Evaluation analysis of prediction methods for pressure drop of refrigerant-lubricant two-phase flow in compressor suction lines. (22nd August 2022)

• Record Type: Journal Article
• Authors: Gu, Bo; Zeng, Weijie; Tian, Zhen; Sha, Yuxiong; Wu, Pengzhan
• Is Part Of: Science and technology for the built environment. Volume 28:Number 8(2022)
• Page Start: 959
• Page End: 969
• Publication Date: 2022-08-22
• Subjects: Heating -- Periodicals; Ventilation -- Periodicals; Air conditioning -- Periodicals; Refrigeration and refrigerating machinery -- Periodicals; Indoor air quality -- Periodicals; Indoor air quality; Air conditioning; Heating; Refrigeration and refrigerating machinery; Ventilation; Periodicals; 697
• DOI: 10.1080/23744731.2022.2048596
• Languages: English
• ISSNs: 2374-474X

Abstract

Abstract : Evaluation analysis on the approaches for predicting two-phase pressure drop of refrigerant-lubricant oil mixture in compressor suction line is presented in this study. A total of 274 pressure drop data for refrigerant-oil mixture in suction line were adopted to develop a novel consolidated database. These data were collected from the authors' measurement and four published literatures. The working fluids included R410A, R134a and R32 with their relevant lubricant oils and the database covered the inner diameter, local quality, liquid and vapor Reynolds numbers with the ranges of 7.1–18.5 mm, 0.927–0.994, 0.32–19.1 and 2.59–20.4 × 10 4, respectively. Two analytical models namely annular flow model (AFM) and double-circle model (DCM) and four representative pressure drop correlations were introduced to conduct an assessment. The comparison result indicated that the analytical models tend to over predict the experimental data of annular flow pattern, while the DCM underestimates the data under stratified-wavy flow conditions. Among the four correlations, Hu correlation provided relatively satisfactory prediction and based on its format, an improved correlation was proposed. The proposed correlation incorporated the surface tension effect and it displayed the best agreement with the database, which yielded mean relative error of 18.16% and captured 85.40% of data within ±30% deviations.