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Measurement of the thermal conductivity of MWCNT-CuO/water hybrid nanofluid using artificial neural networks (ANNs)

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Abstract

In this paper, artificial neural networks (ANNs) are developed to predict the thermal conductivity (\(k_{\text{nf}}\)) of multi-walled carbon nanotubes (MWCNTs)-CuO/water nanofluid. After generating experimental data points, an algorithm is proposed to find the optimum ANN regarding the best performance. Three different states including ANN, experimental, and fitting method have been evaluated, and their errors in \(k_{\text{nf}}\) prediction have been investigated. Regarding the obtained results, it can be seen that the best and worst neuron numbers are 8 and 31, respectively. Then, using curve fitting method, the behavior of nanofluid is predicted by a surface equation with third order. Finally, the ANN results and fitting results have been compared. Finally, it is found that the ability of the ANN to predict the \(k_{\text{nf}}\) is greater. It was also found that the ANN has better performance and correlation and thus less error in the predicted data. On the other hand, comparing methods in predicting the \(k_{\text{nf}}\) is an important issue. The use of ANNs in predicting the \(k_{\text{nf}}\) as a new approach can lead to a great contribution in determining the most desirable performance and achieving the best and most accurate state. In addition, mean squared error (MSE) has obtained 2.4451e−05 for fitting method. According to the experimental data, it can be seen that in φ = 0.6% and T = 50 °C, an increase of more than 30.38% has occurred in the \(k_{\text{nf}}\) compared to the ambient temperature.

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Abbreviations

b i :

Bias of the ith neuron

x j :

jth input

ANNs:

Artificial neural networks

f :

Activation function

LM:

Levenberg–Marquardt

MSE:

Mean squared error

MWCNTs:

Multi-walled carbon nanotubes

N:

Number of experimental data

T:

Temperature °C

tansig(n) :

Activation function for input values

y:

Output of ANN

φ :

Volume fraction of nanoparticles

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Authors and Affiliations

  1. Laboratory of Magnetism and Magnetic Materials, Advanced Institute of Materials Science, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Sara Rostami

  2. Faculty of Applied Sciences, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Sara Rostami

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

    Davood Toghraie & Pouya Barnoon

  4. Department of Mechanical Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Behrooz Shabani & Nima Sina

Authors
  1. Sara Rostami
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  2. Davood Toghraie
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  3. Behrooz Shabani
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  4. Nima Sina
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  5. Pouya Barnoon
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Correspondence to Davood Toghraie.

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Rostami, S., Toghraie, D., Shabani, B. et al. Measurement of the thermal conductivity of MWCNT-CuO/water hybrid nanofluid using artificial neural networks (ANNs). J Therm Anal Calorim 143, 1097–1105 (2021). https://doi.org/10.1007/s10973-020-09458-5

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  • DOI: https://doi.org/10.1007/s10973-020-09458-5

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