Efficacy evaluation of oxide-MWCNT water hybrid nanofluids: An experimental and artificial neural network approach

The temperature dependencies of the thermophysical properties of different hybrid nanofluids have been investigated in the present work. Laboratory experiments were performed and optimized ANN model was developed for prediction and regression analysis. MWCNT-water based Al2O3, TiO2, ZnO and CeO2 nanofluids were used for this purpose. Metal oxide based nanofluids and MWCNT nanofluids were prepared and mixed in the 80:20 volumetric ratio. Nanofluids were prepared for volumetric concentrations varying from 0.25% to 2.0%. Thermophysical properties of hybrid nanofluids were measured at temperatures varying from 25 ?C to 50 ?C. Thermal conductivity ratios, specific heat, dynamic viscosity ratio and density were obtained against varying parameters. The CeO2 - MWCNT/water hybrid nanofluid showed highest efficacy with excellent thermophysical properties and highest Mouromtseff number (MO). A hyper-parameter optimized ANN model predicted the properties of hybrid nanofluids across four thermophysical parameters. The ANN predicted results provide good accuracy with experimental results (R 0.999, MSE < 0.001, Deviations < ? 5%).

Automated summary

The temperature-dependent thermophysical properties of different hybrid nanofluids were investigated in this study, with CeO2-MWCNT/water hybrid nanofluid showing the highest efficacy and excellent thermophysical properties. An optimized artificial neural network (ANN) model accurately predicted the properties of hybrid nanofluids across four thermophysical parameters, with an excellent correlation with experimental results.