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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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
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Abstract	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.
AbstractList	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. 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/(m[sup.2]·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.
Audience	Academic
Author	Sun, Qian Wang, Jian Ma, Xinling Meng, Xiangrui
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Cites_doi	10.1016/j.ijthermalsci.2024.109025 10.1115/1.521444 10.1080/01457632.2015.1052665 10.1007/s40430-021-03299-5 10.1016/j.enconman.2006.03.023 10.1016/j.anucene.2024.110764 10.1016/0894-1777(96)00014-3 10.1016/j.ijheatmasstransfer.2011.08.028 10.1007/BF02945559 10.1016/j.cja.2021.07.027 10.1016/j.ijthermalsci.2009.10.001 10.1615/IHTC11.1530 10.1016/0017-9310(67)90083-X 10.3390/app12104807 10.1080/10407780490454296 10.1016/j.ijheatmasstransfer.2016.07.087 10.1115/1.1409267 10.1016/j.matdes.2021.110037 10.1080/10789669.2002.10391299 10.4324/9780203017869 10.1016/j.cep.2024.109784 10.1002/aic.690180342 10.1016/0017-9310(67)90182-2 10.1115/1.3246905
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SubjectTerms	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
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Title	Investigation on Pressure Drop Characteristics During Refrigerants Condensation Inside Internally Threaded Tubes
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