IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v116y2016ip1p880-893.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544216314499
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.energy.2016.10.029?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
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. 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.
    2. 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.
    3. 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.
    4. 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.
    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.
    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. 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. 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.
    4. 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).
    5. 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.
    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. 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).
    8. 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.

    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. Albrecht, Amie & Howlett, Phil & Pudney, Peter & Vu, Xuan & Zhou, Peng, 2016. "The key principles of optimal train control—Part 1: Formulation of the model, strategies of optimal type, evolutionary lines, location of optimal switching points," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 482-508.
    2. Canca, David & Zarzo, Alejandro, 2017. "Design of energy-Efficient timetables in two-way railway rapid transit lines," Transportation Research Part B: Methodological, Elsevier, vol. 102(C), pages 142-161.
    3. Mo Chen & Zhuang Xiao & Pengfei Sun & Qingyuan Wang & Bo Jin & Xiaoyun Feng, 2019. "Energy-Efficient Driving Strategies for Multi-Train by Optimization and Update Speed Profiles Considering Transmission Losses of Regenerative Energy," Energies, MDPI, vol. 12(18), pages 1-25, September.
    4. Marcin Szott & Marcin Jarnut & Jacek Kaniewski & Łukasz Pilimon & Szymon Wermiński, 2021. "Fault-Tolerant Control in a Peak-Power Reduction System of a Traction Substation with Multi-String Battery Energy Storage System," Energies, MDPI, vol. 14(15), pages 1-23, July.
    5. Jianqiang Liu & Nan Zhao, 2017. "Research on Energy-Saving Operation Strategy for Multiple Trains on the Urban Subway Line," Energies, MDPI, vol. 10(12), pages 1-19, December.
    6. Zhou, Leishan & Tong, Lu (Carol) & Chen, Junhua & Tang, Jinjin & Zhou, Xuesong, 2017. "Joint optimization of high-speed train timetables and speed profiles: A unified modeling approach using space-time-speed grid networks," Transportation Research Part B: Methodological, Elsevier, vol. 97(C), pages 157-181.
    7. Yang, Xin & Xue, Qiuchi & Ding, Meiling & Wu, Jianjun & Gao, Ziyou, 2021. "Short-term prediction of passenger volume for urban rail systems: A deep learning approach based on smart-card data," International Journal of Production Economics, Elsevier, vol. 231(C).
    8. Ye, Hongbo & Liu, Ronghui, 2016. "A multiphase optimal control method for multi-train control and scheduling on railway lines," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 377-393.
    9. Gupta, Shuvomoy Das & Tobin, J. Kevin & Pavel, Lacra, 2016. "A two-step linear programming model for energy-efficient timetables in metro railway networks," Transportation Research Part B: Methodological, Elsevier, vol. 93(PA), pages 57-74.
    10. Albrecht, Amie & Howlett, Phil & Pudney, Peter & Vu, Xuan & Zhou, Peng, 2018. "The two-train separation problem on non-level track—driving strategies that minimize total required tractive energy subject to prescribed section clearance times," Transportation Research Part B: Methodological, Elsevier, vol. 111(C), pages 135-167.
    11. Jie Yang & Limin Jia & Shaofeng Lu & Yunxiao Fu & Ji Ge, 2016. "Energy-Efficient Speed Profile Approximation: An Optimal Switching Region-Based Approach with Adaptive Resolution," Energies, MDPI, vol. 9(10), pages 1-27, September.
    12. Yang, Xin & Chen, Anthony & Ning, Bin & Tang, Tao, 2017. "Bi-objective programming approach for solving the metro timetable optimization problem with dwell time uncertainty," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 97(C), pages 22-37.
    13. Scheepmaker, Gerben M. & Goverde, Rob M.P. & Kroon, Leo G., 2017. "Review of energy-efficient train control and timetabling," European Journal of Operational Research, Elsevier, vol. 257(2), pages 355-376.
    14. Wang, Pengling & Goverde, Rob M.P., 2019. "Multi-train trajectory optimization for energy-efficient timetabling," European Journal of Operational Research, Elsevier, vol. 272(2), pages 621-635.
    15. Yang, Xin & Chen, Anthony & Ning, Bin & Tang, Tao, 2016. "A stochastic model for the integrated optimization on metro timetable and speed profile with uncertain train mass," Transportation Research Part B: Methodological, Elsevier, vol. 91(C), pages 424-445.
    16. Gonzalo Sánchez-Contreras & Adrián Fernández-Rodríguez & Antonio Fernández-Cardador & Asunción P. Cucala, 2023. "A Two-Level Fuzzy Multi-Objective Design of ATO Driving Commands for Energy-Efficient Operation of Metropolitan Railway Lines," Sustainability, MDPI, vol. 15(12), pages 1-24, June.
    17. Zhou, Li & Yang, Xin & Wang, Huan & Wu, Jianjun & Chen, Lei & Yin, Haodong & Qu, Yunchao, 2020. "A robust train timetable optimization approach for reducing the number of waiting passengers in metro systems," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 558(C).
    18. Lee, Chung-Yee & Lee, Hau L. & Zhang, Jiheng, 2015. "The impact of slow ocean steaming on delivery reliability and fuel consumption," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 76(C), pages 176-190.
    19. Yang, Songpo & Liao, Feixiong & Wu, Jianjun & Timmermans, Harry J.P. & Sun, Huijun & Gao, Ziyou, 2020. "A bi-objective timetable optimization model incorporating energy allocation and passenger assignment in an energy-regenerative metro system," Transportation Research Part B: Methodological, Elsevier, vol. 133(C), pages 85-113.
    20. Guifu Du & Dongliang Zhang & Guoxin Li & Chonglin Wang & Jianhua Liu, 2016. "Evaluation of Rail Potential Based on Power Distribution in DC Traction Power Systems," Energies, MDPI, vol. 9(9), pages 1-20, September.

    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:eee:energy:v:116:y:2016:i:p1:p:880-893. 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: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/energy .

    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.