Date of Award

7-2008

Document Type

Thesis

Degree Name

Master of Science (MS)

Legacy Department

Mechanical Engineering

Advisor

Ochterbeck, Jay M

Committee Member

Ma , Lin

Committee Member

Qiao , Rui

Abstract

Recent miniaturization of cooling systems has demanded better designing tools for compact heat exchangers. Forced two-phase flow through small channels is an effective way to better performance in a limited space. However, the characteristics of flow boiling in small channels differ from those in regular size channels, prompting a need for better understanding and development of predictive tools.
The characteristics of flow boiling in compact heat exchangers with parallel rectangular mini-channels of cross sections 0.50 x 0.50 mm, 0.75 x 0.75 mm, 1.00 x 1.00 mm, and 1.50 x 1.50 mm were experimentally investigated. Acetone was the fluid of choice, and the heat exchangers consisted of rectangular aluminum blocks with the mini-channels machined along its length. Various parameters and their influence over the two-phase heat transfer coefficient and two-phase frictional pressure drop were studied.
The two-phase heat transfer coefficient was found independent of quality for the range tested, except at low qualities. It was also found to be independent of mass flux, and mostly dependent of heat flux. These observations indicate a dominance of a mechanism similar to nucleate boiling. The correlation by Lee and Mudawar (2005) predicted the two-phase heat transfer coefficient with good agreement for the quality range of 0.05 to 0.50. Correlations developed for regular size channels generally overpredicted the data.
The two-phase frictional pressure drop was found to increase with mass flux and exit quality, as expected. The classical separated flow model by Lockhart and Martinelli (1949) predicted the trend of two-phase frictional pressure drop with good agreement. Other variations of the separated flow model also predicted the data well for particular cases, but all homogeneous pressure drop models under predicted the data.

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