Abstract:
By using an FCM-based neuro-fuzzy inference system and genetic algorithm-polynomial
neural network as well as experimental data, two models were established in order to
predict the thermal conductivity ratio of alumina (Al2O3)-water nanofluids. In these
models, the target parameter was the thermal conductivity ratio, and the nanoparticle
volume concentration, temperature and Al2O3 nanoparticle size were considered as the
input (design) parameters. The empirical data were divided into train and test sections
for developing the models. Therefore, they were instructed by 80% of the experimental
data and the remaining data (20%) were considered for benchmarking. The results,
which were obtained by the proposed FCM-based Neuro-Fuzzy Inference System (FCMANFIS)
and Genetic Algorithm-Polynomial Neural Network (GA-PNN) models, were
provided and discussed in detail.
