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Licensed Unlicensed Requires Authentication Published by De Gruyter June 15, 2022

Numerical determination of condensation pressure drop of various refrigerants in smooth and micro-fin tubes via ANN method

Andaç Batur Çolak EMAIL logo , Ali Celen and Ahmet Selim Dalkılıç ORCID logo
From the journal Kerntechnik

Abstract

In the current work, the pressure drop of the refrigerant flow in smooth and micro-fin pipes has been modeled with artificial neural networks as one of the powerful machine learning algorithms. Experimental analyses have been evaluated in two groups for the numerical model such as operation parameters/physical properties and dimensionless numbers used in two-phase flows. Feed forward back propagation multi-layer perceptron networks have been developed evaluating the practically obtained dataset having 673 data points covering the flow of R22, R134a, R410a, R502, R507a, R32 and R125 in four different pipes. The outputs acquired from the artificial neural network have been evaluated with the target ones, and the performance factors have been estimated and the prediction accuracy of the network models has been resourced comprehensively. The results revealed that the neural networks could predict the pressure drop of the refrigerant flow in smooth and micro-fin pipes between 10% deviation bands.


Corresponding author: Andaç Batur Çolak, Mechanical Engineering Department, Engineering Faculty, Niğde Ömer Halisdemir University, Niğde 51240, Turkey, E-mail:

Acknowledgments

Experimental data of Eckels and Pate (1991), which was presented in study of Choi et al. (1999) enabled by NIST, was evaluated in the current work. The authors wish to thank them for their contributions to the topic of in-tube condensation.

  1. Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.

  2. Research funding: None declared.

  3. Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Received: 2022-04-03
Published Online: 2022-06-15
Published in Print: 2022-10-26

© 2022 Walter de Gruyter GmbH, Berlin/Boston

Downloaded on 9.12.2022 from https://www.degruyter.com/document/doi/10.1515/kern-2022-0037/html
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