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Comparison of models to predict air infiltration rate of buildings with different surrounding environments

  • Research Article
  • Indoor/Outdoor Airflow and Air Quality
  • Published:
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Abstract

The air infiltration rate of buildings strongly influences indoor environment and energy consumption. In this study, several traditional methods for determining the air infiltration rate were compared, and their accuracy in different scenarios was examined. Additionally, a method combining computational flow dynamics (CFD) with the Swami and Chandra (S-C) model was developed to predict the influence of the surrounding environment on the air infiltration rate. Two buildings in Dalian, China, were selected: one with a simple surrounding environment and the other with a complex surrounding environment; their air infiltration rates were measured. The test results were used to validate the accuracy of the air infiltration rate solution models in different urban environments. For the building with a simple environment, the difference between the simulation and experimental results was 0.86%–22.52%. For the building with a complex environment, this difference ranged from 17.42% to 159.28%. We found that most traditional models provide accurate results for buildings with simple surrounding and that the simulation results widely vary for buildings with complex surrounding. The results of the method of combining CFD with the S-C model were more accurate, and the relative error between the simulation and test results was 10.61%. The results indicate that the environment around the building should be fully considered when calculating the air infiltration rate. The results of this study can guide the application of methods of determining air infiltration rate.

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Abbreviations

ACH:

air change per hour (h−1)

A e :

effective infiltration area (m2)

C out :

outdoor CO2 concentration (ppm)

C t :

CO2 concentration at the end of the test (ppm)

\({c_{{t_0}}}\) :

CO2 concentration at the beginning of the test (ppm)

C A :

actual flow coefficient

C P :

pressure coefficient

C Q :

flow coefficient

C S :

thermal-pressure-induced infiltration coefficient

C W :

wind-induced infiltration coefficient

F schedule :

user-defined work schedule

H :

building height (m)

I design :

coefficient of air infiltration

n :

airflow index

p dyn :

dynamic pressure (Pa)

ΔP in-out :

internal/external pressure difference (Pa)

Q :

air infiltration rate (m3/s)

t :

end of CO2 concentration decay (h)

t 0 :

beginning of CO2 concentration decay (h)

ΔT :

indoor and outdoor temperature difference (°C)

V inflow :

inflow wind speed (m/s)

V ref :

velocity at reference height (m/s)

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Acknowledgements

This work was supported by the National Natural Science Foundation of China (51838007), and the Tsinghua-Toyota Joint Research Institute Inter-disciplinary Program.

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Authors and Affiliations

  1. Department of Building Science, Tsinghua University, Beijing, 100084, China

    Shu Zheng, Xiujiao Song & Xudong Yang

  2. School of Civil Engineering, Dalian University of Technology, Dalian, 116024, China

    Shu Zheng, Lin Duanmu & Yu Xue

  3. Shanxi Research Institute for Clean Energy, Tsinghua University, Taiyuan, 030032, China

    Xudong Yang

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  1. Shu Zheng
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  2. Xiujiao Song
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  3. Lin Duanmu
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  4. Yu Xue
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Contributions

Material preparation, data collection and analysis were performed by Shu Zheng, Xiujiao Song, Lin Duanmu, Yu Xue, and Xudong Yang. The first draft of the manuscript was written by Shu Zheng and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Yu Xue or Xudong Yang.

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The authors have no competing interests to declare that are relevant to the content of this article. Xudong Yang is the founding Editor-in-Chief of Building Simulation.

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This study does not contain any studies with human or animal subjects performed by any of the authors.

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Zheng, S., Song, X., Duanmu, L. et al. Comparison of models to predict air infiltration rate of buildings with different surrounding environments. Build. Simul. (2024). https://doi.org/10.1007/s12273-024-1118-5

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