R14 flow condensation heat transfer performance: Measurements and modeling based on two-phase flow patterns

• Published in: International journal of heat and mass transfer Vol. 136; no. C; pp. 298 - 311
• Main Authors: Song, Qinglu, Chen, Gaofei, Xue, Hanwen, Zhao, Yanxing, Gong, Maoqiong
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.06.2019; Elsevier BV; Elsevier
• Subjects: Condensation; Correlation; Data points; Deviation; Flow condensation heat transfer; Flow distribution; Heat flux; Heat transfer; Horizontal tube; Mathematical models; Pressure effects; Saturation; Tetrafluoromethane; Tetrafluoromethane (R14); Two phase flow; Flow condensation heat transfer; Correlation; Horizontal tube; Tetrafluoromethane (R14)
• ISSN: 0017-9310; 1879-2189
• DOI: 10.1016/j.ijheatmasstransfer.2019.02.105

•Flow condensation heat transfer characteristics of R14 were studied experimentally.•The effects of mass flux, saturation pressure, heat flux and vapor quality were analyzed.•Fifteen well-known flow condensation correlations were compared with the experimental data of R14.•An improved flow pattern-based heat transfer model was developed.•This new model can well predict 1370 data points from nine published literature. An experimental investigation on flow condensation heat transfer characteristics of tetrafluoromethane (R14) in a horizontal smooth tube with inner diameter of 4 mm was carried out. Experiments were implemented at mass fluxes from 200 to 650 kg/(m2 s), saturation pressures from 1 to 3 MPa and heat fluxes from 8.3 to 28.2 kW/m2 over the entire range of vapor quality. The effects of saturation pressure, mass flux, heat flux and vapor quality were analyzed and discussed. In addition, R14 experimental data were compared with fifteen well-known flow condensation heat transfer correlations. Based on the comparison result and experimental data, an improved flow pattern-based heat transfer correlation was proposed and predicted R14 experimental data well with a mean absolute relative deviation of 6.26%. Finally, 1370 data points from nine published literature were adopted to evaluate the predictive ability of this new model. It achieves a satisfactory predicting result with a mean absolute relative deviation of 20.04% and 77.61% of the experimental data within the deviation bandwidth of ±30%.