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

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.

Automated summary

This study optimized the thermophysical properties of Cu/engine oil nanofluid using response surface methodology and non-dominated sorting genetic algorithm II. The results showed the highest TC and lowest viscosity occur when the temperature and solid volume fraction of the nanofluid are at their maximum values.