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Improved correlation for predicting heat transfer coefficients during condensation inside smooth horizontal tubes.

Authors :
Blose, Sibongakonke C
Ewim, Daniel R E
Eloka-Eboka, Andrew C
Adelaja, Adekunle O
Source :
International Journal of Low Carbon Technologies. 2023, Vol. 18, p750-763. 14p.
Publication Year :
2023

Abstract

This study presents an improved correlation for predicting heat transfer coefficients for condensation inside smooth macro-tubes. Experimental data was obtained for various fluids, including R12, R1234yf, R1234ze, R134a, R22, R263fa, R245a, R32, R404a, R410a, and R600a. Tube diameters ranging from 3 to 14 mm, mass fluxes ranging from 50 to 1400 kg/m2s, and a vapor quality between 0 and 1 were used to cover a wide range of operating conditions. This data included 1683 data points from 50 sources. The comparison of the existing experimental data base with different correlations showed that the Cavallini et al. correlation had a mean absolute deviation of 19%, followed by those made by the correlations of Koyama and Yonemoto (24%), Dobson and Chato (24%), Huang et al. (24%), Shah (29%), and Haraguchi et al. (33%). Based on this comparison, an improved correlation modelled after the correlation of Cavallini et al. correlation was proposed. The accuracy and reliability of the proposed correlations were evaluated by comparing the predicted values with independent experimental data from the literature, wherein it was found that this new correlation predicted the databank with a mean absolute error of 17%. It was also found that this new proposed correlation provided better predictions of the heat transfer coefficient than existing correlations. The proposed correlation will improve the design and optimization of heat exchangers used in various industries, as well as provide valuable insights into condensation behavior inside smooth macro-tubes. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
17481317
Volume :
18
Database :
Academic Search Index
Journal :
International Journal of Low Carbon Technologies
Publication Type :
Academic Journal
Accession number :
175416520
Full Text :
https://doi.org/10.1093/ijlct/ctad052