Investigating the Thermal Conductivity Coefficient of Water-Based Ternary Hybrid Nanofluid Containing Mwcnts by Artificial Neural Network Method

Document Type : Original Article

Authors

1 Department of Mechanical Engineering, Imam Hossein University, Tehran, Iran

2 Chemical Engineering Department-Faculty of Technology and Engineering-Imam Hossein University -Tehran-Iran

Abstract

Thermal conductivity of MWCNT(40%)-CuO(30%)-TiO2(30%)/Water nanofluid in different volume fractions and temperatures is modeled and analyzed by artificial neural network method. The type of artificial neural network is MLP. 48 experimental data series are used, 70%, 15%, and 15% are used for training, validation, and testing, respectively. The optimal neural structure has two hidden layers, in which there are 4 neurons in the first layer and 5 neurons in the second layer, with the transfer functions of logsig and tansig, respectively. Neural network training is done with Levenberg-Marquardt (ML) algorithm. The values of regression coefficient, R, and mean squared error, MSE, for the optimal structure are obtained as 0.9995753 and 2.8734E-06, respectively. The correlation equation is also presented to predict the thermal conductivity of nanofluid. The comparison between the correlation equation and the artificial neural network shows the superiority of the artificial neural network. MOD values for artificial neural network are also in the range of -3% to +7%.

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https://creativecommons.org/licenses/by/4.0/

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History

  • Receive Date: 15 October 2023
  • Revise Date: 21 December 2024
  • Accept Date: 15 January 2024
  • Publish Date: 19 February 2024

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