Multi-objective optimization
Neural network
Nanofluids
Thermal conductivity
Viscosity
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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
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