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ISSN 打印: 1065-5131

ISSN 在线: 1563-5074

The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) IF: 2.3 To calculate the five year Impact Factor, citations are counted in 2017 to the previous five years and divided by the source items published in the previous five years. 2017 Journal Citation Reports (Clarivate Analytics, 2018) 5-Year IF: 1.8 The Immediacy Index is the average number of times an article is cited in the year it is published. The journal Immediacy Index indicates how quickly articles in a journal are cited. Immediacy Index: 0.2 The Eigenfactor score, developed by Jevin West and Carl Bergstrom at the University of Washington, is a rating of the total importance of a scientific journal. Journals are rated according to the number of incoming citations, with citations from highly ranked journals weighted to make a larger contribution to the eigenfactor than those from poorly ranked journals. Eigenfactor: 0.00037 The Journal Citation Indicator (JCI) is a single measurement of the field-normalized citation impact of journals in the Web of Science Core Collection across disciplines. The key words here are that the metric is normalized and cross-disciplinary. JCI: 0.6 SJR: 0.433 SNIP: 0.593 CiteScore™:: 4.3 H-Index: 35

Indexed in

Pool Boiling of Refrigerants R-134a and R-404A on Porous and Structured Tubes Part II. Heat Transfer Performance

卷 13, 册 1, 2006, pp. 85-97
DOI: 10.1615/JEnhHeatTransf.v13.i1.60
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摘要

This is the second part of a two-part experimental work that is intended to provide a further understanding of the physical phenomenon for refrigerants R-134a and R-404A boiling on structured and porous tubes. This paper provides measurement results of heat transfer performance on smooth and enhanced tubes. The results show that the relation of heat transfer performance to the heat flux can be well explained by the bubble dynamics as observed in Part I of this study.
The present study also supports the suggestion of Chen et al. [2002] that for tubes with surface pores or with surface pores connected by sub-surface tunnel, the high performance of structured surfaces is largely brought about by evaporation inside the tunnels. For a surface with big openings, most of heat load is transported by sensible heat, especially at a high flux.

对本文的引用
  1. Yang Chien-Yuh, Liu Chien-Fu, Effect of coating layer thickness for boiling heat transfer on micro porous coated surface in confined and unconfined spaces, Experimental Thermal and Fluid Science, 47, 2013. Crossref

  2. Liu Chien-Fu, Yang Chien-Yuh, Effect of space distance for boiling heat transfer on micro porous coated surface in confined space, Experimental Thermal and Fluid Science, 50, 2013. Crossref

  3. Zheng XiRu, Kim Min Soo, Park Chan Woo, A Study on the Sinterning of the Carbon Nanotube/Metal Composites for the Heat Transfer Enhancement, Composites Research, 26, 6, 2013. Crossref

  4. Kim Nae-Hyun, An Experimental Study on Pool Boiling Heat Transfer Enhancement of Structured Tubes Having Three-Dimensional Roughness, Korean Journal of Air-Conditioning and Refrigeration Engineering, 28, 5, 2016. Crossref

  5. Wang Yonghui, Zhang Jili, Ma Zhixian, Experimental study of pool boiling on a novel reentrant cavity tube surface with R134a, International Journal of Heat and Mass Transfer, 135, 2019. Crossref

  6. Wang Yonghui, Ma Zhixian, Zhang Jili, Pool boiling heat transfer on a reentrant cavity tube with R134a: Effects of saturation temperature under ice storage condition, International Journal of Heat and Mass Transfer, 166, 2021. Crossref

  7. Su Ching-Yuan, Yang Chien-Yuh, Jhang Bo-Wei, Hsieh Yu-Ling, Sin Yu-Yu, Huang Cheng-Chun, Pool Boiling Heat Transfer Enhanced by Fluorinated Graphene as Atomic Layered Modifiers, ACS Applied Materials & Interfaces, 12, 9, 2020. Crossref

  8. Mehdi Sadaf, Nannapaneni Saideep, Hwang Gisuk, Structural-material-operational performance relationship for pool boiling on enhanced surfaces using deep neural network model, International Journal of Heat and Mass Transfer, 198, 2022. Crossref

将发表的论文

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