Fast computing techniques for Bayesian uncertainty quantification in structural dynamics

Abstract

A Bayesian probabilistic framework for uncertainty quantification and propagation in structural dynamics is reviewed. Fast computing techniques are integrated with the Bayesian framework to efficiently handle large-order models of hundreds of thousands or millions degrees of freedom and localized nonlinear actions activated during system operation. Fast and accurate component mode synthesis (CMS) techniques are proposed, consistent with the finite element (FE) model parameterization, to achieve drastic reductions in computational effort when performing a system analysis. Additional substantial computational savings are also obtained by adopting surrogate models to drastically reduce the number of full system re-analyses and parallel computing algorithms to efficiently distribute the computations in available multi-core CPUs. The computational efficiency of the proposed approach is demonstrated by updating a high-fidelity finite element model of a bridge involving hundreds of thousands of degrees of freedom. © The Society for Experimental Mechanics, Inc. 2013.