Establishing artificial neural network to investigate thermal properties of carbon nanotube/zinc oxide composite dispersed in water

Abstract

Nanofluid refers to the mixture of fluid and solid nanoparticles (NPs). If this mixture contains one NP or fluid, it is called ‘mono nanofluid’ (MN). Further, if this mixture contains more than one NPs or fluid, it is called ‘hybrid nanofluid’ (HN). In this research, first, zinc oxide NP was dispersed in the water base fluid and formed an MN. Then, thermal conductivity (TC) of MN was measured. After that, multiwalled carbon nanotube (MWCNT) NP was added to the MN and formed an HN. Further, TC of HN was measured. Both MN and HN TCs were compared, and the results revealed that by adding MWCNT, TC enhanced by about 18.79% (from 25.12% of MN to 43.91% of HN). On the other hand, to analyze the phase structure and to observe the microstructure, X‐ray diffraction analysis was examined. The measured TC for both samples was at volume fractions up to 1.0% and temperatures up to 50°C. After an experimental study, two novel correlations were calculated by the curve fitting method for MN and HN individually. In the end, to predict the other Vf and temperature, the artificial neural network approach has been developed.