An artificial neural network-based numerical estimation of the boiling pressure drop of different refrigerants flowing in smooth and micro-fin tubes

Çolak A. B., Bacak A., KAYACI N., DALKILIÇ A. S.

Kerntechnik, vol.89, no.1, pp.15-30, 2024 (SCI-Expanded) identifier

  • Publication Type: Article / Article
  • Volume: 89 Issue: 1
  • Publication Date: 2024
  • Doi Number: 10.1515/kern-2023-0087
  • Journal Name: Kerntechnik
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex
  • Page Numbers: pp.15-30
  • Keywords: artificial neural network, machine learning, micro-fin tube, pressure drop, two-phase flow
  • Yıldız Technical University Affiliated: Yes


In thermal engineering implementations, heat exchangers need to have improved thermal capabilities and be smaller to save energy. Surface adjustments on tube heat exchanger walls may improve heat transfer using new manufacturing technologies. Since quantifying enhanced tube features is quite difficult due to the intricacy of fluid flow and heat transfer processes, numerical methods are preferred to create efficient heat exchangers. Recently, machine learning algorithms have been able to analyze flow and heat transfer in improved tubes. Machine learning methods may increase heat exchanger efficiency estimates using data. In this study, the boiling pressure drop of different refrigerants in smooth and micro-fin tubes is predicted using an artificial neural network-based machine learning approach. Two different numerical models are built based on the operating conditions, geometric specifications, and dimensionless numbers employed in the two-phase flows. A dataset including 812 data points representing the flow of R12, R125, R134a, R22, R32, R32/R134a, R407c, and R410a through smooth and micro-fin pipes is used to evaluate feed-forward and backward propagation multi-layer perceptron networks. The findings demonstrate that the neural networks have an average error margin of 10 percent when predicting the pressure drop of the refrigerant flow in both smooth and micro-fin tubes. The calculated R-values for the artificial neural network’s supplementary performance factors are found above 0.99 for all models. According to the results, margins of deviations of 0.3 percent and 0.05 percent are obtained for the tested tubes in Model 1, while deviations of 0.79 percent and 0.32 percent are found for them in Model 2.