期刊
INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER
卷 116, 期 -, 页码 -出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.icheatmasstransfer.2020.104645
关键词
Artificial Neural Networks (ANNs); Thermal conductivity; Hybrid Newtonian nanofluid
资金
- Fujian Province Natural Science Foundation [2018J01506]
- University-industry cooperation program of Department of Science and Technology of Fujian Province [2019H6018]
- Fuzhou Science and Technology Planning Project [2018S113, 2018G92]
- Educational Research Projects of Young Teachers of Fujian Province [JK2017038, JAT170439]
- Outstanding Young Scientist Training Program of Colleges in Fujian Province
In this study, after generating experimental data points of Zinc Oxide (ZnO)-Silver (Ag) (50%-50%)/Water nanofluid, an algorithm is proposed to calculate the best neuron number in the Artificial Neural Network (ANN), and the performance and correlation coefficient for ANN has been calculated. Then, using the fitting method, a surface is fitted on the experimental data, and the correlation coefficient and performance of this method have been calculated. Finally, the absolute values of errors in both methods have been compared. It can be seen that the best neuron number in the hidden layer is 7 neurons. We concluded that both methods could predict the behavior of nanofluid, but the fitting method had smaller errors. Also, the ANN method had better ability in predicting the thermal conductivity of nanofluid based on the volume fraction of nanoparticles and temperature. Finally, we found that, in ANN, all outputs, the maximum absolute value of error is 0.0095, and the train performance is 1.6684-05.
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