IDEAS home Printed from https://ideas.repec.org/a/hin/complx/5769794.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://downloads.hindawi.com/journals/8503/2017/5769794.pdf
    Download Restriction: no
    File URL: http://downloads.hindawi.com/journals/8503/2017/5769794.xml
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2017/5769794?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Zahra Sefidgar & Amir Ahmadi Joneidi & Ahmad Arabkoohsar, 2023. "A Comprehensive Review on Development and Applications of Cross-Flow Wind Turbines," Sustainability, MDPI, vol. 15(5), pages 1-39, March.
    2. Guo, Fen & Song, Baowei & Mao, Zhaoyong & Tian, Wenlong, 2020. "Experimental and numerical validation of the influence on Savonius turbine caused by rear deflector," Energy, Elsevier, vol. 196(C).
    3. Tian, Wenlong & Song, Baowei & Mao, Zhaoyong, 2020. "Numerical investigation of wind turbines and turbine arrays on highways," Renewable Energy, Elsevier, vol. 147(P1), pages 384-398.
    4. Zhaoyong Mao & Fuliang Zhao, 2017. "Structure Optimization of a Vibration Suppression Device for Underwater Moored Platforms Using CFD and Neural Network," Complexity, Hindawi, vol. 2017, pages 1-21, December.
    5. Rezaeiha, Abdolrahim & Montazeri, Hamid & Blocken, Bert, 2018. "Towards optimal aerodynamic design of vertical axis wind turbines: Impact of solidity and number of blades," Energy, Elsevier, vol. 165(PB), pages 1129-1148.
    6. Guo, Zijian & Liu, Tanghong & Xu, Kai & Wang, Junyan & Li, Wenhui & Chen, Zhengwei, 2020. "Parametric analysis and optimization of a simple wind turbine in high speed railway tunnels," Renewable Energy, Elsevier, vol. 161(C), pages 825-835.
    7. Miraglia, Marco & Romano, Donato & Camboni, Domenico & Inglese, Francesco & Oddo, Calogero Maria & Stefanini, Cesare, 2023. "Mechatronics-enabled harvesting of polarized wind kinetic energy through novel bio-mimetic swaying devices," Renewable Energy, Elsevier, vol. 211(C), pages 743-760.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hin:complx:5769794. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Mohamed Abdelhakeem (email available below). General contact details of provider: https://www.hindawi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.