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Prediction of heat transfer characteristics in a microchannel with vortex generators by machine learning

  • Alişan Gönül ORCID logo EMAIL logo , Andaç Batur Çolak ORCID logo , Nurullah Kayaci ORCID logo , Abdulkerim Okbaz ORCID logo and Ahmet Selim Dalkilic ORCID logo
Published/Copyright: January 5, 2023
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Kerntechnik
From the journal Kerntechnik Volume 88 Issue 1

Abstract

Because of the prompt improvements in Micro-Electro-Mechanical Systems, thermal management necessities have altered paying attention to the compactness and high energy consumption of actual electronic devices in industry. In this study, 625 data sets obtained numerically according to the change of five different geometric parameters and Reynolds numbers for delta winglet type vortex generator pairs placed in a microchannel were utilized. Four dissimilar artificial neural network models were established to predict the heat transfer characteristics in a microchannel with innovatively oriented vortex generators in the literature. Friction factor, Nusselt number, and performance evaluation criteria were considered to explore the heat transfer characteristics. Different neuron numbers were determined in the hidden layer of each of the models in which the Levethenberg–Marquardt training algorithm was benefited as the training algorithm. The predicted values were checked against the target data and empirical correlations. The coefficient of determination values calculated for each machine learning model were found to be above 0.99. According to obtained results, the designed artificial neural networks can provide high prediction performance for each data set and have higher prediction accuracy compared to empirical correlations. All data predicted by machine learning models were collected within the range of ±3% deviation bands, whereas the majority of the estimated data by empirical correlations dispersed within ±20% ones. For that reason, a full evaluation of the estimation performance of artificial neural networks versus empirical correlations data is enabled to fill a gap in the literature as one of the uncommon works.

Keywords: artificial neural network; heat transfer enhancement; Levenberg–Marquardt; machine learning; microchannel; vortex generator
Corresponding author: Alişan Gönül, Department of Mechanical Engineering, Siirt University, 56100 Siirt, Turkey, E-mail:

  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: 2022-08-20
Revised: 2022-10-25
Published Online: 2023-01-05
Published in Print: 2023-02-23

© 2022 Walter de Gruyter GmbH, Berlin/Boston

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  2. Application of the COCOSYS code in the safety evaluation of Czech nuclear power plants
  3. Improving of electric network feeding nuclear facility based on multiple types DGs placement
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https://doi.org/10.1515/kern-2022-0075
Keywords for this article
artificial neural network; heat transfer enhancement; Levenberg–Marquardt; machine learning; microchannel; vortex generator

Articles in the same Issue

  1. Frontmatter
  2. Application of the COCOSYS code in the safety evaluation of Czech nuclear power plants
  3. Improving of electric network feeding nuclear facility based on multiple types DGs placement
  4. Design and evaluation of ecological interface for Feedwater Deaerating Tank and Gas Stripper System based on cognitive work analysis
  5. Evaluation of different integrated burnable absorber materials in fuel assemblies of Bushehr WWER-1000 nuclear reactor
  6. Effective physical protection system design and implementation at a radiological facility: an integrated and risk management approach
  7. Determination of limiter design and material composition of MT-II spherical tokamak
  8. Dynamics effects of tritium reduction on the energy gain of D-T fuel pellet using double cone ignition
  9. Design of an unattended ore grading measurement system in a uranium mine
  10. Prediction of heat transfer characteristics in a microchannel with vortex generators by machine learning
  11. Prediction of nanofluid flows’ optimum velocity in finned tube-in-tube heat exchangers using artificial neural network
  12. Investigating the in-core 60Co production assembly for open pool type reactor
  13. Calendar of events
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