Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7933934 | Physica E: Low-dimensional Systems and Nanostructures | 2017 | 20 Pages |
Abstract
In this paper, the thermal conductivity ratio of MgO-MWCNTs/EG hybrid nanofluids has been predicted by an optimal artificial neural network at solid volume fractions of 0.05%, 0.1%, 0.15%, 0.2%, 0.4% and 0.6% in the temperature range of 25-50 °C. In this way, at the first, thirty six experimental data was presented to determine the thermal conductivity ratio of the hybrid nanofluid. Then, four optimal artificial neural networks with 6, 8, 10 and 12 neurons in hidden layer were designed to predict the thermal conductivity ratio of the nanofluid. The comparison between four optimal ANN results and experimental showed that the ANN with 12 neurons in hidden layer was the best model. Moreover, the results obtained from the best ANN indicated the maximum deviation margin of 0.8%.
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Authors
Masoud Vafaei, Masoud Afrand, Nima Sina, Rasool Kalbasi, Forough Sourani, Hamid Teimouri,