期刊
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
卷 161, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijheatmasstransfer.2020.120241
关键词
Microchannel; Flow boiling; Two-phase pressure drop; R134a; R1234ze(E)
资金
- Science Foundation of China University of Petroleum, Beijing [2462018YJRC029]
- Mechanical and Power Engineering Innovation Research Platform of China University of Petroleum, Beijing [2462020XKJS01]
- National Natural Science Foundation of China [51576106]
- Science Foundation for Creative Research Groups of China [51621062]
- Opening fund of Shandong Provincial Key Laboratory of Oil & Gas Storage and Transportation Safety
- Fundamental Research Funds for the Central Universities
An experimental study was carried out on two-phase pressure drop of R134a and R1234ze(E) flow boiling in glass microchannel arrays containing 50 parallel microchannels. Each channel has a hydraulic diameter of 301.6 mu m, an aspect ratio of 2.46 and a length of 50 mm. Experimental conditions, including saturation temperatures of 26.7 degrees C, 29.1 degrees C and 31.3 degrees C, and mass fluxes of 50-250 kg/(m(2) s), were taken into account. The effect of refrigerant properties, mass flux and saturation temperature on the flow boiling friction pressure drop were investigated. The two-phase friction pressure drop increases with increasing mass flux and decreasing saturation temperature. Specially, the effect of heating methods, including single-side heating and double-side heating, were experimentally evaluated. The two-phase friction pressure drop in microchannel arrays heated from both sides is higher than that with single-side heating. The experimental data was compared with two-phase friction pressure drop empirical correlations in the literature. The correlation provided by Zhang and Webb gives the best prediction of the experimental data with an average deviation of 34.27% for R134a and 16.88% for R1234ze(E). A new empirical correlation was provided based on the current experimental data, which could predict the current data with 94.5% prediction errors lower than 20%. (C) 2020 Elsevier Ltd. All rights reserved.
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