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

Akhgar, Alireza, Toghraie, Davood, Sina, Nima, Afrand, Masoud
Powder technology 2019 v.355 pp. 602-610
algorithms, data collection, nanofluids, nanoparticles, neural networks, powders, prediction, temperature, thermal conductivity
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.