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Characteristics of thermal stratification and its effects on HVAC energy consumption for an atrium building in south China

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  • Dai, Baolian
  • Tong, Yan
  • Hu, Qi
  • Chen, Zheng

Abstract

More and more public buildings have functional zones without physical partitioning, and non-uniform thermal environments often exist inside it. Understanding this phenomenon and its influences on HVAC energy consumption is of great significance. Field measurements were carried out on a transport station in Nanjing, South China during daytimes, and thermal stratifications were found to be evident in the non-air-conditioned atrium with the linear gradients being range of 0.06–2.0 °C/m. An energy model coupled with CFD model is developed and verified by measured data. Further simulations are performed under cases of atrium height, glazed-roof material and season. The results show that the CFD simulated convective heat transfer coefficients of inner surfaces (CHTCIS) of the atrium vary according to scenario; linear temperature gradients differ by cases, and specifically there are two gradients in hot summer with the dimensionless interface heights being approximately 0.56–0.6; the HVAC loads of the air-conditioned zones are estimated to be 115–146 W/m2 when adopting thermal stratification and user-defined CHTCIS, and larger than that when no thermal stratification and built-in CHTCIS. The double low-e 6 mm plane glass contributes to the most favorable temperatures in both cold winter and hot summer, followed by the smallest HVAC loads.

Suggested Citation

  • Dai, Baolian & Tong, Yan & Hu, Qi & Chen, Zheng, 2022. "Characteristics of thermal stratification and its effects on HVAC energy consumption for an atrium building in south China," Energy, Elsevier, vol. 249(C).
    • Handle: RePEc:eee:energy:v:249:y:2022:i:c:s0360544222003280
    DOI: 10.1016/j.energy.2022.123425
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    References listed on IDEAS

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    1. Ochoa, Carlos E. & Aries, Myriam B.C. & van Loenen, Evert J. & Hensen, Jan L.M., 2012. "Considerations on design optimization criteria for windows providing low energy consumption and high visual comfort," Applied Energy, Elsevier, vol. 95(C), pages 238-245.
    2. Lee, J.W. & Jung, H.J. & Park, J.Y. & Lee, J.B. & Yoon, Y., 2013. "Optimization of building window system in Asian regions by analyzing solar heat gain and daylighting elements," Renewable Energy, Elsevier, vol. 50(C), pages 522-531.
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    Cited by:

    1. Gao, Yuan & Miyata, Shohei & Akashi, Yasunori, 2023. "How to improve the application potential of deep learning model in HVAC fault diagnosis: Based on pruning and interpretable deep learning method," Applied Energy, Elsevier, vol. 348(C).
    2. Huang, He & Wang, Honglei & Hu, Yu-Jie & Li, Chengjiang & Wang, Xiaolin, 2022. "Optimal plan for energy conservation and CO2 emissions reduction of public buildings considering users' behavior: Case of China," Energy, Elsevier, vol. 261(PA).

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