Heat transfer and frictional pressure drop during condensation in plate heat exchangers: Assessment of correlations and a new method

• Published in: International journal of heat and mass transfer Vol. 135; pp. 996 - 1012
• Main Authors: Tao, Xuan, Infante Ferreira, Carlos A.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01-06-2019; Elsevier BV
• Subjects: Assessment; Condensation; Condensation heat transfer; Corrugated plates; Dimensional analysis; Dimensionless numbers; Fluxes; Heat exchangers; Heat transfer; Heat transfer coefficients; Multi-variable regression analysis; Plate heat exchangers; Pressure drop; Regression analysis; Two-phase frictional pressure drop; Condensation heat transfer; Plate heat exchangers; Multi-variable regression analysis; Two-phase frictional pressure drop; Assessment
• ISSN: 0017-9310; 1879-2189
• DOI: 10.1016/j.ijheatmasstransfer.2019.01.132

•Summary of an experimental database including 2376 heat transfer data and 1590 frictional pressure drop data.•Survey and classification of heat transfer and frictional pressure drop correlations.•Assessment of correlations with the experimental database.•Development of a new frictional pressure drop correlation, cross validation and sensitivity analysis.
The corrugation channels of plate heat exchangers enhance the heat transfer and complicate the prediction of heat transfer coefficients and frictional pressure drop. This paper reviews the heat transfer and frictional pressure drop correlations for condensation in plate heat exchangers, and classifies the correlations into basic forms. An experimental database is developed including the data of HFCs, hydrocarbons, HFOs and CO2. The mass fluxes are in the range of 2–150 kg·m−2·s−1. The chevron angles and hydraulic diameters are distributed in 25.7°–70° and 3.23–8.08 mm. The saturated temperatures are −34.4 to 72.1 °C, while the reduced pressures are from 0.03 to 0.49. Eight heat transfer correlations are assessed with the database. The correlation of Longo et al. [48] predicts the experimental data best, while the correlation of Kuo et al. [16] shows the second best performance. Six frictional pressure drop correlations are compared with the database. The prediction of frictional pressure drop is relatively poor, and a new correlation is developed using multi-variable regression analysis with non-dimensional numbers. This new correlation predicts 87.5% of the experimental data within ±50%.