文献类型：期刊文献

英文题名：A novel method for calculating correlation of flow condensation pressure drop in micro-fin tube

作者：Shen, Daoming[1]; Gui, Chao[1]; Xia, Jinhong[1]; Xue, Songtao[2,3]

第一作者：申道明

机构：[1] College of Civil Engineering and Architecture, Xinxiang University, e-mail:, Henan, China; [2] Research institute of structural engineering and disaster reduction, Tongji University, Shanghai, China; [3] Dep of Architecture, Tohoku Institute of Technology, Sendai, Japan

第一机构：新乡学院土木工程与建筑学院

年份：2020

卷号：82

期号：2

起止页码：143-160

外文期刊名：UPB Scientific Bulletin, Series D: Mechanical Engineering

收录：EI(收录号：20202408806122);Scopus(收录号：2-s2.0-85086012387)

基金：This work was supported by the National Natural Science Foundation of China (No. 41877251)

语种：英文

外文关键词：Condensation - Cooling water - Errors - Fins (heat exchange) - Forecasting - Heat transfer - Pressure drop - Tubes (components) - Two phase flow

摘要：In this paper, the two-phase flow condensation heat transfer experiment of R134a, inside the micro-fin tube, was carried out on the test bench. The influence of working conditions and micro-fin tube structural parameters, on the pressure drop, was investigated, while correlations were also used, to predict the pressure drop inside the tube. The experimental results show that, the pressure drop is positively correlated with mass flux and fin helical angle, while negatively correlated with condensation temperature and cooling water Re. Furthermore, correlations of Cavallini et al., Haraguchi et al., and Pierre have a good predictive effect on the pressure drop, with an average prediction error of less than 17%, while Goto et al correlation overestimates the pressure drop. Based on the pressure drop experimental data of R134a, the function relationship, between v/l and Xtt, was fitted again, using the computing mechanism of Goto et al correlation. Next, new correlation of predicting the pressure drop, inside the tube, is proposed, while the statistic test shows: the prediction error of new correlation is within ±30% and the average prediction error is less than 10%. ? 2020, Politechnica University of Bucharest. All rights reserved.