Using neural network and RSM to evaluate improvement in thermal conductivity of nanodiamond-iron oxide/antifreeze

This study aimed to investigate the accuracy of the artificial neural network in estimating thermal conductivity (k) of ferrofluid-based nanofluids. The parameters of k(ND+Fe2O3/EG)-water and kEG-water have been measured at 20-60 degrees C, 0.05, 0.1, and 0.2 vol.% and the results showed that kFe(2)O(3)/EG-water was greater than kEG-water by 89%, which is obtained at 60 degrees C and 0.2 vol.%. To estimate kND+ Fe3O4/EG-water a three-layer ANN was developed that contained two, three, and one neurons, respectively. This neural network was able to estimate k(ND+) (Fe3O4/EG)-water with less than 0.8% error considering of R-2=0.996. The response surface methodology was also implemented, and it was observed that cubic polynomials, taking to account of R-2=0.994, will figure out the best results so that k(ND+ Fe3O4/EG)-water can be predicted with an error of less than 0.5%.

Automated summary

This study investigates the accuracy of artificial neural network and response surface methodology in estimating the thermal conductivity of ferrofluid-based nanofluids. The results show that the neural network and response surface methodology can accurately estimate the thermal conductivity with errors less than 0.8% and 0.5% respectively.