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Data-driven and machine learning-based optimization design for AUV shape
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Yun-tian LI1, 2, Wei-zheng CHEN1, Qing HAI1, Wei-ye CHEN1
Journal of Ship Mechanics | 2025, 29(12) : 1848 - 1861
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Journal of Ship Mechanics | 2025, 29(12): 1848-1861
• Hydrodynamics •
Data-driven and machine learning-based optimization design for AUV shape
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Yun-tian LI1, 2, Wei-zheng CHEN1, Qing HAI1, Wei-ye CHEN1
Affiliations
  • 1.China Ship Scientific Research Center, Wuxi 214082, China
  • 2.CSSC Marine Power Zhenjiang Co., Ltd., Zhenjiang 212005, China
Published: 2025-12-15 doi: 10.3969/j.issn.1007-7294.2025.12.003
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The performance of Autonomous Underwater Vehicles (AUVs) is significantly influenced by their shape design. This study presents a novel AUV shape optimization method that integrates data-driven approaches and machine learning technique to focus on the impact of the head and tail profiles, and the configuration of the fins and rudders on AUV performance. A parameterized hydrodynamic analysis workflow was developed to automatically generate the hydrodynamic parameters required during the optimization process. Based on this workflow, a data-driven framework was constructed for multi-objective optimization of AUV shapes, with the aims of minimizing drag and maximizing maneuverability. Surrogate models for the two optimization objectives above were built using Multilayer Perceptron (MLP) neural networks and ensemble learning methods respectively, and their performances were compared with traditional surrogate models. The optimization problem was solved using the Non-dominated Sorting Genetic Algorithm II. Comparative analysis of the initial and optimized AUV shapes demonstrates significant improvements in hydrodynamic performance, confirming the feasibility and effectiveness of the proposed method.

Autonomous Underwater Vehicle  /  shape design  /  CFD simulation  /  multi-objective optimization  /  machine learning  /  parametric design
Yun-tian LI, Wei-zheng CHEN, Qing HAI, Wei-ye CHEN. Data-driven and machine learning-based optimization design for AUV shape[J]. Journal of Ship Mechanics, 2025 , 29 (12) : 1848 -1861 . DOI: 10.3969/j.issn.1007-7294.2025.12.003
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doi: 10.3969/j.issn.1007-7294.2025.12.003
  • Receive Date：2025-07-04
  • Online Date：2026-07-07
  • Published：2025-12-15
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  • Received：2025-07-04
Affiliations
    1.China Ship Scientific Research Center, Wuxi 214082, China
    2.CSSC Marine Power Zhenjiang Co., Ltd., Zhenjiang 212005, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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