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

In this paper, the thermal conductivity (knf) of cerium oxide/ethylene glycol nanofluid is extracted for different temperatures (T = 25, 30, 35, 40, 45, and 50 degrees C) and the volume fraction of nanoparticles ((sic) = 0, 0.25, 0.5, 0.75, 1, 1.5, 2 and 2.5%) and then knf 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 f 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 knf is much higher than the change in the same amount of f 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.

Automated summary

This paper extracted the thermal conductivity of cerium oxide/ethylene glycol nanofluid for different temperatures and volume fractions of nanoparticles, and predicted it using Artificial Neural Network (ANN) and fitting method. The experiments showed an increase in the thermal conductivity ratio of the nanofluid with increasing volume fraction and temperature. It was observed that the rate of increase in thermal conductivity at high temperatures is higher than at low temperatures. The ANN proved to be highly effective in predicting the thermal behavior of the nanofluid.