Abstract: A major part of the flow field involves no complicated
turbulent behavior in many turbulent flows. In this research work, in
order to reduce required memory and CPU time, the flow field was
decomposed into several blocks, each block including its special
turbulence. A two dimensional backward facing step was considered
here. Four combinations of the Prandtl mixing length and standard k-
E models were implemented as well. Computer memory and CPU
time consumption in addition to numerical convergence and accuracy
of the obtained results were mainly investigated. Observations
showed that, a suitable combination of turbulence models in different
blocks led to the results with the same accuracy as the high order
turbulence model for all of the blocks, in addition to the reductions in
memory and CPU time consumption.
Abstract: In this paper back-propagation artificial neural
network (BPANN) with Levenberg–Marquardt algorithm is
employed to predict the limiting drawing ratio (LDR) of the deep
drawing process. To prepare a training set for BPANN, some finite
element simulations were carried out. die and punch radius, die arc
radius, friction coefficient, thickness, yield strength of sheet and
strain hardening exponent were used as the input data and the LDR
as the specified output used in the training of neural network. As a
result of the specified parameters, the program will be able to
estimate the LDR for any new given condition. Comparing FEM and
BPANN results, an acceptable correlation was found.