Thermal Conductivity Modeling of Propylene Glycol - Based Nanofluid Using Artificial Neural Network

Abstract

The article introduces an artificial neural network model that simulates and predicts thermal conductivity and particle size of propylene glycol-based nanofluids containing Al2O3 and TiO2 nanoparticles in a temperature range of 20 - 80oc. The experimental data indicated that the nanofluids have excellent stability over the temperature scale of interest and thermal conductivity enhancement for both nanofluid samples. The neural network system was trained on the available experimental data. The system was designed to find the optimal network that has the best training performance. The nonlinear equations which represent the relation between the inputs and output were obtained. The results of the neural network model and the theoretical models of the proposed system were performed and compared with the experimental results. The neural network system appears to yield the best fit consistent with experimental data. The results of the paper demonstrate the ability of the neural network model as an excellent computational tool in the nanofluid field.