Statistical modeling and investigation of thermal characteristics of a new nanofluid containing cerium oxide powder

In this paper, the thermal conductivity (k(nf)) of cerium oxide/ethylene glycol nanofluid is extracted for different temperatures (T = 25, 30, 35, 40, 45, and 50 °C) and the volume fraction of nanoparticles ([Formula: see text] 0, 0.25, 0.5, 0.75, 1, 1.5, 2 and 2.5%) and then k(nf) is predicted by two methods including Artificial Neural Network (ANN) and fitting method. For both methods, the results have been presented and compared. The experiments showed that with increasing [Formula: see text] and temperature, the thermal conductivity ratio (TCR) of nanofluid increases. It was also observed that when the experiments are performed at high temperatures, the rate of increase in k(nf) is much higher than the change in the same amount of [Formula: see text] change at low temperatures. An ANN with 7 neurons has a correlation coefficient very close to 1 and this proves that the outputs are compatible with experimental results. Also, it can be seen that the ANN could predict the thermal behavior of cerium oxide/ethylene glycol nanofluid more accurately.