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Reducing Traction Energy Consumption with a Decrease in the Weight of an All-Metal Gondola Car

Author

Listed:
  • Maryna Bulakh

    (Faculty of Mechanics and Technology, Rzeszow University of Technology, 37-450 Stalowa Wola, Poland)

  • Leszek Klich

    (Faculty of Mechanics and Technology, Rzeszow University of Technology, 37-450 Stalowa Wola, Poland)

  • Oleksandra Baranovska

    (Faculty of Political Science and Journalism, University of Maria Curie-Skłodowska, 20-612 Lublin, Poland)

  • Anastasiia Baida

    (Faculty of Law, Canon Law and Administration, The John Paul II Catholic University of Lublin, 20-950 Lublin, Poland)

  • Sergiy Myamlin

    (Department of Electrical Power Engineering, Electrical Engineering and Electromechanics, Ukrainian State University of Railway Transport, 61050 Kharkiv, Ukraine)

Abstract

The paper presented studies on reducing traction energy consumption with a decrease in the weight of an all-metal gondola car. Based on the proposed mathematical criterion, a new form of a blind floor was obtained, which makes it possible to reduce the weight of an all-metal gondola car. The aim of the paper was to reduce traction energy consumption with a decrease in the weight of an all-metal gondola car. For an all-metal gondola car with a modified form of a blind floor, strength studies were performed based on the finite element method. The equivalent stresses of the blind floor of an all-metal gondola car were 140.6 MPa, and the equivalent strains were 7.08 × 10 −4 . The margin of safety of the blind floor of an all-metal gondola car was 1.57. The weight of an all-metal gondola car with a modified form of a blind floor was reduced by 5.1% compared to a typical all-metal gondola car. For an all-metal gondola car with a modified form of a blind floor, a comparison was made of the traction energy consumption with typical all-metal gondola cars. Traction energy consumption with empty all-metal gondola cars were reduced by 2.5–3.1%; with loaded all-metal gondola cars by 2.4–7.3%, depending on the travel time interval.

Suggested Citation

  • Maryna Bulakh & Leszek Klich & Oleksandra Baranovska & Anastasiia Baida & Sergiy Myamlin, 2023. "Reducing Traction Energy Consumption with a Decrease in the Weight of an All-Metal Gondola Car," Energies, MDPI, vol. 16(18), pages 1-12, September.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6733-:d:1244364
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    References listed on IDEAS

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    1. Andrey Andryushchenko & Pavel Kolpahchyan & Alexander Zarifyan Jr., 2018. "Reduction Of Electric Locomotive'S Energy Consumption By Scalable Tractive Power Control," Transport Problems, Silesian University of Technology, Faculty of Transport, vol. 13(2), pages 103-110, June.
    2. Giulia Sandrini & Marco Gadola & Daniel Chindamo & Andrea Candela & Paolo Magri, 2023. "Exploring the Impact of Vehicle Lightweighting in Terms of Energy Consumption: Analysis and Simulation," Energies, MDPI, vol. 16(13), pages 1-31, July.
    3. Xiaowen Wang & Zhuang Xiao & Mo Chen & Pengfei Sun & Qingyuan Wang & Xiaoyun Feng, 2020. "Energy-Efficient Speed Profile Optimization and Sliding Mode Speed Tracking for Metros," Energies, MDPI, vol. 13(22), pages 1-29, November.
    4. Denys Baranovskyi & Maryna Bulakh & Adam Michajłyszyn & Sergey Myamlin & Leonty Muradian, 2023. "Determination of the Risk of Failures of Locomotive Diesel Engines in Maintenance," Energies, MDPI, vol. 16(13), pages 1-14, June.
    5. Shuai Su & Tao Tang & Yihui Wang, 2016. "Evaluation of Strategies to Reducing Traction Energy Consumption of Metro Systems Using an Optimal Train Control Simulation Model," Energies, MDPI, vol. 9(2), pages 1-19, February.
    6. He, Deqiang & Yang, Yanjie & Chen, Yanjun & Deng, Jianxin & Shan, Sheng & Liu, Jianren & Li, Xianwang, 2020. "An integrated optimization model of metro energy consumption based on regenerative energy and passenger transfer," Applied Energy, Elsevier, vol. 264(C).
    7. Fei Shang & Jingyuan Zhan & Yangzhou Chen, 2020. "Energy-Saving Train Regulation for Metro Lines Using Distributed Model Predictive Control," Energies, MDPI, vol. 13(20), pages 1-18, October.
    8. Agostinho Rocha & Armando Araújo & Adriano Carvalho & João Sepulveda, 2018. "A New Approach for Real Time Train Energy Efficiency Optimization," Energies, MDPI, vol. 11(10), pages 1-21, October.
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