Optimization, Modeling of Thermal Conductivity and Viscosity of
Cu/Engine Oil Nanofluids by NSGA-II Using RSM
Abstract
This study provides the optimization of thermophysical properties of
Cu/engine oil nanofluid. In this optimization, the objective functions
were determined with the experimental data of viscosity and TC of
nanofluid using RSM. Two equations for predicting thermal conductivity
(TC) and viscosity data were presented which can accurately predict
these properties. The NSGA-II method was used for multi-objective
optimization (Mo-O) and Pareto’s front was introduced to study optimal
viscosity and TC responses. According to the results, the highest TC and
the lowest viscosity occurs when the temperature and solid volume
fraction (SVF) of the nanoparticle are at their maximum values. Among
the results, those with the highest TC and the lowest viscosity are
referred to as optimal points.