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Pict. 4.1.1 – modeFRONTIER’s workflow
Picture 4.2.1 – History chart SX
block XY displacement. The constraint conditions have to be
understood with a little tolerance in displacement. In fact,
every constraint allows a clearance to avoid stress
concentration due to an over- constrained condition.
Applying the temperature field to the modeled system as
described before, we continue with the structural simulation
to calculate the stress on and the deformation of the examined
component.
In order to avoid value distortion, due to mesh problems,
instead of considering maximum and minimum values, we
have taken into account a mean value of this quantity close
to the glass constraints.
4 Optimization of the glass support
The initial model described previously has been parametrized
to allow the management by modeFRONTIER; the described
parameters refer to the dimensions of the upper and lower
glass constraints. While we focused on these constraints, the
distances from the left and right glass edges and their width
were parametrized.
The aim of this step was to define an optimum set-up of the
constraint system that minimizes the glass deformations in
pyrolysis conditions.
Newsletter EnginSoft Year 6 n°4 - 19
4.1 Project set-up in modeFRONTIER
Variables used in this first optimization sub-step are therefore
four and each couple refers to the dimension of a constraint.
The constraints on the glass are four, symmetrical, and hence
it is sufficient to modify the dimensions of only one to modify
the couple: these will be the variables of the optimization.
Lower and upper bounds of all the variables were set according
to the customer’s requirements.
By using modeFRONTIER, we want to manage the entire FEM
(ANSYS) process automatically, to obtain the desired results.
To interface the FEM model with the optimizer, some macros
were built, or rather a series of pre- and post-processing
instructions to modify the geometry of the model during each
simulation.
During the set-up of the optimization, some factors, such as
time for each calculation or maximum available time have to
be taken into account in order to define the best strategy.
In this project, the time for each calculation was about 75
minutes, not negligible; this made us choose a genetic
algorithm that has a good robustness to find the optimum.
The objectives were:
Minimization SXZ shear stress;
Minimization SX normal stress;
Minimization SZ normal.
The chosen algorithm was the MOGA (Multi Objective Genetic
Algorithm), starting from an initial random population (DOE)
of the input variables domain.
Simulation parameters:
MOGA iterations: 10
DOE dimensions: 12 - variables number multiplied for
objectives
With these settings we have to do 120 runs for a total run
time of 150 hours
4.2 Optimization results
After the optimization process, a good convergence of results
was achieved: values of shear and stresses decreased up to
40% with respect to the original configuration.
Picture 4.2.1 shows an example of the history charts of
stresses SX.
As this is a multi-objective optimization, optimum results are
more than one: in fact, we could have some designs which
achieve the first objective, but are very far from the other
objectives. Hence we are looking for the best tradeoff!
In this job, all three objective are very correlated, so the
convergence is parallel, which allows us to choose two
optimal designs.
From the obtained results we can extract some important
information about the component behavior in real working
conditions, especially with regard to the glass constraints
dimension and their dispersion across the oven door:
Distance of the lower constraint from the edge of the
glass seems to have no influence on stresses;
Width of lower constraint should be bigger than original;
Distance of the upper constraint from the edge of the
glass seems to have no influence on stresses;
Width of upper constraint should be smaller than original;