Thermophysical properties of water, water and ethylene glycol mixture-based nanodiamond + Fe3O4 hybrid nanofluids: An experimental assessment and application of data-driven approaches

Abstract

This paper aims to study the thermophysical properties of water, water and ethylene glycol mixture-based nanodiamond + Fe3O4 hybrid nanofluids experimentally and numerically using two data-driven approaches, namely, Multivariate linear regression (MLR) and Multivariate linear regression with interaction (MLRI) models. Three types of base fluids, such as (i) water, (ii) 40:60% water and ethylene glycol mixture, and (iii) 60:40% water and ethylene glycol mixture, were used to prepare the hybrid nanofluids. For all the base fluid, ϕ is used as 0.05%, 0.1%, and 0.2%. Results indicate that, higher values of thermal conductivity and viscosity is 17.76% and 72.9% at ϕ = 0.2% and 60 °C in comparison to water data. Similarly, for 40:60% water and ethylene glycol mixturebased nanodiamond + Fe3O4 hybrid nanofluid, the maximum thermal conductivity and viscosity enhancements are 14.65% and 79.01% at ϕ = 0.2% and at 60 °C compared to the base fluid. However, the maximum thermal conductivity and viscosity enhancements for ϕ = 0.2% and at 60 °C of 60:40% water and ethylene glycol mixture-based nanodiamond + Fe3O4 hybrid nanofluid are 12.79% and 50.84% over the basefluid data. Empirical and data-driven correlations were proposed based on the experimental data. The developed data-driven models resulted in robust individual relationships to predict the thermophysical properties of all types of hybrid nanofluids by superior performance compared to corresponding empirical correlations. The reported results exhibited that for thermal conductivity, density, Specific heat, and viscosity estimation, the MLRI (R = 0.9996), MLR (R = 0.99989), MLR (R = 0.9999998), and MLRI (R = 0.9857) had the best predictive performance.