Use the Controller-based AlgorithmThis optimization task is a standard bead optimization task. The set-up including starting a BEAD_CONTROLLER task, loading the model and definition of design area, objective function, bead height constraint as well as additional optimization settings can be done as described in the example in the Getting Started Manual. The optimization result looks as follows: The first eigenvalue is only slightly higher 0.3140 (1.3%) after the controller-based optimization. The eigenvalue will NOT become higher if you change the number of iterations for the controller approach! Use the Sensitivity-based AlgorithmThis optimization task is a standard bead optimization task. The set-up including starting a BEAD_CONTROLLER task, loading the model and definition of design area, objective function, bead height constraint as well as additional optimization settings can be done as described in the example in the Getting Started Manual. The optimization result looks as follows: Result is a final eigenvalue of 0.3558 (15%), which may even become higher if one lets the optimization continue for more than 20 iterations. |