Comparison of the Experimental and Predicted Data for Thermal Conductivity of Fe3O4/water Nanofluid Using Artificial Neural Networks

Abstract

Objective(s): This study aims to evaluate and predict the thermal conductivity of iron oxide nanofluid at different temperatures and volume fractions by artificial neural network (ANN) and correlation using experimental data. Methods: Two-layer perceptron feedforward artificial neural network and backpropagation Levenberg-Marquardt (BP-LM) training algorithm are used to predict the thermal conductivity of the nanofluid. Fe3O4 nanoparticles are prepared by chemical co-precipitation method and thermal conductivity coefficient is measured using 2500TPS apparatus. Results: Fe3O4 nanofluids with particle size of 20-25 nm are used to test the effectiveness of ANN. Thermal conductivity of Fe3O4 /water nanofluid at different temperatures of 25, 30 and 35℃ and volume concentrations, ranging from 0.05% to 5% is employed as training data for ANN. The obtained results show that the thermal conductivity of Fe3O4 nanofluid increases linearly with volume fraction and temperature. Conclusions: the artificial neural network model has a reasonable agreement in predicting experimental data. So it can be concluded the ANN model is an effective method for prediction of the thermal conductivity of nanofluids and has better prediction accuracy and simplicity compared with the other existing theoretical methods.