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Experimental and numerical investigation of braking energy on thermal environment of underground subway station in China's northern severe cold regions

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  • Zhang, Huan
  • Zhu, Chunguang
  • Zheng, Wandong
  • You, Shijun
  • Ye, Tianzhen
  • Xue, Peng

Abstract

A large amount of heat and piston wind is generated during the train braking. The piston wind will be heated by the braking energy simultaneously, which has a significant influence on the air temperature and the flow field of subway stations and tunnels. The study conducted a train-induced experiment in Tai yuanjie (TYJ) station, and the air temperature and velocity data were recorded. Meanwhile, a train-induced numerical simulation was carried out for full-scaled model based on dynamic mesh and the theory of braking energy. The experimental data was compared with the simulation results to analyze the unsteady air temperature field and unsteady air flow field of the actual station. The results indicate that the braking energy of the train cannot be ignored in the simulation of the subway station in winter. However, the utilization of the braking energy is inefficient. To improve the utilization of the braking energy, this paper put forward one passive approach and three active approaches. The simulation results indicated that the active approaches could improve the heat utilization of braking energy significantly. Ventilation system could transport part of the piston wind to the station by fans so that it can prevent cold air entering the station.

Suggested Citation

  • Zhang, Huan & Zhu, Chunguang & Zheng, Wandong & You, Shijun & Ye, Tianzhen & Xue, Peng, 2016. "Experimental and numerical investigation of braking energy on thermal environment of underground subway station in China's northern severe cold regions," Energy, Elsevier, vol. 116(P1), pages 880-893.
    • Handle: RePEc:eee:energy:v:116:y:2016:i:p1:p:880-893
    DOI: 10.1016/j.energy.2016.10.029
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    References listed on IDEAS

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    1. Fei Lin & Shihui Liu & Zhihong Yang & Yingying Zhao & Zhongping Yang & Hu Sun, 2016. "Multi-Train Energy Saving for Maximum Usage of Regenerative Energy by Dwell Time Optimization in Urban Rail Transit Using Genetic Algorithm," Energies, MDPI, vol. 9(3), pages 1-21, March.
    2. Li, Xiang & Lo, Hong K., 2014. "An energy-efficient scheduling and speed control approach for metro rail operations," Transportation Research Part B: Methodological, Elsevier, vol. 64(C), pages 73-89.
    3. Li, Xiang & Lo, Hong K., 2014. "Energy minimization in dynamic train scheduling and control for metro rail operations," Transportation Research Part B: Methodological, Elsevier, vol. 70(C), pages 269-284.
    4. Huan Xia & Huaixin Chen & Zhongping Yang & Fei Lin & Bin Wang, 2015. "Optimal Energy Management, Location and Size for Stationary Energy Storage System in a Metro Line Based on Genetic Algorithm," Energies, MDPI, vol. 8(10), pages 1-23, October.
    5. Fukuyo, Kazuhiro, 2006. "Application of computational fluid dynamics and pedestrian-behavior simulations to the design of task-ambient air-conditioning systems of a subway station," Energy, Elsevier, vol. 31(5), pages 706-718.
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    Cited by:

    1. Shuang Meng & Dan Zhou & Zhe Wang, 2019. "Moving model analysis on the transient pressure and slipstream caused by a metro train passing through a tunnel," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-23, September.
    2. Yu, Yanzhe & You, Shijun & Zhang, Huan & Ye, Tianzhen & Wang, Yaran & Wei, Shen, 2021. "A review on available energy saving strategies for heating, ventilation and air conditioning in underground metro stations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 141(C).
    3. Ren, Zhili & Gao, Xiangkui & Wang, Tao & Xiao, Yimin & Zeng, Zhen & Chen, Long & Pang, Yantao & Ma, Yunlong & Xiong, Qian & Chen, Senlin & Ren, Yucheng, 2024. "Numerical study on thermal storage and exothermic characteristics of subway station fresh air shaft surrounding rock," Energy, Elsevier, vol. 293(C).
    4. Xiaonan Yan & Liangliang Tao & Junqin Peng & Yanhua Zeng & Yong Fang & Yun Bai, 2020. "Behavior of Piston Wind Induced by Braking Train in a Tunnel," Energies, MDPI, vol. 13(23), pages 1-19, December.
    5. Ji, Yongming & Wang, Wenqiang & Fan, Yujing & Hu, Songtao, 2023. "Coupling effect between tunnel lining heat exchanger and subway thermal environment," Renewable Energy, Elsevier, vol. 217(C).
    6. Liu, Minzhang & Zhu, Chunguang & Zhang, Huan & Zheng, Wandong & You, Shijun & Campana, Pietro Elia & Yan, Jinyue, 2019. "The environment and energy consumption of a subway tunnel by the influence of piston wind," Applied Energy, Elsevier, vol. 246(C), pages 11-23.
    7. He, Deqiang & Teng, Xiaoliang & Chen, Yanjun & Liu, Bin & Wang, Heliang & Li, Xianwang & Ma, Rui, 2022. "Energy saving in metro ventilation system based on multi-factor analysis and air characteristics of piston vent," Applied Energy, Elsevier, vol. 307(C).
    8. Li, Shiying & Xu, Jun & Pu, Xiaohui & Tao, Tao & Gao, Haonan & Mei, Xuesong, 2019. "Energy-harvesting variable/constant damping suspension system with motor based electromagnetic damper," Energy, Elsevier, vol. 189(C).
    9. Pan, Deng & Zhao, Liting & Luo, Qing & Zhang, Chuansheng & Chen, Zejun, 2018. "Study on the performance improvement of urban rail transit system," Energy, Elsevier, vol. 161(C), pages 1154-1171.
    10. Yanzhe Yu & Shijun You & Shen Wei & Huan Zhang & Tianzhen Ye & Yaran Wang & Yanling Na, 2022. "Exploring the Applicability of Building Energy Performance Certification Systems in Underground Stations in China," Sustainability, MDPI, vol. 14(6), pages 1-18, March.

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