In this study, we propose an effective method to estimate the reliability of finite element models reduced by the automated multi-level substructuring (AMLS) method. The proposed error estimation method can accurately predict relative eigenvalue errors in reduced finite element models. A new, enhanced transformation matrix for the AMLS method is derived from the original transformation matrix by properly considering the contribution of residual substructural modes. The enhanced transformation matrix is an important prerequisite to develop the error estimation method. Adopting the basic concept of the error estimation method recently developed for the Craig-Bampton method, an error estimation method is developed for the AMLS method. Through various numerical examples, we demonstrate the accuracy of the proposed error estimation method and explore its computational efficiency.
In this paper, the accuracy of the Craig-Bampton method, one of the most widely used component mode synthesis methods, is improved. Considering the higher-order effect of residual modes that are simply truncated in the Craig-Bampton method, the original finite element model can be more accurately reduced. In this formulation, unknown eigenvalues are considered as additional generalized coordinates, which can be eliminated by employing the concept of system equivalent reduction expansion process. The new component mode synthesis is named the higher-order Craig-Bampton method. The formulation of the higher-order Craig-Bampton method is presented, and its improved accuracy is demonstrated through various examples.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.