Effects of temperature and nanoparticle mixing ratio on the thermophysical properties of GNP–Fe₂O₃ hybrid nanofluids: an experimental study with RSM and ANN modeling

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.