A reduction methodology using free-free component eigenmodes and Arnoldi enrichment
For engineers performing modal analysis in industrial contexts, this offers a more efficient and reusable reduced-order modeling approach.
This paper develops a reduction methodology for building updatable reduced-order models whose size is independent of interface size, using free-free component eigenmodes and Arnoldi enrichment. The method achieves accuracy comparable to Craig-Bampton with smaller, partially updatable models.
In order to perform faster simulations, the model reduction is nowadays used in industrial contexts to solve large and complex problems. However, the efficiency of such an approach is sometimes cut by the interface size of the reduced model and its reusability. In this article, we focus on the development of a reduction methodology for the build of modal analysis oriented and updatable reduced order model whose size is not linked to their contacting interface. In order to allow latter model readjusting, we impose the use of eigenmodes in the reduction basis. Eventually, the method introduced is coupled to an Arnoldi based enrichment algorithm in order to improve the accuracy of the reduced model produced. In the last section the proposed methodology is discussed and compared to the Craig and Bampton reduction method. During this comparison we observed that even when not enriched, our work enables us to recover the Craig and Bampton accuracy with partially updatable and smaller reduced order model.