Open Access

ARTICLE

A Comparative Study on Hydrodynamic Optimization Approaches for AUV Design Using CFD

KL Vasudev¹, Manish Pandey², Jaan H. Pu^3,*

1 Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India
2 Department of Civil Engineering, Indian Institute of Technology Kharagpur, Kharagpur, 721302, India
3 Faculty of Engineering and Digital Technologies, University of Bradford, Bradford, BD7 1DP, UK

* Corresponding Author: Jaan H. Pu. Email:

Fluid Dynamics & Materials Processing 2025, 21(7), 1545-1569. https://doi.org/10.32604/fdmp.2025.065289

Received 09 March 2025; Accepted 26 June 2025; Issue published 31 July 2025

Abstract

This study presents a comparative analysis of optimisation strategies for designing hull shapes of Autonomous Underwater Vehicles (AUVs), paying special attention to drag, lift-to-drag ratio, and delivered power. A fully integrated optimisation framework is developed accordingly, combining a single-objective Genetic Algorithm (GA) for design parameter generation, Computer-Aided Geometric Design (CAGD) for the creation of hull geometries and associated fluid domains, and a Reynolds-Averaged Navier–Stokes (RANS) solver for evaluating hydrodynamic performance metrics. This unified approach eliminates manual intervention, enabling automated determination of optimal hull configurations. Three distinct optimisation problems are addressed using the proposed methodology. First, the drag minimisation of a reference afterbody geometry (A1) at zero angle of attack is performed under constraints of fixed length and internal volume for various flow velocities spanning the range from 0.5 to 15 m/s. Second, the lift-to-drag ratio of A1 is maximised at a 6° angle of attack, maintaining constant total length and internal volume. Third, delivered power is minimised for A1 at a 0° angle of attack. The comparative analysis of results from all three optimisation cases reveals hull shapes with practical design significance. Notably, the shape optimised for minimum delivered power outperforms the other two across a range of velocities. Specifically, it achieves reductions in required power by 7.6%, 7.8%, 10.2%, and 13.04% at velocities of 0.5, 1.0, 1.5, and 2.152 m/s, respectively.

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Keywords

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