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Numerical prediction of frosting growth characteristics of microchannel louvered fin heat exchanger

Author

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  • Ling, Weihao
  • Wu, Jingtao
  • Li, Xuan
  • Ma, Jianjun
  • Ding, Yu
  • Li, Bingcheng
  • Zeng, Min

Abstract

Microchannel heat exchangers (MHXs), develop thin boundary layers owing to interruptive surfaces that break up and reform the boundary layers. However, once frosting occurs, the operation of the MHX is seriously affected. In order to clarify the growth and distribution of frost layer at the surface of the MHX, a three-dimensional frosting growth model of microchannel louvered fins is established and verified using the OpenFOAM software platform and self-programming method. The influences of boundary conditions, such as moist air inlet velocity, humidity ratio, and cold-wall surface temperature on the thickness of the local frost layer, pressure drop, and outlet temperature of the microchannel louvered fins are explored. For the fin in the middle of rear half, the lower cold-wall temperature (−12 °C compared with −8 °C) reduces the frost-clogging-channel time from 240 s to 160 s, while the higher humidity ratio (4 g kg−1 compared with 3.4 g kg−1) decreases the clogging time from 280 s to 200 s. Moreover, the high moist air inlet velocity (2.5 m s−1 compared with 1 m s−1) increases the pressure drop at 480 s from 276 Pa to 1011 Pa. This study provides guidance for predicting the local frosting of MHXs under moist conditions.

Suggested Citation

  • Ling, Weihao & Wu, Jingtao & Li, Xuan & Ma, Jianjun & Ding, Yu & Li, Bingcheng & Zeng, Min, 2023. "Numerical prediction of frosting growth characteristics of microchannel louvered fin heat exchanger," Energy, Elsevier, vol. 283(C).
    • Handle: RePEc:eee:energy:v:283:y:2023:i:c:s0360544223019138
    DOI: 10.1016/j.energy.2023.128519
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    References listed on IDEAS

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    1. Li, Zhaoyang & Wang, Wei & Sun, Yuying & Wang, Shiquan & Deng, Shiming & Lin, Yao, 2021. "Applying image recognition to frost built-up detection in air source heat pumps," Energy, Elsevier, vol. 233(C).
    2. Eom, Yong Hwan & Chung, Yoong & Park, Minsu & Hong, Sung Bin & Kim, Min Soo, 2021. "Deep learning-based prediction method on performance change of air source heat pump system under frosting conditions," Energy, Elsevier, vol. 228(C).
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    Cited by:

    1. Wang, Haitao & Wei, Jiahua & Guo, Chengzhou & Yang, Liu & Wang, Zuyuan, 2024. "Numerical investigation of the effects of different influencing factors on thermal performance of naturally ventilated roof," Energy, Elsevier, vol. 289(C).

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