Reliable models for calculating the condensation heat transfer inside smooth helical tubes of different flow directions utilizing smart computational techniques
Abstract Condensers with helical tubes have received much attention in diverse industries. The optimal design of the mentioned equipment necessitates predictive tools for calculating the condensation heat transfer coefficient (HTC). However, literature models are applicable only to specific operatio...
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Nature Portfolio
2025-08-01
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| Online Access: | https://doi.org/10.1038/s41598-025-15240-0 |
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| author | Chou-Yi Hsu Nikunj Rachchh T. Ramachandran Aman Shankhyan A. Karthikeyan Ahmad Alkhayyat Prabhat Kumar Sahu Abhinav Kumar Satvik Vats F. Ranjbar |
| author_facet | Chou-Yi Hsu Nikunj Rachchh T. Ramachandran Aman Shankhyan A. Karthikeyan Ahmad Alkhayyat Prabhat Kumar Sahu Abhinav Kumar Satvik Vats F. Ranjbar |
| author_sort | Chou-Yi Hsu |
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| description | Abstract Condensers with helical tubes have received much attention in diverse industries. The optimal design of the mentioned equipment necessitates predictive tools for calculating the condensation heat transfer coefficient (HTC). However, literature models are applicable only to specific operational and geometrical conditions. The current study aims at developing reliable models for the condensation HTC within smooth helical tubes at all flow directions. Two machine learning (ML) techniques, namely Support Vector Machine (SVM) and Gaussian Process Method (GPM) were implemented to accomplish this target. To design and validate the models, 563 HTC data, encompassing a wide spectrum of conditions, were gathered from 10 experimental studies. While both SVM and GPM tools provided excellent predictions, the latter achieved the highest accuracy with mean absolute percentage error (MAPE) and R 2 value of 3.36% and 99%, respectively, for the testing dataset. Also, more than 96% of the HTC values calculated by the GPM model were situated within a ± 5% error margin. The accuracy of the literature correlations was also analyzed based on the collected data, and it was found that all of them showed MAPE values exceeding 25% from the experimental data. Moreover, unlike the previous models, the novel ML tools allowed the prediction of HTC for all flow directions with adequate precision. Also, they were capable of describing the physical variations of the condensation HTC versus operational factors. Finally, the dominant dimensionless parameters governing the two-phase Nusselt number in helical tubes were identified based on a sensitivity analysis. |
| format | Article |
| id | doaj-art-aed3ceb047dd43ddb69656d35d2eee36 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-aed3ceb047dd43ddb69656d35d2eee362025-08-24T11:22:46ZengNature PortfolioScientific Reports2045-23222025-08-0115111910.1038/s41598-025-15240-0Reliable models for calculating the condensation heat transfer inside smooth helical tubes of different flow directions utilizing smart computational techniquesChou-Yi Hsu0Nikunj Rachchh1T. Ramachandran2Aman Shankhyan3A. Karthikeyan4Ahmad Alkhayyat5Prabhat Kumar Sahu6Abhinav Kumar7Satvik Vats8F. Ranjbar9Thunderbird School of Global Management, Arizona State University Tempe CampusDepartment of Mechanical Engineering, Faculty of Engineering & Technology, Marwadi University Research Center, Marwadi UniversityDepartment of Mechanical Engineering, School of Engineering and Technology, JAIN (Deemed to be University)Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara UniversityDepartment of Mechanical Engineering, Sathyabama Institute of Science and TechnologyDepartment of Computers Techniques Engineering, College of Technical Engineering, The Islamic UniversityDepartment of Computer Science and Information Technology, Siksha ‘O’ Anusandhan (Deemed to be University)Department of Nuclear and Renewable Energy, Ural Federal University Named after the First President of Russia Boris YeltsinDepartment of Computer Science and Engineering, Graphic Era Hill UniversityDepartment of Mechanical Engineering, Islamic Azad University, Najafabad BranchAbstract Condensers with helical tubes have received much attention in diverse industries. The optimal design of the mentioned equipment necessitates predictive tools for calculating the condensation heat transfer coefficient (HTC). However, literature models are applicable only to specific operational and geometrical conditions. The current study aims at developing reliable models for the condensation HTC within smooth helical tubes at all flow directions. Two machine learning (ML) techniques, namely Support Vector Machine (SVM) and Gaussian Process Method (GPM) were implemented to accomplish this target. To design and validate the models, 563 HTC data, encompassing a wide spectrum of conditions, were gathered from 10 experimental studies. While both SVM and GPM tools provided excellent predictions, the latter achieved the highest accuracy with mean absolute percentage error (MAPE) and R 2 value of 3.36% and 99%, respectively, for the testing dataset. Also, more than 96% of the HTC values calculated by the GPM model were situated within a ± 5% error margin. The accuracy of the literature correlations was also analyzed based on the collected data, and it was found that all of them showed MAPE values exceeding 25% from the experimental data. Moreover, unlike the previous models, the novel ML tools allowed the prediction of HTC for all flow directions with adequate precision. Also, they were capable of describing the physical variations of the condensation HTC versus operational factors. Finally, the dominant dimensionless parameters governing the two-phase Nusselt number in helical tubes were identified based on a sensitivity analysis.https://doi.org/10.1038/s41598-025-15240-0Heat transfer coefficientCondensationHelical tubesMachine learningPrediction |
| spellingShingle | Chou-Yi Hsu Nikunj Rachchh T. Ramachandran Aman Shankhyan A. Karthikeyan Ahmad Alkhayyat Prabhat Kumar Sahu Abhinav Kumar Satvik Vats F. Ranjbar Reliable models for calculating the condensation heat transfer inside smooth helical tubes of different flow directions utilizing smart computational techniques Scientific Reports Heat transfer coefficient Condensation Helical tubes Machine learning Prediction |
| title | Reliable models for calculating the condensation heat transfer inside smooth helical tubes of different flow directions utilizing smart computational techniques |
| title_full | Reliable models for calculating the condensation heat transfer inside smooth helical tubes of different flow directions utilizing smart computational techniques |
| title_fullStr | Reliable models for calculating the condensation heat transfer inside smooth helical tubes of different flow directions utilizing smart computational techniques |
| title_full_unstemmed | Reliable models for calculating the condensation heat transfer inside smooth helical tubes of different flow directions utilizing smart computational techniques |
| title_short | Reliable models for calculating the condensation heat transfer inside smooth helical tubes of different flow directions utilizing smart computational techniques |
| title_sort | reliable models for calculating the condensation heat transfer inside smooth helical tubes of different flow directions utilizing smart computational techniques |
| topic | Heat transfer coefficient Condensation Helical tubes Machine learning Prediction |
| url | https://doi.org/10.1038/s41598-025-15240-0 |
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