Convergence criteria in optistruct

Punith

Hello,

 how can I see the convergence criteria in optistruct output file? Is there any control card to set that.? Also I am running a topology optimisation of the wheel disc. I have taken a static analysis and I have a load case where there is a load acting in only one direction. In the analysis I have the max stresses in the applied direction only, ie at only one ventilation hole edge. So while moving to optimisation how can I set this stress distribution throughout 360 degree of the disc? Can I use extrusion constraint and define a node path in a circular direction? I already used it but wanted to make sure if this is the right way.

 

Thanks,

Punith Guptha

Altair Forum User

Hi Punith,

 

You can make out whether it is a soft convergence or regular by using iterative messages.

 

From help:

 

Two convergence tests are used in OptiStruct and satisfaction of only one of these tests is required.

Regular convergence (design is feasible) is achieved when the convergence criteria are satisfied for two consecutive iterations.  This means that for two consecutive iterations, the change in the objective function is less than the objective tolerance and constraint violations are less than 1%.  At least three analyses are required for regular convergence, as the convergence is based on the comparison of true objective values (values obtained from an analysis at the latest design point).  An exception (design is infeasible) is when the constraints remain violated by more than 1%, and for three consecutive iterations the change in the objective function is less than the objective tolerance and the change in the constraint violations is less than 0.2%.  In this case, the iterative process will be terminated with a conclusion ‘No feasible design can be obtained.’

Soft convergence is achieved when there is little or no change in the design variables for two consecutive iterations.  It is not necessary to evaluate the objective (or constraints) for the final design point, as the model is unchanged from the previous iteration.  Therefore, soft convergence requires one less iteration than regular convergence.