| Authors | Mohammad Hemmat Esfe, Peyman Razi, Mohammad Hadi Hajmohammad, Seyed Hadi Rostamian, Wail Sami Sarsam, Ali Akbar Abbasian Arani, Mahidzal Dahari |
|---|---|
| Journal | INT COMMUN HEAT MASS |
| Presented by | University of Kashan |
| Page number | 154–160 |
| Volume number | 82 |
| Paper Type | Full Paper |
| Published At | 2016 |
| Journal Grade | ISI |
| Journal Type | Electronic |
| Journal Country | Iran, Islamic Republic Of |
Abstract
In this study, multi-objective optimization of nanofluid aluminum oxide in a mixture of water and ethylene glycol (40:60) is studied. In order to reduce viscosity and increase thermalconductivityofnanofluids, NSGA-II algorithm is used to alter the temperature and volume fraction of nanoparticles. Neural network modeling of experimental data is used to obtain the values of viscosity and thermal conductivity on temperature and volume fraction of nanoparticles. In order to evaluate the optimization objective functions, neural network optimization is connected to NSGA-II algorithm and at any time assessment of the fitness function, the neural network model is called. Finally, Pareto Front and the corresponding optimum points are provided and introduced. Optimal results showed that the optimum viscosity and thermal conductivity occurs at maximum temperature.