An improvement of the uncertainty quantification in computational structural dynamics with nonlinear geometrical effects

Abstract : In this work, we present an improvement of a computational methodology for the uncertainty quantification of structures in presence of geometric nonlinearities. The implementation of random uncertainties is carried out through the nonparametric probabilistic framework from a nonlinear reduced-order model. With such usual modeling, it is difficult to analyze the influence of uncertainties on the nonlinear part of the operators with respect to its linear counterpart. In order to address this problem, an approach is proposed to take into account uncertainties for both the linear and the nonlinear operators. The methodology is then validated in the context of the nonlinear post-buckling of a cylindrical shell and in the context of a nonlinear mistuned industrial integrated bladed-disk.
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International Journal for Uncertainty Quantification, Begell House Publishers, 2017, 7 (1), pp.83-98. 〈10.1615/Int.J.UncertaintyQuantification.2016019141〉
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Evangéline Capiez-Lernout, Christian Soize. An improvement of the uncertainty quantification in computational structural dynamics with nonlinear geometrical effects. International Journal for Uncertainty Quantification, Begell House Publishers, 2017, 7 (1), pp.83-98. 〈10.1615/Int.J.UncertaintyQuantification.2016019141〉. 〈hal-01465405〉

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