Multi-objective optimization of viscosity and thermal conductivity of TiO2/ BioGlycol-water nanofluids with sorting non-dominated genetic algorithm II coupled with response surface methodology

The study aimed to optimize of viscosity and thermal conductivity (TC) properties of TiO2/BG-Water (20:80) and TiO2/BG-Water (30:70) nanofluids (NFs). In this study, simulation methods (NSGA-II, RSM, ANN) are used with the aim of faster and more accurate results, saving time and exorbitant laboratory costs. TC and viscosity of TiO2/ BG-Water (20:80) NF in RSM have regression coefficients R2 = 0.9913 and R2 = 0.9982, respectively. For TiO2/ BG-Water (30:70) NF in RSM, TC and viscosity have regression coefficients R2 = 0.9911 and R2 = 0.9993, respectively. These results indicate the high accuracy of the predicted model. Using ANN, the MLP structure with the best efficiency for each NFs is introduced. In the MLP model, TC and viscosity of TiO2/BG-Water (20:80) NF have regression coefficient R2 = 0.9997 and TC and viscosity of TiO2/BG-Water (30:70) NF have regression coefficient R2 = 0.9998. Finally, after defining the variables and objective functions in the NSGA-II method, optimization of multi-objective was performed using RSM and MOPSO. Pareto fronts are introduced to evaluate optimal viscosity and TC responses. The results of the Pareto front show that optimum TC and viscosity of NFs occur when T and solid volume fraction (SVF) are at their maximum. The exact results of this study from the prediction and optimization of TC and viscosity of these NFs, can help craftsmen to better understand the properties of heat transfer.