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Heat Transfer Model for Condensation in Non-Circular Microchannels

[+] Author Affiliations
Akhil Agarwal

Shell Global Solutions US Inc., Houston, TX

Todd M. Bandhauer, Srinivas Garimella

Georgia Institute of Technology, Atlanta, GA

Paper No. ICNMM2007-30223, pp. 117-126; 10 pages
doi:10.1115/ICNMM2007-30223
From:
  • ASME 2007 5th International Conference on Nanochannels, Microchannels, and Minichannels
  • ASME 5th International Conference on Nanochannels, Microchannels, and Minichannels
  • Puebla, Mexico, June 18–20, 2007
  • Conference Sponsors: Nanotechnology Institute
  • ISBN: 0-7918-4272-X | eISBN: 0-7918-3800-5
  • Copyright © 2007 by ASME

abstract

A model for predicting heat transfer during condensation of refrigerant R134a in horizontal noncircular microchannels is presented. The thermal amplification technique developed and reported in earlier work by the authors is used to measure condensation heat transfer coefficients for six non-circular microchannels (0.424 < Dh < 0.839 mm) of different shapes over the mass flux range 150 < G < 750 kg/m2 -s. The channels included barrel-shaped, N-shaped, rectangular, square, and triangular extruded tubes, and a channel with a W-shaped corrugated insert that yielded triangular microchannels. Results from previous work by the authors on condensation flow mechanisms in microchannel geometries were used to interpret the results based on the applicable flow regimes. The effect of tube shape was also considered in deciding the applicable flow regime. A modified version of the annular flow based heat transfer model proposed recently by the authors for circular microchannels, with the required shear stress being calculated from a noncircular microchannel pressure drop model also reported earlier was found to best correlate the present data for square, rectangular and barrel-shaped microchannels. For the other microchannel shapes with sharp acute-angle corners, a mist flow based model from the literature on larger tubes was found to suffice for the prediction of the heat transfer data. These models predict the data significantly better than the other available correlations in the literature.

Copyright © 2007 by ASME

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