Autonomous Underwater Vehicle Hull Design: Design Principles, Hydrodynamic Evaluation and Optimization

Abstract

Autonomous Underwater Vehicles have become essential platforms for ocean exploration, environmental monitoring, offshore engineering, and military applications. As the primary hydrodynamic component of an AUV, the hull significantly influences drag, endurance, maneuverability, and payload capacity. With advances in computational fluid dynamics (CFD), parametric modeling, optimization algorithms, and artificial intelligence, AUV hull design has evolved from empirical approaches to simulation-driven and intelligent optimization frameworks. This paper reviews recent progress in AUV hull design, focusing on representative AUV platforms, classical hull design theories, parametric geometric modeling methods, CFD-based hydrodynamic evaluation, and hull shape optimization techniques. Gradient-based optimization, evolutionary algorithms, surrogate-assisted optimization, and AI-driven methods are systematically compared, together with recent developments in global optimization, adaptive sampling, multi-fidelity surrogate modeling, and integrated optimization frameworks. Finally, future trends in AUV hull design are discussed, including the integration of digital twins, multidisciplinary design optimization, high-performance computing, and AI-assisted intelligent design. These emerging technologies are expected to improve the efficiency, accuracy, and automation of next-generation AUV hull design.