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Using artificial neural network to predict thermal conductivity of ethylene glycol with alumina nanoparticle

Effects of temperature and solid volume fraction

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Abstract

The correlations of thermal conductivity of alumina nanoparticle dispersed in pure ethylene glycol were proposed by neural network modeling using experimental data. The required input and target data have been taken from the experimental measurement to train artificial neural network (ANN). The temperatures were changed within 24–50 °C. Levenberg algorithm was used to train the ANN. Results showed that the thermal conductivity of nanofluid had a significant increase with increasing solid volume fraction of nanoparticles. The results also revealed that the ANN model can predict the thermal conductivity of Al2O3–EG nanofluid accurately with maximum deviation of 1.3 % and high correlation coefficient (R > 0.998).

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Acknowledgements

The authors would like to acknowledge the assistance provided by the nanofluid Laboratory of Semnan university science and technology Park for providing necessary instrumentation to carry out the sample preparation and helping in the analysis of samples to complete the article in time.

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Authors and Affiliations

  1. Department of Mechanical Engineering, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran

    Mohammad Hemmat Esfe & Davood Toghraie

  2. Young Researchers and Elite Club, Khomeinishahr Branch, Islamic Azad University, Isfahan, Iran

    Hadi Rostamian

  3. Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, Taipei, 10608, Taiwan, ROC

    Wei-Mon Yan

Authors
  1. Mohammad Hemmat Esfe
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  2. Hadi Rostamian
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  3. Davood Toghraie
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  4. Wei-Mon Yan
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Correspondence to Mohammad Hemmat Esfe or Hadi Rostamian.

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Hemmat Esfe, M., Rostamian, H., Toghraie, D. et al. Using artificial neural network to predict thermal conductivity of ethylene glycol with alumina nanoparticle. J Therm Anal Calorim 126, 643–648 (2016). https://doi.org/10.1007/s10973-016-5506-7

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  • DOI: https://doi.org/10.1007/s10973-016-5506-7

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