期刊
INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER
卷 103, 期 -, 页码 701-714出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijheatmasstransfer.2016.07.074
关键词
Local heat transfer measurement; A new flow pattern based model; Subcooled and saturated flow boiling; Refrigerants; Multi-microchannel evaporators
资金
- China Scholarship Council
A comprehensive experimental campaign has been conducted to measure the local heat transfer coefficients during flow boiling of refrigerants in multi-microchannel evaporators. Two additional refrigerants (R245fa and R236fa) were tested in two silicon evaporators at three inlet subcoolings and at three outlet saturation temperatures. The test section backside temperatures were measured by a fine-resolution infrared (IR) camera providing a two-dimensional thermal map, which was used by solving the three-dimensional inverse heat conduction problem to obtain the local heat transfer coefficients on a pixel by-pixel basis. The experimental results revealed different trends along the flow direction. The decreasing trend (when existing) at the beginning of the channel was attributable to the single-phase thermal developing flow, then heat transfer increased from the onset of subcooled flow boiling up until the onset of saturated flow boiling, and afterwards it decreased again until entering the annular flow regime where it started to pick up and rose significantly. Combining our new data together with our recent experimental work of Huang et al. (2016), a new flow pattern based model has been proposed for local heat transfer prediction, starting from single-phase flow all the way through to annular flow. This new model also included a new local heat transfer method for the subcooled flow boiling region since no truly local sub cooled heat transfer prediction method can be found in the literature for microchannels. This new flow pattern based model predicted the total local heat transfer database (1,941,538 local points) well with a MAE of 14.2% and with 90.1% of the data predicted within 30%. It successfully tracks the experimental trends without any jumps in predictions when changing flow patterns. (C) 2016 Elsevier Ltd. All rights reserved.
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