A new empirical model for accurate investigation of rheological behavior of MWCNT/oxide nanoparticles-engine oil hybrid nano-lubricants

Abstract

One of the most fundamental properties of nanofluids is their viscosity. To determine the viscosity of these fluids, many models based on experimental or theoretical studies have been developed. However, the bulk of these models either offer outcomes that are inaccurate or are restricted to a certain operating condition. As a result, determining the viscosity of nanofluids is a difficult undertaking. In this work, a novel model based on Least Squares Support Vector Machines (LSSVM) was provided to predict the viscosity of hybrid nano-lubricants of MWCNT-metal oxide nanoparticles/engine oil. Metal oxide nanoparticles including Al2O3, ZnO, SiO2, TiO2, CuO, and MgO, and engine oils including 5W50, 10W40, 20W50, SAE40, and SAE50 were considered in the studied hybrid nano-lubricants. The reliability of the proposed model was determined by comparing its outputs to experimental data obtained at various temperatures, nanoparticles’ concentrations, shear rates, and solid volume fractions. Additionally, the LSSVM model's coefficient of determination for all data was found to be 0.99649, demonstrating its high accuracy. Moreover, using the Monte-Carlo technique, the sensitivity analysis of the input variables on the output was computed which showed that the most sensitive variables for this model are shear rate, lubricant density, and nanoparticles volume fraction. This work highlights the requirement for a trustworthy approach to determine the viscosity of hybrid nano-lubricants and suggests one.