Thermal conductivity and dynamic viscosity play key role in heat transfer capacity
of nanofluids. In the present study, thermal conductivity and dynamic
viscosity of Fe2O3/water are modeled by applying various artificial neural network
algorithms. The applied algorithms are MLP, GA-RBF, LSSVM, and
CHPSO ANFIS algorithms. The data for modeling procedure are extracted
from several experimental studies. Obtained results by the different algorithms
are compared and it was concluded that the highest R-squared values
belonged to GA-RBF algorithm which were equal to 0.9962 and 0.9982 for
thermal conductivity ratio and dynamic viscosity, respectively.
