Uncertainty Propagation in Dynamic Sub-structuring by Model Reduction Integrated Domain Decomposition

T Chatterjee, S Adhikari & MI Friswell (Swansea University)

Computer Methods in Applied Mechanics and Engineering, Vol. 366, 1 July 2020, paper 113060


This paper addresses computational aspects in dynamic sub-structuring of built-up structures with uncertainty. Component mode synthesis (CMS), which is a model reduction technique, has been integrated within the framework of domain decomposition (DD), so that reduced models of individual sub-systems can be solved with smaller computational cost compared to solving the full (unreduced) system by DD. This is particularly relevant for structural dynamics applications where the overall system physics can be captured by a relatively low number of modes. The theoretical framework of the proposed methodology has been extended for application in stochastic dynamic systems. To limit the number of eigenvalue analyses to be performed corresponding to the random realizations of input parameters, a locally refined high dimensional model representation model with stepwise least squares regression is presented. Effectively, a bi-level decomposition is proposed, one in the physical space and the other in the stochastic space. The geometric decomposition in the physical space by the proposed model reduction-based DD reduces the computational cost of a single analysis of the system and the functional decomposition in the stochastic space by the proposed meta-model lowers the number of simulations to be performed on the actual system. The results achieved by solving a finite-element model of an assembled beam structure and a 3D space frame illustrate good performance of the proposed methodology, highlighting its potential for complex problems.

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This material has been published in Computer Methods in Applied Mechanics and Engineering, Vol. 366, 1 July 2020, paper 113060, the only definitive repository of the content that has been certified and accepted after peer review. Copyright and all rights therein are retained by Elsevier.

Link to paper using doi: 10.1016/j.cma.2020.113060

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