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Measuring the transient airflow rates of the infiltration through the doorway of the cold store by using a local air velocity linear fitting method

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  • Tian, Shen
  • Gao, Yuping
  • Shao, Shuangquan
  • Xu, Hongbo
  • Tian, Changqing

Abstract

The measurement of the infiltration airflow rates can support the calculation of the infiltration cooling load for the better understanding and optimizing the energy consumption of cold stores. However, the large temperature difference and the intense transient features make it difficult and complex to measure the airflow rates accurately. In this paper, a simple and practical method to measure the transient infiltration airflow rates is developed by using the local air velocity linear fitting. The proposed method is validated by the measurement results of the tracer gas decay method. It is concluded that the proposed method shows a good performance on the transient infiltration airflow rates measurement. The measurement errors are between ±10%. To enhance the application of this method, the layout of the measuring points of the air velocities are analyzed. The results show that, along the vertical layout direction, air velocity measuring points around the neutral level (where the cold and the warm air separate, about the middle height of the door) are not preferred when using this method. What’s more, the calculation of the infiltration cooling load by using this measuring method is also discussed.

Suggested Citation

  • Tian, Shen & Gao, Yuping & Shao, Shuangquan & Xu, Hongbo & Tian, Changqing, 2018. "Measuring the transient airflow rates of the infiltration through the doorway of the cold store by using a local air velocity linear fitting method," Applied Energy, Elsevier, vol. 227(C), pages 480-487.
    • Handle: RePEc:eee:appene:v:227:y:2018:i:c:p:480-487
    DOI: 10.1016/j.apenergy.2017.07.018
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    References listed on IDEAS

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    1. Reilly, Aidan & Kinnane, Oliver, 2017. "The impact of thermal mass on building energy consumption," Applied Energy, Elsevier, vol. 198(C), pages 108-121.
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    3. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    4. Capizzi, Giacomo & Sciuto, Grazia Lo & Cammarata, Giuliano & Cammarata, Massimiliano, 2017. "Thermal transients simulations of a building by a dynamic model based on thermal-electrical analogy: Evaluation and implementation issue," Applied Energy, Elsevier, vol. 199(C), pages 323-334.
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