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Optimization and modeling of thermal conductivity and viscosity of Cu/engine oil nanofluids by NSGA‐II using RSM
Author(s) -
Esfe Mohammad Hemmat,
Motallebi Sayyid Majid
Publication year - 2021
Publication title -
mathematical methods in the applied sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.719
H-Index - 65
eISSN - 1099-1476
pISSN - 0170-4214
DOI - 10.1002/mma.6953
Subject(s) - nanofluid , viscosity , thermal conductivity , sorting , response surface methodology , multi objective optimization , volume fraction , materials science , thermodynamics , mathematics , volume (thermodynamics) , thermal , mathematical optimization , composite material , algorithm , physics , statistics
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.