期刊
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
卷 519, 期 -, 页码 209-216出版社
ELSEVIER
DOI: 10.1016/j.physa.2018.12.010
关键词
ANN; Curve-fitting; Hybrid nanofluid; Correlation; Experimental data
资金
- National Natural Science Foundation of China [51606214]
In this paper, the experimental data on the thermal conductivity of EG based hybrid nanofluid containing zinc oxide and titanium oxide have been used. At the first, three two-variable correlations have been proposed using curve-fitting on experimental data. After that, the best transfer function for training the artificial neural network has been selected. The input variables of neural network were temperature and solid volume fraction, while the output variable was the thermal conductivity enhancement of the nanofluid. Moreover, the correlation outputs, ANN results and experimental data have been compared. The results showed that there is a good agreement between experimental data and neural network results so that the resulting model of the neural network is able to predict the thermal conductivity enhancement of the nanofluid. The findings also indicated that the accuracy of the neural network is much greater than the curve fitting method to predict thermal conductivity enhancement of ZnO-TiO2/EG hybrid nanofluid. (C) 2018 Published by Elsevier B.V.
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