Multilevel model reduction for uncertainty quantification in computational structural dynamics

被引：0
|
作者：
O. Ezvan
A. Batou
C. Soize
L. Gagliardini
机构：
[1] Université Paris-Est,
[2] Laboratoire Modélisation et Simulation Multi Echelle,undefined
[3] MSME UMR 8208 CNRS,undefined
[4] PSA Peugeot Citroën,undefined
[5] Direction Technique et Industrielle,undefined
[6] Centre Technique de Vélizy A,undefined
来源：
Computational Mechanics | 2017年 / 59卷
关键词：
High modal density; Reduced-order model; Uncertainty quantification; Broad frequency band; Structural dynamics;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
This work deals with an extension of the reducedorder models (ROMs) that are classically constructed by modal analysis in linear structural dynamics for which the computational models are assumed to be uncertain. It is based on a multilevel projection strategy consisting in introducing three reduced-order bases that are obtained by using a spatial filtering methodology of local displacements. This filtering involves global shape functions for the kinetic energy. The proposed multilevel stochastic ROM is constructed by using the nonparametric probabilistic approach of uncertainties. It allows for affecting a specific level of uncertainties to each type of displacements associated with the corresponding vibration regime. The proposed methodology is applied to the computational model of an automobile structure, for which the multilevel stochastic ROM is identified with respect to experimental measurements. This identification is performed by solving a statistical inverse problem.
引用
收藏
页码：219 / 246
页数：27
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