  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.

Numerical Analysis;Computational Engineering, Finance, and Science;Classical Physics