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System reliability study of geothermal energy walls in subway stations based on rapid thermal performance prediction model

Author

Listed:
  • Su, Xing
  • Chen, Chaoyang
  • Huang, Yixiang
  • Tian, Shaochen
  • Xia, Jihao
  • Liu, Jun
  • Yu, Yuanbo

Abstract

The energy conservation potential of relative systems in subway stations can be improved via the implementation of energy walls. However, current research mainly focuses on analyzing the factors affecting the energy walls' heat transfer performance, neglecting the reliability and performance assessment for ground source heat pump systems based on energy walls. This paper focuses on a subway station in hot-summer and cold-winter climate zone. Initially, both a resistance-capacity model and a 3D finite element model of energy walls are established. The resistance-capacity model is validated to be integrated with heat pump units to propose the rapid system performance prediction. Subsequently, the system thermal performance with the input of dynamic loads and conventional operational strategies is investigated. The results reveal that due to the heating and cooling load imbalance, the system inevitably experiences thermal imbalances and enters an inefficient operational state by the 6th year. Finally, to address this issue, a hybrid heat pump system with cooling towers is proposed, which demonstrates better thermal performance when used during non-cooling seasons. Another hybrid heat pump system for simultaneous cooling and heating is also proposed. These approaches both delay the onset of the system's inefficient operational state by 1–2 years, thereby improving the thermal performance.

Suggested Citation

  • Su, Xing & Chen, Chaoyang & Huang, Yixiang & Tian, Shaochen & Xia, Jihao & Liu, Jun & Yu, Yuanbo, 2024. "System reliability study of geothermal energy walls in subway stations based on rapid thermal performance prediction model," Energy, Elsevier, vol. 304(C).
  • Handle: RePEc:eee:energy:v:304:y:2024:i:c:s0360544224020164
    DOI: 10.1016/j.energy.2024.132242
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