• CN:11-2187/TH
  • ISSN:0577-6686

›› 2012, Vol. 48 ›› Issue (24): 134-140.

• 论文 • 上一篇    下一篇

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小通道内的冷凝换热模型分析

胡灿;李敏霞;马一太;付兴   

  1. 天津大学中低温热能高效利用教育部重点实验室
  • 发布日期:2012-12-20

Analysis of Condensation Heat Transfer Model in Small Channels

HU Can;LI Minxia;MA Yitai;FU Xing   

  1. Key Laboratory of Medium-Low Temperature Energy Efficient Utilization of Ministry of Education, Tianjin University
  • Published:2012-12-20

摘要: 目前国内外对于大通道内的冷凝换热研究较多,而对小通道内的冷凝换热研究较少,小通道内重力、切应力、表面张力的相互作用与大通道不同,导致小通道内的冷凝换热机理不同于大通道,因此大通道内的冷凝换热模型不能很好地预测小通道内的冷凝换热,而小通道内冷凝换热的研究对设计和优化紧凑型换热器具有重要意义。总结9种小通道内的冷凝换热预测模型,并根据11个独立研究机构的测试结果,收集6种工质(R134a,R32,R22,R123,R410A,R1234yf)的1 183个小通道内的冷凝换热试验数据点。比较各模型的预测结果和数据点发现,各预测模型并不是适用于小通道内所有工质和工况的预测,应根据工质和工况选择合适的模型。GARIMELLA的预测模型对R134a、R32、R22、R1234yf的数据点的预测误差很小,而KOYAMA的预测模型适用范围比较广,大部分工况下误差也是可接受的。

关键词: 冷凝换热, 小通道, 预测模型

Abstract: There are many researches about condensation heat transfer in macro-channels, but the studies of condensation heat transfer in small channels is limited available,and the interaction of gravity,shear stress and surface tension in small channels is different with macro-channels, result that the mechanism of condensation heat transfer in small channels is different with macro-channels, therefore condensation heat transfer prediction model of macro-channels can not predict heat transfer coefficients of small channels very well. While, the research of condensation heat transfer in small channels is significance for designing and optimizing compact heat exchanger. Nine condensation heat transfer prediction models of small channels are addressed, and a database is setup by collecting 1 183 condensation heat transfer data points of small channels which involves six types of refrigerants(R134a, R32, R22, R123, R410A, R1234yf )from 11 independent research groups. On basis of the comparison between the prediction results and the database,it is found that most of prediction models cannot capture the points of all fluids under various operating conditions in small channels, proper model should be chosen according to refrigerants and operating conditions. Garimella’s prediction model is the best one for R134a, R32, R22and R1234yf. Koyama’s model can almost fit for all data under most of the operating conditions within an acceptable deviation.

Key words: Condensation heat transfer, Prediction model, Small channels

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