Sensitivity-based bead optimization

The bead optimization method that is based on gradient information is described in this chapter.

Sensitivity-based bead optimization (BEAD_SENSITIVITY) makes it possible to define very complex optimization tasks. It has been shown in industrial size examples that the method is very powerful and attractive, especially for dynamic problems.

The typical problems which can be solved by this algorithm are:

  • Maximize stiffness (linear static)
  • Minimize displacement for critical nodes (linear static)
  • Maximize first eigenvalue (modal)
  • Maximize a certain eigenvalue (using mode tracking)
  • Move eigenvalues away from certain frequency (band gap optimization with modal analysis)

Important:
  • The sensitivity-based algorithm has no bead filter implemented. This means that the results are not necessarily a distinct bead pattern like the results from the controller algorithm.
  • Design nodes must be connected to elements which are supported by SIMULIA Tosca Structure. For more information, see Design variables (DV_BEAD).