Prediction of nanofluid flows’ optimum velocity in finned tube-in-tube heat exchangers using artificial neural network

Andaç Batur Çolak; Hatice Mercan; Özgen Açıkgöz; Ahmet Selim Dalkılıç; Somchai Wongwises

doi:10.1515/kern-2022-0097

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Prediction of nanofluid flows’ optimum velocity in finned tube-in-tube heat exchangers using artificial neural network

Andaç Batur Çolak , Hatice Mercan , Özgen Açıkgöz , Ahmet Selim Dalkılıç and Somchai Wongwises

Published/Copyright: December 22, 2022

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From the journal Kerntechnik Volume 88 Issue 1

Abstract

The average flow velocity in heat exchangers is considered less often and thus needs further and detailed investigation because of its crucial influence on the overall thermal performance of the application. The use of nanofluids has similar influences to finned tube designs. Considering the rise in heat transfer and pressure drop, uncertainties in cost analyses with the uses of fins and nanoparticles, evaluation of optimum operating velocity of the fluids is necessary. On the contrary, there aren’t enough experimental, parametric, or numerical investigations present on this subject. The use of machine learning techniques to heat transfer applications to make optimization becomes popular recently. In this work, important factors of the process as tube number, cleanliness factor, and overall cost as output factors have been estimated by an artificial intelligence method using 339 data points. The influence of input factors of Reynolds number, thermal conductivity, specific heat, viscosity, and total fin surface efficiency on the outputs have been studied. Total tube number, cleanliness factor, and total cost analysis have been determined with deviations of −0.66%, 0.001%, and 0.12% as a result of the solution with 6 inputs, correspondingly.

Keywords: ANN; cost analysis; finned double-pipe heat exchanger; Levenberg–Marquardt; MLP

Corresponding author: Andaç Batur Çolak, Information Technologies Application and Research Center, Istanbul Commerce University, Istanbul 34445, Türkiye, E-mail: abcolak@ticaret.edu.tr

Funding source: Thailand Science Research and Innovation

Award Identifier / Grant number: Fundamental Fund 2022

Acknowledgments

The fourth author acknowledges the visiting professorship from KMUTT. The fifth author acknowledges the National Science and Technology Development Agency (NSTDA) under the “Research Chair Grant”, and the Thailand Science Research and Innovation (TSRI) under Fundamental Fund 2022.

Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
Research funding: None declared.
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.

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Received: 2022-10-22

Published Online: 2022-12-22

Published in Print: 2023-02-23

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Articles in the same Issue

https://doi.org/10.1515/kern-2022-0097

Keywords for this article

ANN; cost analysis; finned double-pipe heat exchanger; Levenberg–Marquardt; MLP