Experimental and numerical analysis of rheological characterization of hybrid nano-lubricants containing COOH-Functionalized MWCNTs and oxide nanoparticles

Abstract

In this study, the dynamic viscosity of four different hybrid nano-lubricants was investigated. The base fluid was SAE 40 engine oil. The suspended nanoparticles consisted of 20 Vol% of COOH-Functionalized MWCNTs and 80 Vol% of oxide nanoparticles (SiO2, Al2O3, MgO, and ZnO). The experiments were performed at temperatures between 25 and 50°∁ and solid volume fractions of 0.05, 0.25, 0.50, 0.75 and 1%. In addition, the decision tree, random forest, Support Vector Machine (SVM), and Radial Basis Function Artificial Neural Networks (RBF-ANN) were used to predict the viscosity of prepared nanofluids. The decision tree and random forest methods have the highest accuracy in terms of predicting the dynamic viscosity of prepared nano-lubricants.