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Energy Consumption Analysis of Helicopter Traction Device: A Modeling Method Considering Both Dynamic and Energy Consumption Characteristics of Mechanical Systems

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  • Qian Liu

    (School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Zhuxin Zhang

    (School of Vehicle and Energy, Yanshan University, Qinhuangdao 066004, China)

  • Tuo Jia

    (School of Vehicle and Energy, Yanshan University, Qinhuangdao 066004, China)

  • Lixin Wang

    (School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China)

  • Dingxuan Zhao

    (School of Mechanical Engineering, Yanshan University, Qinhuangdao 066004, China)

Abstract

Since modern times, the increase in shipborne equipment has brought tremendous pressure to the energy supply system. Establishing an accurate and reliable energy consumption model that reflects the dynamic characteristics of the system will provide an essential theoretical reference for energy efficiency optimization. This paper proposes a modeling method that considers both the dynamic characteristics and energy consumption characteristics of the system, based on the power bond-graph theory. Firstly, the transmission principle and energy transfer process of hydraulic and electric helicopter traction devices are analyzed. Then, the energy consumption is analyzed, and the state equation and energy equation of the system are established. Finally, the simulation tests are carried out. The results show that the proposed dynamic modeling method is reasonable and effective and can well reflect the dynamic characteristics and energy consumption characteristics of the system.

Suggested Citation

  • Qian Liu & Zhuxin Zhang & Tuo Jia & Lixin Wang & Dingxuan Zhao, 2022. "Energy Consumption Analysis of Helicopter Traction Device: A Modeling Method Considering Both Dynamic and Energy Consumption Characteristics of Mechanical Systems," Energies, MDPI, vol. 15(20), pages 1-20, October.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:20:p:7772-:d:948559
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    References listed on IDEAS

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    1. Somu, Nivethitha & M R, Gauthama Raman & Ramamritham, Krithi, 2020. "A hybrid model for building energy consumption forecasting using long short term memory networks," Applied Energy, Elsevier, vol. 261(C).
    2. Nuchturee, Chalermkiat & Li, Tie & Xia, Hongpu, 2020. "Energy efficiency of integrated electric propulsion for ships – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 134(C).
    3. Liu, Wei & Li, Li & Cai, Wei & Li, Congbo & Li, Lingling & Chen, Xingzheng & Sutherland, John W., 2020. "Dynamic characteristics and energy consumption modelling of machine tools based on bond graph theory," Energy, Elsevier, vol. 212(C).
    4. Borutzky, W. & Barnard, B. & Thoma, J.U., 2000. "Describing bond graph models of hydraulic components in Modelica," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 53(4), pages 381-387.
    5. Ke Song & Yimin Wang & Cancan An & Hongjie Xu & Yuhang Ding, 2021. "Design and Validation of Energy Management Strategy for Extended-Range Fuel Cell Electric Vehicle Using Bond Graph Method," Energies, MDPI, vol. 14(2), pages 1-31, January.
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    Cited by:

    1. Marcin Żugaj & Mohammed Edawdi & Grzegorz Iwański & Sebastian Topczewski & Przemysław Bibik & Piotr Fabiański, 2023. "An Unmanned Helicopter Energy Consumption Analysis," Energies, MDPI, vol. 16(4), pages 1-28, February.

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