RECENT DEVELOPMENTS IN MODEL REDUCTION METHODS

Abstract eng:
Despite continuing advances in computing power, computational cost has continued to rise due to the rapidly increasing size of finite element (FE) models required in engineering practice. This fact has caused the need to utilize model reduction methods than ever. In model reduction methods, there are many important research issues. Among them, in this presentation, we focus on practical methods to evaluate the reliability of the reduced models and on the development of the high performance reduction methods providing more accurate and efficient reduced models. We first introduce the error estimators for the Craig-Bampton (CB) method, the automated multi-level substructuring (AMLS) method, the Guyan reduction method, and the flexibility-based component mode synthesis (FCMS) method, which are excellent tools to estimate the reliability of the reduced models. We also introduce the recently developed model reduction methods: the enhanced CB (ECB) method, the enhanced AMLS (EAMLS) method, the higher order CB (HCB) method, the improved dual CB method, the algebraic dynamic condensation (ADC) method, and the iterative algebraic dynamic condensation (IADC) method. These methods provide significantly accurate reduced models with computational efficiency. Their basic concepts are introduced and the performance of the methods are demonstrated through several benchmark problems.

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