Two-phase flow patterns, heat transfer and pressure drop characteristics of R600a during flow boiling inside a horizontal tube

•Flow patterns of R600a were studied and transition models were evaluated.•The effects of parameters on heat transfer and pressure drop were analyzed.•Prediction models of heat transfer coefficient and pressure drop were evaluated.•New prediction model for frictional pressure drop was developed.

A detailed experimental study was carried out to investigate the flow boiling heat transfer and pressure drop characteristics of R600a in a smooth horizontal tube with an inner diameter of 6 mm. The experiments were performed at conditions covering saturation pressures from 0.215 to 0.415 MPa, mass fluxes from 67 to 194 kg m−2 s−1 and heat fluxes from 10.6 to 75.0 kW m−2. Based on an high speed camera, four main flow regimes can be observed: plug flow, stratified-wavy flow, slug flow and annular flow. Intermittent to annular flow transition was detected and plotted on flow pattern maps. Comparisons with available transition lines in the literature have been made. Furthermore, the influences of saturation pressure, mass flux and heat flux on heat transfer coefficient were analyzed. The experimental data was compared with the calculated data of seven well-known correlations. The results indicated that, the correlation of Liu and Winterton showed the best agreement with a mean absolute relative deviation of 11.5%. For two-phase frictional pressure drop, mass flux had obviously positive effect on frictional pressure drop, while negative effect was found for saturation pressure. Eight correlations were evaluated and the correlation of Müller-Steinhagen and Heck gave the best fit to the experimental data with a mean absolute relative deviation of 32.9%. A new correlation of pressure drop which accounted for the influence of surface tension and gravitational force was developed on the basis of Müller-Steinhagen and Heck correlation and its mean absolute relative deviation was about 16.6% for the experimental data.