Optimization, modeling and accurate prediction of thermal conductivity and dynamic viscosity of stabilized ethylene glycol and water mixture Al2O3 nanofluids by NSGA-II using ANN. (March 2017)

• Record Type: Journal Article
• Authors: Hemmat Esfe, Mohammad; Razi, Peyman; Hajmohammad, Mohammad Hadi; Rostamian, Seyed Hadi; Sarsam, Wail Sami; Abbasian Arani, Ali Akbar; Dahari, Mahidzal
• Is Part Of: International communications in heat and mass transfer. Volume 82(2017)
• Page Start: 154
• Page End: 160
• Publication Date: 2017-03
• Subjects: Multi-objective optimization -- Neural network -- Nanofluids -- Thermal conductivity -- Viscosity; Heat -- Transmission -- Periodicals; Mass transfer -- Periodicals; Chaleur -- Transmission -- Périodiques; Transfert de masse -- Périodiques; Heat -- Transmission; Mass transfer; Periodicals; 621.4022
• DOI: 10.1016/j.icheatmasstransfer.2016.08.015
• Languages: English
• ISSNs: 0735-1933

Abstract

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 thermal conductivity of nanofluids, 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.