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Designing artificial neural network on thermal conductivity of Al2O3–water–EG (60–40 %) nanofluid using experimental data

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Abstract

The main purpose of this research was to investigate the efficiency of artificial neural networks in modeling thermal conductivity data of water–EG (40–60 %) nanofluid with aluminum oxide nanoparticles (with average diameter of 36 nm). The measured data as modeling input data are in six volume fractions from 0 to 1.5 % and different temperatures from 20 to 60 °C. In order to optimize the network, different numbers of neurons with different transfer functions have been tested and after preprocessing and normalizing the data, the optimum network structure with one hidden layer and six neurons was obtained. This structure simulated the experimental data with very high precision. The measured thermal conductivity was compared with the two models that calculated thermal conductivity for mixtures. The results indicated that Hamilton–Crosser and Lu–Lin models failed in estimating the thermal conductivity of Alumina–water–EG nanofluid in different temperatures and concentration. Finally, a new correlation was presented based on experimental data with regression coefficient of 0.9974.

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Abbreviations

ANN:

Artificial neural network

Dev.:

Deviation of results from empirical data

EG:

Ethylene glycol

k:

Thermal conductivity

MSE:

Mean-squared error

R:

Regression coefficient

S:

Standard distance from regression line

Std:

Standard deviation

T:

Temperature

φ :

Volume concentration

eff.:

Efficient

exp.:

Obtained experimentally

f:

Fluid

nf:

Nanofluid

P:

Particle

pred.:

Predicted results

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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. Department of Computer Engineering, Imam Hossein University, Tehran, Iran

    Mohammad Reza Hassani Ahangar

  3. Department of Mechanical Engineering, Islamshahr Branch, Islamic Azad University, Tehran, Iran

    Mohammad Hadi Hajmohammad & Hossein Tourang

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

    Hadi Rostamian

Authors
  1. Mohammad Hemmat Esfe
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  2. Mohammad Reza Hassani Ahangar
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  3. Davood Toghraie
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  4. Mohammad Hadi Hajmohammad
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  5. Hadi Rostamian
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  6. Hossein Tourang
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Correspondence to Mohammad Hemmat Esfe or Davood Toghraie.

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Hemmat Esfe, M., Ahangar, M.R.H., Toghraie, D. et al. Designing artificial neural network on thermal conductivity of Al2O3–water–EG (60–40 %) nanofluid using experimental data. J Therm Anal Calorim 126, 837–843 (2016). https://doi.org/10.1007/s10973-016-5469-8

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  • DOI: https://doi.org/10.1007/s10973-016-5469-8

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