Optimization and modeling of thermal conductivity and viscosity of Cu/engine oil nanofluids by NSGA‐II using RSM

Abstract

This study provides the optimization of the thermophysical properties of Cu/engine oil nanofluid. In this optimization, the objective functions were determined using response surface methodology (RSM) to analyze the experimental data of nanofluid viscosity and thermal conductivity (TC). Two equations are presented for the accurate prediction of TC and viscosity data. The nondominated sorting genetic algorithm II (NSGA‐II) method was used for multi‐objective optimization (Mo‐O), and Pareto's front was introduced to study optimal viscosity and TC. According to the results, the highest TC and the lowest viscosity occur when the temperature and solid volume fraction of the nanofluid are at their maximum values.