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.
Complete list of metadatas

Cited literature [13 references]  Display  Hide  Download

https://hal-upec-upem.archives-ouvertes.fr/hal-01276798
Contributor : Christian Soize <>
Submitted on : Saturday, February 20, 2016 - 2:29:11 PM
Last modification on : Friday, October 4, 2019 - 1:31:45 AM
Long-term archiving on : Saturday, November 12, 2016 - 11:50:11 PM

File

conference-2016-IMAC-Orlando-1...
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01276798, version 1

Collections

Citation

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⟩

Share

Metrics

Record views

478

Files downloads

370