Heat transfer and frictional pressure drop during condensation in plate heat exchangers: Assessment of correlations and a new method. (June 2019)

• Record Type: Journal Article
• Authors: Tao, Xuan; Infante Ferreira, Carlos A.
• Is Part Of: International journal of heat and mass transfer. Volume 135(2019)
• Page Start: 996
• Page End: 1012
• Publication Date: 2019-06
• Subjects: Condensation heat transfer -- Two-phase frictional pressure drop -- Assessment -- Multi-variable regression analysis -- Plate heat exchangers; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Electronic journals; 621.4022
• DOI: 10.1016/j.ijheatmasstransfer.2019.01.132
• Languages: English
• ISSNs: 0017-9310

Abstract

Highlights: 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. Abstract: 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%.