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Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network Method

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  • Wenlong Tian
  • Zhaoyong Mao
  • Fuliang Zhao
  • Zhicao Zhao

Abstract

This paper presents an optimization method for the design of the layout of an autonomous underwater vehicles (AUV) fleet to minimize the drag force. The layout of the AUV fleet is defined by two nondimensional parameters. Firstly, three-dimensional computational fluid dynamics (CFD) simulations are performed on the fleets with different layout parameters and detailed information on the hydrodynamic forces and flow structures around the AUVs is obtained. Then, based on the CFD data, a back-propagation neural network (BPNN) method is used to describe the relationship between the layout parameters and the drag of the fleet. Finally, a genetic algorithm (GA) is chosen to obtain the optimal layout parameters which correspond to the minimum drag. The optimization results show that the total drag of the AUV fleet can be reduced by 12% when the follower AUV is located directly behind the leader AUV and the drag of the follower AUV can be reduced by 66% when it is by the side of the leader AUV.

Suggested Citation

  • Wenlong Tian & Zhaoyong Mao & Fuliang Zhao & Zhicao Zhao, 2017. "Layout Optimization of Two Autonomous Underwater Vehicles for Drag Reduction with a Combined CFD and Neural Network Method," Complexity, Hindawi, vol. 2017, pages 1-15, December.
    • Handle: RePEc:hin:complx:5769794
    DOI: 10.1155/2017/5769794
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    References listed on IDEAS

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    1. Tian, Wenlong & Mao, Zhaoyong & An, Xinyu & Zhang, Baoshou & Wen, Haibing, 2017. "Numerical study of energy recovery from the wakes of moving vehicles on highways by using a vertical axis wind turbine," Energy, Elsevier, vol. 141(C), pages 715-728.
    2. Wenlong Tian & Zhaoyong Mao & Yukai Li, 2017. "Numerical Simulations of a VAWT in the Wake of a Moving Car," Energies, MDPI, vol. 10(4), pages 1-14, April.
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    Cited by:

    1. He, Yangying & Mou, Junmin & Chen, Linying & Zeng, Qingsong & Huang, Yamin & Chen, Pengfei & Zhang, Song, 2022. "Will sailing in formation reduce energy consumption? Numerical prediction of resistance for ships in different formation configurations," Applied Energy, Elsevier, vol. 312(C).

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