Developing dissimilar artificial neural networks (ANNs) to prediction the thermal conductivity of MWCNT-TiO2/Water-ethylene glycol hybrid nanofluid

In this paper, we developed dissimilar artificial neural networks (ANNs) by suitable architectures and training algorithms via sensitivity analysis to predict the thermal conductivity MWCNT -TiO2/ Water-Ethylene glycol nanofluid. Forecasting of thermal conductivity of MWCNT –TiO2/ Water-Ethylene gly...

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Bibliographic Details
Published in:Powder technology 2019-10, Vol.355, p.602-610
Main Authors: Akhgar, Alireza, Toghraie, Davood, Sina, Nima, Afrand, Masoud
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural network
Artificial neural networks
Concentration
Correlation
Datasets
Empirical analysis
Ethylene
Ethylene glycol
Heat conductivity
Heat transfer
Nanofluid
Nanofluids
Nanoparticles
Neural networks
Sensitivity analysis
Stability analysis
Temperature
Thermal conductivity
Titanium dioxide
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Summary:In this paper, we developed dissimilar artificial neural networks (ANNs) by suitable architectures and training algorithms via sensitivity analysis to predict the thermal conductivity MWCNT -TiO2/ Water-Ethylene glycol nanofluid. Forecasting of thermal conductivity of MWCNT –TiO2/ Water-Ethylene glycol nanofluid based on changes in temperature and concentration using ANN and stability analysis is done. MWCNTs-TiO2 hybrid nanoparticles were also used at a 50:50 volume ratio. The dataset of ANN was divided into three main parts including 70% for the train, 15% for test and 15% for validation and the results of the optimum ANN are in a better agreement to the empirical dataset, and it can predict the thermal conductivity of MWCNT-TiO2-Wa-EG(50–50) better than the correlation. The empirical dataset, ANN outputs, and correlation results were presented. There is a small difference between correlation results and ANN outputs, and it can be concluded that ANN outputs are can predict the empirical results better than the correlation formula. [Display omitted] •Forecasting of thermal conductivity of nanofluid based on changes in temperature and concentration.•Using artificial neural network and stability analysis.•Investigation of stability of MWCNT -TiO2/ Ethylene glycol nanofluid.
ISSN:0032-5910
1873-328X
DOI:10.1016/j.powtec.2019.07.086