Estimation of thermal conductivity of Al2O3/water (40%)–ethylene glycol (60%) by artificial neural network and correlation using experimental data

In this work, the estimation of thermal conductivity of Al2O3 nanoparticles in water (40%)–ethylene glycol (60%) has been investigated. An empirical relationship has been proposed based on experimental data and in terms of temperature and volume fraction. Besides, a model has been presented using feedforward multi-layer perceptron (MLP) artificial neural network (ANN). The presented correlation relationship estimates empirical data very well. However, artificial neural network has a higher regression coefficient and lower error compared to the presented relationship. After examining different structures of neural network with different transfer functions, a structure was selected with two hidden layers and 5 neurons in the first and second layers and tangent sigmoid transfer function for both layers. The results indicate that artificial neural networks can precisely estimate the experimental data of thermal conductivity of Al2O3/water (40%)–ethylene glycol (60%) nanofluids.