Algorithm

Oxide

Nanoparticle

Biology

Metallurgy

Materials science

Thermal conductivity

Computer science

Correlation coefficient

Zinc

Volume fraction

Thermodynamics

Physics

Nanotechnology

Thermal

Non-Newtonian fluid

Artificial intelligence

Composite material

Nanofluid

Approximation error

Artificial neural network

Biological system

Mechanics

Machine learning

International Communications in Heat and Mass Transfer

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 1.6684e-05.

Using of Artificial Neural Networks (ANNs) to predict the thermal conductivity of Zinc Oxide–Silver (50%–50%)/Water hybrid Newtonian nanofluid