Rheological behavior predictions of non-Newtonian nanofluids via correlations and artificial neural network for thermal applications

Nanofluids possess enhanced viscous and thermal features that can be utilized to improve the heat transfer performance of several applications involving sustainable manufacturing and industrial ecology, such as in heating/cooling systems, electronics, transportation etc. Therefore, it is important to understand and optimize the flow pattern of these fluids. This research emphasizes the predictions of viscosity of water/ethylene-glycol (EG) based non-Newtonian nanofluids. Four experiment-based data sets are used to predict and validate the effective viscosity, i.e., Fe₃O₄-Ag/EG, MWCNT-alumina/water-EG, Fe₃O₄-Ag/water-EG, and MWCNT-SiO₂/EG-water, via existing correlations and artificial neural network (ANN). The modeling is based on three input parameters, i.e., particle concentrations, temperatures, and shear rates, and one output parameter, i.e., viscosity. The predicted outcomes are compared to the three existing correlation structures. The error matrix consists of the coefficient of determination (R²), average absolute deviation (AAD %), the sum of squared error (SSE), that are employed to evaluate the performance of the model. Results from ANN are found to be more precise, with R² values greater than 0.99 for all datasets, compared to data fitting into existing correlations, in which Fe₃O₄-Ag/water-EG resulted in an R² value as low as 0.72, to determine the nanofluids’ effective viscosity.