Correlation of Viscosity in Nanofluids using Genetic Algorithm-neural Network (GA-NN)

Abstract

An accurate and efficient artificial neural network based on genetic algorithm (GA) is developed for predicting of nanofluids viscosity. The genetic algorithm (GA) is used to optimize the neural network parameters. The experimental viscosity in eight nanofluids in the range 238.15–343.15 K with the nanoparticle volume fraction up to 9.4% was used. The obtained results show that the GA-NN model has a good agreement with the experimental data with absolute deviation 2.48% and high correlation coefficient (R ≥ 0.98). The Results also reveals that GA-NN model outperforms to the conventional neural nets in predicting the viscosity of nanofluids with the overall percentage improvement of 39%. Furthermore, the results have also been compared with Einstein, Batchelor and Masoumi et al. models. The findings demonstrate that this model is an efficient method and have better accuracy.