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

By A. Akhgar, Davood Toghraie, Nima Sina, Masoud Afrand. Powder Technology, 2019

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Abstract 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.

Subfields: Biomedical Engineering, Electrical and Electronic Engineering, Renewable Energy, Sustainability and the Environment

View Paper: https://www.semanticscholar.org/paper/6ab0fcd582a6ed571a96705c814f62b7eb426e0d