Designing artificial neural network on thermal conductivity of Al2O3–water–EG (60–40 %) nanofluid using experimental data

The main purpose of this research was to investigate the efficiency of artificial neural networks in modeling thermal conductivity data of water–EG (40–60 %) nanofluid with aluminum oxide nanoparticles (with average diameter of 36 nm). The measured data as modeling input data are in six volume fractions from 0 to 1.5 % and different temperatures from 20 to 60 °C. In order to optimize the network, different numbers of neurons with different transfer functions have been tested and after preprocessing and normalizing the data, the optimum network structure with one hidden layer and six neurons was obtained. This structure simulated the experimental data with very high precision. The measured thermal conductivity was compared with the two models that calculated thermal conductivity for mixtures. The results indicated that Hamilton–Crosser and Lu–Lin models failed in estimating the thermal conductivity of Alumina–water–EG nanofluid in different temperatures and concentration. Finally, a new correlation was presented based on experimental data with regression coefficient of 0.9974.