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Queen Mary University of LondonQueen Mary University of London
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School of Engineering and Materials Science
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PhD Thesis: Condensation of single horizontal integral-fin tubes – effect of vapour and fin geometry


Year: 2006

Supervisor(s): Adrian Briggs

New experimental data are presented for forced-convection condensation of steam and ethylene glycol on a set of nine single, integral-fin tubes. The first set of five tubes had constant fin height and thickness of 1.6 and 0.25mm respectively while fin spacing was varied from 0.25 to 2.0mm. The second set of four tubes had constant fin spacing and thickness of 1.0 and 0.5 mm respectively while fin height was varied from 0.5 to 1.6 mm. All tubes had a fin root diameter of 12.7mm. A smooth tube of outside diameter 12.7 mm was also tested.

Tests were performed with downward flowing pure vapour for a range of velocities up to 62 m/s for steam and 222 m/s for ethylene glycol. Vapour-side, heat-transfer coefficients were obtained by subtracting the pre-determined coolant-side and wall resistances from the measured overall resistance. Optimum tubes were those with fin spacing of 0.25 and 0.5 mm for atmospheric pressure steam and low pressure ethylene glycol respectively, at all velocities. For low pressure steam, the optimum tube varied with vapour velocity. An increase in enhancement ratio with increasing vapour velocity for low pressure steam, which is the opposite trend to that found in most earlier experimental studies, was thought to be due to a reduction in condensate flooding between the fins due to vapour shear.

A semi-empirical model has been developed for forced-convection condensation on integral-fin tubes. The correlation includes the effects of gravity, surface tension and vapour shear on the vapour-side heat-transfer coefficient. Data from the present and previous works, consisting of 4 condensing fluids with 18 tube geometries and covering a wide range of heat fluxes and vapour velocities, were used to find the empirical constants in the model. The model was able to predict the majority of the 2800 data points to within ±25%.