Multiphase Science and Technology A Quarterly

ISSN for PRINT: 0276-1459

For Online Access

Best Paper Award Selection - Editorial Board Site

2000, Volume12

Issue 3&4

302 pages

Issue price - $280.00

S. Momoki
Nagasaki University, Nagasaki 852-8521, Japan

Avram Bar-Cohen
Laboratory of the Thermal Management of Electronics, Department of Mechanical Engineering, University of Minnesota, Minneapolis, MN 55455; and Department of Mechanical Engineering, University of Maryland, College park, MD 20742, USA

Dr. Arthur E. Bergles
Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, New York; University of Maryland, College Park, Maryland; Massachusetts Institute of Technology, Cambridge, Massachusetts, USA

While a large number of correlations have been proposed for the pressure drop encountered in gas-liquid two-phase flow in pipes, insufficient attention has been paid to the flow regimes associated with specific datasets used in developing these correlations. This may reduce the extrapolative accuracy of these correlations, and limit their utility in the design of future steam generation plants, air-conditioners with new alternative refrigerants, and other emerging applications of two-phase flow. In this study, five commonly used correlations for the frictional pressure drop component in gas-liquid two-phase flow in a horizontal smooth tube were examined for the experimental dataset sorted by the predicted flow regime using the Taitel et al method in order to explore the potential for using flow regime information to improve the predictive accuracy. The simple homogeneous model correlation was found to work very well for all of the entire annular data, with an average accuracy of ±27%, and the Chisholm correlation worked best for intermittent data, with agreement within ±24%, where the simple homogeneous correlation offers ±36%. These results may support the premise that consideration of some flow pattern indicator may prove effective in establishing better two-phase flow correlations.

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