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Journal of Thermal Analysis and Calorimetry

, Volume 126, Issue 2, pp 837–843 | Cite as

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

  • Mohammad Hemmat Esfe
  • Mohammad Reza Hassani Ahangar
  • Davood Toghraie
  • Mohammad Hadi Hajmohammad
  • Hadi Rostamian
  • Hossein Tourang
Article

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.

Keywords

Nanofluid Thermal conductivity Artificial neural network Thermophysical properties 

List of symbols

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

Greek symbols

φ

Volume concentration

Subscripts

eff.

Efficient

exp.

Obtained experimentally

f

Fluid

nf

Nanofluid

P

Particle

pred.

Predicted results

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Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2016

Authors and Affiliations

  • Mohammad Hemmat Esfe
    • 1
  • Mohammad Reza Hassani Ahangar
    • 2
  • Davood Toghraie
    • 1
  • Mohammad Hadi Hajmohammad
    • 3
  • Hadi Rostamian
    • 4
  • Hossein Tourang
    • 3
  1. 1.Department of Mechanical Engineering, Khomeinishahr BranchIslamic Azad UniversityIsfahanIran
  2. 2.Department of Computer EngineeringImam Hossein UniversityTehranIran
  3. 3.Department of Mechanical EngineeringIslamshahr Branch, Islamic Azad UniversityTehranIran
  4. 4.Young Researchers and Elite Club, Khomeinishahr BranchIslamic Azad UniversityIsfahanIran

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