WASET
	%0 Journal Article
	%A Hajir Karimi and  Fakheri Yousefi and  Mahmood Reza Rahimi
	%D 2011
	%J International Journal of Chemical and Molecular Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 49, 2011
	%T Correlation of Viscosity in Nanofluids using Genetic Algorithm-neural Network (GA-NN)
	%U https://publications.waset.org/pdf/604
	%V 49
	%X An accurate and proficient artificial neural network
(ANN) based genetic algorithm (GA) is developed for predicting of
nanofluids viscosity. A genetic algorithm (GA) is used to optimize
the neural network parameters for minimizing the error between the
predictive viscosity and the experimental one. The experimental
viscosity in two nanofluids Al2O3-H2O and CuO-H2O from 278.15
to 343.15 K and volume fraction up to 15% were used from
literature. The result of this study reveals that GA-NN model is
outperform to the conventional neural nets in predicting the viscosity
of nanofluids with mean absolute relative error of 1.22% and 1.77%
for Al2O3-H2O and CuO-H2O, respectively. Furthermore, the results
of this work have also been compared with others models. The
findings of this work demonstrate that the GA-NN model is an
effective method for prediction viscosity of nanofluids and have
better accuracy and simplicity compared with the others models.
	%P 53 - 60