%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