Abstract:
This study investigated the impact of temperature and nanoparticle mixing ratio on the thermophysical properties of hybrid
nanofluids (HNFs) made with graphene nanoplatelets (GNP) and iron oxide nanoparticles (
Fe2O3). The results showed
that increased temperature led to higher thermal conductivity (TC) and electrical conductivity (EC), and lower viscosity in
HNFs. Higher GNP content relative to Fe2O3
also resulted in higher TC but lower EC and viscosity. Artificial neural network
(ANN) and response surface methodology (RSM) were used to model and correlate the thermophysical properties of HNFs.
The ANN models showed a high degree of correlation between predicted and actual values for all three properties (TC, EC,
and viscosity). The optimal number of neurons varied for each property. For TC, the model with six neurons performed the
best, while for viscosity, the model with ten neurons was optimal. The best ANN model for EC contained 18 neurons. The
RSM results indicated that the 2-factor interaction term was the most significant factor for optimizing TC and EC; while,
the linear term was most important for optimizing viscosity. The ANN models performed better than the RSM models for
all properties. The findings provide insights into factors affecting the thermophysical properties of HNFs and can inform the
development of more effective heat transfer fluids for industrial applications.
