Investigation on Pressure Drop Characteristics During Refrigerants Condensation Inside Internally Threaded Tubes

This study investigates the influence of geometric parameters of internally threaded tubes on heat transfer and resistance characteristics. Experimental analyses were conducted on pressure drop for 9.52 mm outer diameter tubes with various industry-standard geometric parameter combinations. Using R4...

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Bibliographic Details
Published in:Energies (Basel) Vol. 18; no. 10; p. 2662
Main Authors: Meng, Xiangrui, Wang, Jian, Sun, Qian, Ma, Xinling
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.05.2025
Subjects:
Accuracy
Analysis
condensation heat transfer
Copper
Design engineering
Efficiency
Friction
friction factor correlation
Heat exchangers
Heat transfer
internally threaded tube
Investigations
Machine learning
Optimization
R410A
Refrigerants
Research methodology
Reynolds number
Simulation
Turbulence models
ISSN:1996-1073, 1996-1073
Online Access:Get full text
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Summary:This study investigates the influence of geometric parameters of internally threaded tubes on heat transfer and resistance characteristics. Experimental analyses were conducted on pressure drop for 9.52 mm outer diameter tubes with various industry-standard geometric parameter combinations. Using R410A as the working fluid under turbulent flow conditions (Re = 20,000–60,000), experimental parameters included the following: mass velocity 50–600 kg/(m2·s), condensation temperature 45 ± 0.2 °C, and geometric ranges of thread height (e = 0.0001–0.0003 m), helix angle (α = 17–46°), crest angle (β = 16–53°), and number of ribs (Ns = 50–70). Results demonstrate that the newly developed correlation based on Webb and Ravigururajan friction factor models shows improved prediction accuracy for R410A condensation pressure drop in ribbed tubes. Model II achieved a mean absolute percentage error (MAPE) of 7.08%, with maximum and minimum errors of 27.66% and 0.76%, respectively. The standard deviation decreased from 0.0619 (Webb-based Model I) to 0.0362. Integration of SVR machine learning further enhanced tube selection efficiency through optimized correlation predictions.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en18102662