In the optimization task editor, click the
Density tab.
Select the Density update strategy.
This setting controls the rate at which the
Optimization module
updates the relative material density of design elements during the
optimization. In most cases you should accept the default setting
(Normal). However, if the design responses are very
sensitive and you have problems fulfilling the constraints, you may need a more
conservative rate that requires more optimization iterations.
Do either of the following to specify the relative density of each
element during the initial optimization iteration:
-
Select Optimization product default to allow
the
Optimization module
to determine the initial density. If the material volume is selected as a
constraint, the
Optimization module
calculates the initial density such that the volume constraint is fulfilled
exactly. If the material volume is selected as an objective function, each
element has an initial relative density of 50%.
-
Select Specify and enter a value (0.0 <
initial density ≤ 1.0). You should use this option
only if volume is selected as an objective function and not as a constraint and
if you know, prior to the optimization, that setting the initial density to a
larger or smaller value will fulfill other constraints; for example,
displacement constraints. You can use a value greater that 0.5 in conjunction
with volume constraints to stabilize nonlinear or contact problems and to
improve the convergence behavior.
Enter the Minimum density, the Maximum
density, and the Maximum change per design
cycle.
The minimum density must be greater than 0.0, and the maximum density
must be less than or equal to 1.0. Changing the density bounds is not
recommended, in particular the upper bound. You may need to increase the lower
bound if the default value leads to a nearly singular stiffness matrix.
Numerical experiments indicate that a value of 0.25 (default) is
acceptable for the maximum change in density. A lower limit in the change of
density, such as 0.1, is recommended for complicated design responses and
optimization formulations. However, a lower limit often leads to a higher
number of optimization iterations.
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