The radial basis function neural network (RBFNN) simulation has been designed to simulate and predict
the mean velocity of capillary flow in transition from laminar to turbulent flow and the root-mean-square
vorticity as a function of wall-normal position at different values of Reynolds number. The system was
trained on the available data of the two cases. Therefore, we designed the system to work in automatic
way for finding the best network that has the ability to have the best test and prediction. The proposed
system shows an excellent agreement with that of an experimental data in these cases. The technique has
been also designed to simulate the other distributions not presented in the training set and predicted them
with effective matching.