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Uncertainty quantification for nonlinear reduced-order elasto-dynamics computational models

Abstract : The present work presents an improvement of a computational methodology for the uncertainty quantifi-cation 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 adress 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 linear and nonlinear mistuning of an industrial integrated bladed-disk.
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Submitted on : Saturday, February 20, 2016 - 2:29:11 PM
Last modification on : Thursday, September 29, 2022 - 2:21:15 PM
Long-term archiving on: : Saturday, November 12, 2016 - 11:50:11 PM


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  • HAL Id : hal-01276798, version 1



Evangéline Capiez-Lernout, Christian Soize, M Mbaye. Uncertainty quantification for nonlinear reduced-order elasto-dynamics computational models. IMAC-XXXIV, A Conference and Exposition on Structural Dynamics, SEM/IMAC, Jan 2016, Orlando, Fl, United States. pp.1-10. ⟨hal-01276798⟩



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