basis function interpolation, is constructed to replace remeshing operations. Considering the expensive objective and cheap constraint characteristics of AUV shape optimization problems, a feasibility-weighted expected improvement Bayesian optimization (FWEI-BO) algorithm is proposed. Testing on benchmark problems shows that FWEI-BO has a stronger search capability and lower computational cost. Second, the framework is organized into an integrated and automated workflow. Through the seamless connection of morphing, simulation, and optimization, this workflow autonomously identifies the optimal solution. Finally, the developed framework is applied to the Myring-type AUV shape optimization, namely drag minimization subject to volume constraints. The results show that the drag of the AUV is reduced by 4.5% and the optimization algorithm identified after 44 CFD evaluations.

Keywords:

Autonomous underwater vehicles

Optimization design

Computational fluid dynamics

Mesh morphing