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

Journal: Scientific reports
Published Date: Aug 19, 2025

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 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.

Authors

  • Chou-Yi Hsu
    Thunderbird School of Global Management, Arizona State University, Tempe Campus, Phoenix, AZ, 85004, USA.
  • Nikunj Rachchh
    Department of Mechanical Engineering, Faculty of Engineering & Technology, Marwadi University Research Center, Marwadi University, Rajkot, Gujarat, 60003, India.
  • T Ramachandran
    Department of Sciences, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, 641112, India.
  • Aman Shankhyan
    Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, Punjab, 140401, India.
  • A Karthikeyan
    Department of Computer Science and Engineering, Panimalar Engineering College, Chennai, Tamil Nadu, India.
  • Ahmad Alkhayyat
    Department of Computer Techniques Engineering, College of Technical Engineering, The Islamic University, Najaf, Iraq; Department of Computer Techniques Engineering, College of Technical Engineering, The Islamic University of Al Diwaniyah, Al Diwaniyah, Iraq; Department of Computers Techniques Engineering, College of Technical Engineering, The Islamic University of Babylon, Babylon, Iraq.
  • Prabhat Kumar Sahu
    Department of Computer Science and Information Technology, Siksha 'O' Anusandhan (Deemed to be University), Bhubaneswar, Odisha, 751030, India.
  • Abhinav Kumar
  • Satvik Vats
    Computer Science and Engineering, Graphic Era Hill University, Dehradun, Uttarakhand, India.
  • F Ranjbar
    Department of Mechanical Engineering, Islamic Azad University, Najafabad Branch, Najafabad, Iran. fereydoonranjbar1990@gmail.com.

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