A general correlation for saturated flow boiling heat transfer in channels of various sizes and flow directions. (April 2017)

• Record Type: Journal Article
• Authors: Fang, Xiande; Wu, Qi; Yuan, Yuliang
• Is Part Of: International journal of heat and mass transfer. Volume 107(2017:Apr.)
• Page Start: 972
• Page End: 981
• Publication Date: 2017-04
• Subjects: Flow boiling -- Heat transfer coefficient -- Correlation -- Prediction method -- Evaporation; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Electronic journals; 621.4022
• DOI: 10.1016/j.ijheatmasstransfer.2016.10.125
• Languages: English
• ISSNs: 0017-9310

Abstract

Highlights: Builds two databases containing 24, 442 data points from 161 sources. Proposes accurate general model for saturated flow boiling heat transfer. New correlation predicts the two databases with MAD = 4.5%. New correlation is applicable to various channel sizes and flow directions. New correlation is applicable to both pre-dryout and post-dryout regimes. Abstract: A general correlation for heat transfer coefficients of saturated flow boiling is developed based on the first experimental database consisting of 17, 778 data points from 101 sources and 13 different fluids. The new correlation makes a significant breakthrough in the prediction accuracy for saturated flow boiling heat transfer, achieving a mean absolute deviation (MAD) of 4.5% against the database, with 68.1% and 89.7% of the data falling within ±5% and ±10% error bands, respectively. The new correlation is validated and compared with 45 existing correlations on the base of the second database of saturated flow boiling heat transfer. The second database contains 6664 data points from 60 sources and 18 different fluids and has no duplicate data from the first. The validation results show that the new correlation has an MAD of 4.4% against the second database, with 69.0% and 93.5% of the data within ±5% and ±10% error bands respectively, further confirming its superior prediction accuracy and reliability. The best existing correlation for the second database, meanwhile, has an MAD of 26.0%, with only 13.0% and 26.0% of the data falling within ±5% and ±10% error bands, respectively. The new correlation is applicable to various channel sizes, flow directions, and flow regimes.