Applicability of artificial neural network and nonlinear regression to predict thermal conductivity modeling of Al2O3–water nanofluids using experimental data

In the present study, the thermal conductivity of Al2O3–water nanofluid at different temperatures and solid volume fractions has been modeled by artificial neural network (ANN) and correlation using experimental data. The thermal conductivity of the nanofluids at different fluid temperatures, ranging from 26 to 55 °C is employed as training data for ANN. Furthermore, based on the experimental data and using artificial neural network, a correlation for modeling the thermal conductivity of the nanofluid in terms of temperature and solid volume fraction is proposed. The results show that the proposed correlation has good ability for predicting the thermal conductivity of the nanofluids. On the other hand, the ANN model shows excellent agreement with the results of the experimental data.