Heat transfer of Cu–water nanofluid over a
stretching cylinder in the presence of magnetic field has
been investigated. The group method of data handling
(GMDH) type neural networks (NNs) is used to calculate
Nusselt number formulation. Results indicate that GMDHtype
NN in comparison with fourth-order Runge–Kutta
integration scheme provides an effective means of efficiently
recognizing the patterns in data and accurately
predicting a performance. The effects of nanoparticle volume
fraction, magnetic parameter and Reynolds number on
Nusselt number are studied by sensitivity analyses. The
results show that Nusselt number is an increasing function
of Reynolds number and volume fraction of nanoparticles
while it is a decreasing function of magnetic parameter. As
volume fraction of nanoparticles increases, the effect of
this parameter on Nusselt number also increases, but
opposite behavior is obtained for magnetic parameter and
Reynolds number
