Applying knowledge management in optimal modeling of viscosity of nanofluids by response surface methodology for use in automobiles engine

Abstract

In this study, using knowledge management and RSM, modeling and prediction of viscosity of oil-based hybrid nanofluid (HNF) with Multi-walled carbon nanotube (MWCNT) and Al2O3 nanoparticles in temperature conditions of 25–50 °C and solid volume fraction of SVF=0.0625–1% have been addressed. To find the most accurate model for predicting viscosity, Cubic, Quartic, and Fifth models were examined in terms of various model accuracy evaluation parameters. Based on the obtained results, the Fifth model has the most accuracy in terms of coefficient of determination and coefficient of variance with values of 0.9997 and 1.10, respectively. Based on the correlation presented to predict viscosity due to the dependence of viscosity on shear rate, the behavior of HNF is non-Newtonian. The graphs of temperature in terms of SVF for viscosity in all three models show that viscosity decreases with increasing temperature and viscosity increases with increasing SVF. For hot lubrication conditions, the best condition (255.28 mPa.sec) is at 35 °C and SVF is 0.161%. Also, knowledge management is considered as a safe and optimal method to increase efficiency, productivity and effectiveness in the field of nanofluids (NFs), which brings high added value to this sector.