Fuzzy modeling and optimization for experimental thermophysical properties of water and ethylene glycol mixture for Al2O3 and TiO2 based nanofluids

The current study aims to enhance the performance of nanofluid mixture by determining the optimal operating parameters using particle swarm optimization. More specifically, the use of aluminum oxide (Al2O3) and titanium dioxide (TiO2) nanopartides dispersed in distilled water and ethylene glycol with 50:50 volumetric proportions are investigated to enhance the thermophysical properties. The nanofluid properties were measured using different volume fractions (0.05 & 03 vol%) and a temperature ranging from (25-70 degrees C). The effect of surfactant on the stability and thermophysical properties of the metal oxide based nanofluids were also investigated. With the help of the experimental data sets, the nanofluid model was constructed using fuzzy logic, and then the optimal operating parameters are identified using particle swarm optimization. In the optimization procedure, three parameters; temperature, and the volume fractions of both Al2O3 and different operating parameters are used as decision variables. TiO2. The effect of these three operating parameters on the mixtures density, viscosity, and thermal conductivity is studied. Applying the proposed methodology resulted in obtaining the best condition that produces the optimal output that can minimize both the density and viscosity and at the same time maximizes the thermal conductivity. (C) 2019 Elsevier B.V. All rights reserved.