MODEL REDUCTION THROUGH ENHANCED CRAIG-BAMPTON TRANSFORMATION AND INTERIOR MODE SELECTION

Abstract eng:
The application of the most advanced engineering techniques, such as model updating, design optimization, real-time control, requires the availability of very accurate and computationally efficient system models. To meet such a need several model reduction techniques have been developed and improved. In recent studies it has been proved that very accurate reduced-order models can be obtained by enhancing the Craig-Bampton method, which is the most implemented reduction technique because of its effectiveness and easiness of implementation. Not only does the accuracy of reduced-order models depend on the strategy adopted but also on the modes retained at the reduction stage. In particular, it has been proved that a proper selection of the interior Craig-Bampton modes to be retained in the reduced-order model can considerably boost the accuracy of "minimum-size" models. In this paper a reduction strategy is proposed that exploits both the benefits of enhanced Craig-Bampton transformation and of an effective mode selection criterion. The latter, in particular, ranks and selects through analytical coefficients the interior modes on the basis of their contribution to the system dynamics, while the first adds the contribution of the neglected interior modes through the enhanced transformation, which is based on the residual flexibility of the neglected modes. The minor increase in the complexity of the Craig Bampton method due to the proposed strategy is compensated by the remarkable improvement of reduced-order models in terms of accuracy and size. The effectiveness of the proposed strategy is validated through a few representative examples.

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