Uncertainty Propagation Combining Robust Condensation and Generalized Polynomial Chaos Expansion

Abstract

Among probabilistic uncertainty propagation methods, the generalized Polynomial Chaos Expansion (gPCE) has recently shown a growing emphasis. The numerical cost of the non-intrusive regression method used to compute the gPCE coefficient depends on the successive Latin Hypercube Sampling (LHS) evaluations, especially for large size FE models, large number of uncertain parameters, presence of nonlinearities and when using iterative techniques to compute the dynamic responses. To overcome this issue, the regression technique is coupled with a robust condensation method adapted to the Craig-Bampton component mode synthesis approach leading to computational cost reduction without significant loss of accuracy. The performance of the proposed method and its comparison to the LHS simulation are illustrated by computing the time response of a structure composed of several coupled-beams containing localized nonlinearities and stochastic design parameters.