Evidence map›Paper›PMID 40830180›Full record

ArticleScientific reports2025

Reliable models for calculating the condensation heat transfer inside smooth helical tubes of different flow directions utilizing smart computational techniques.

Chou-Yi Hsu, Nikunj Rachchh, T Ramachandran, Aman Shankhyan, A Karthikeyan, Ahmad Alkhayyat, Prabhat Kumar Sahu, Abhinav Kumar, Satvik Vats, F Ranjbar

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Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

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10 authors.

Chou-Yi HsuThunderbird School of Global Management, Arizona State University Tempe Campus, Phoenix, AZ, 85004, USA.
Nikunj RachchhDepartment of Mechanical Engineering, Faculty of Engineering & Technology, Marwadi University Research Center, Marwadi University, Rajkot, Gujarat, 60003, India.
T RamachandranDepartment of Mechanical Engineering, School of Engineering and Technology, JAIN (Deemed to be University), Bangalore, Karnataka, India.
Aman ShankhyanCentre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India.
A KarthikeyanDepartment of Mechanical Engineering, Sathyabama Institute of Science and Technology, Chennai, Tamil Nadu, India.
Ahmad AlkhayyatDepartment of Computers Techniques Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq.
Prabhat Kumar SahuDepartment of Computer Science and Information Technology, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, 751030, India.
Abhinav KumarDepartment of Nuclear and Renewable Energy, Ural Federal University Named after the First President of Russia Boris Yeltsin, Yekaterinburg, 620002, Russia.
Satvik VatsDepartment of Computer Science and Engineering, Graphic Era Hill University, Dehradun, India.
F RanjbarDepartment of Mechanical Engineering, Islamic Azad University, Najafabad Branch, Najafabad, Iran. fereydoonranjbar1990@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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

Indexed as

CondensationHeat transfer coefficientHelical tubesMachine learningPrediction

Identifiers

PMID40830180
PMCPMC12365103

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