Uncertainty quantification for viscoelastic composite structures in computational linear structural dynamics

被引：0
|
作者：
Capillon, R. [1 ]
Desceliers, C. [1 ]
Soize, C. [1 ]
机构：
[1] Univ Paris Est, CNRS, MSME, UMR 8208, 5 Bd Descartes, F-77454 Marne La Vallee, France
来源：
PROCEEDINGS OF ISMA2016 INTERNATIONAL CONFERENCE ON NOISE AND VIBRATION ENGINEERING AND USD2016 INTERNATIONAL CONFERENCE ON UNCERTAINTY IN STRUCTURAL DYNAMICS | 2016年
关键词：
MODEL;
D O I：
暂无
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
This paper deals with the analysis of the propagation of uncertainties in computational linear dynamics for strongly dissipative 3D linear viscoelastic composite structures in the presence of parameter uncertainties as well as model uncertainties. The approach used for modeling uncertainties is the probabilistic nonparametric approach, which consists in replacing the matrices of the nominal reduced-order model with random matrices following a probability distribution obtained using information theory. Special care is taken regarding the stochastic modeling of the random reduced stiffness and damping matrices. These two matrices are statistically dependent through a set of compatibility equations, implied by the causality of the system. This set of equations involves Hilbert transforms of the frequency-dependent part of the two matrices and are used in order to generate statistically independent realizations of each random matrix which satisfy the causality principle for the Monte Carlo stochastic solver.
引用
收藏
页码：4241 / 4245
页数：5
相关论文
共 50 条
  • [1] Uncertainty quantification in computational linear structural dynamics for viscoelastic composite structures
    Capillon, R.
    Desceliers, C.
    Soize, C.
    [J]. COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING, 2016, 305 : 154 - 172
  • [2] Model uncertainties in computational viscoelastic linear structural dynamics
    Capillon, R.
    Desceliers, C.
    Soize, C.
    [J]. X INTERNATIONAL CONFERENCE ON STRUCTURAL DYNAMICS (EURODYN 2017), 2017, 199 : 1210 - 1215
  • [3] Multilevel model reduction for uncertainty quantification in computational structural dynamics
    O. Ezvan
    A. Batou
    C. Soize
    L. Gagliardini
    [J]. Computational Mechanics, 2017, 59 : 219 - 246
  • [4] Multilevel model reduction for uncertainty quantification in computational structural dynamics
    Ezvan, O.
    Batou, A.
    Soize, C.
    Gagliardini, L.
    [J]. COMPUTATIONAL MECHANICS, 2017, 59 (02) : 219 - 246
  • [5] AN IMPROVEMENT OF THE UNCERTAINTY QUANTIFICATION IN COMPUTATIONAL STRUCTURAL DYNAMICS WITH NONLINEAR GEOMETRICAL EFFECTS
    Capiez-Lernout, Evangeline
    Soize, Christian
    [J]. INTERNATIONAL JOURNAL FOR UNCERTAINTY QUANTIFICATION, 2017, 7 (01) : 83 - 98
  • [6] Uncertainty quantification in computational structural dynamics: A new paradigm for model validation
    Alvin, KF
    Oberkampf, WL
    Diegert, KV
    Rutherford, BM
    [J]. IMAC - PROCEEDINGS OF THE 16TH INTERNATIONAL MODAL ANALYSIS CONFERENCE, VOLS 1 AND 2, 1998, 3243 : 1191 - 1198
  • [7] Quantification of uncertainty in computational fluid dynamics
    Roache, PJ
    [J]. ANNUAL REVIEW OF FLUID MECHANICS, 1997, 29 : 123 - 160
  • [8] Uncertainty quantification for chaotic computational fluid dynamics
    Yu, Y.
    Zhao, M.
    Lee, T.
    Pestieau, N.
    Bo, W.
    Glimm, J.
    Grove, J. W.
    [J]. JOURNAL OF COMPUTATIONAL PHYSICS, 2006, 217 (01) : 200 - 216
  • [9] Quantification of numerical uncertainty in computational fluid dynamics modelling of hydrocyclones
    Karimi, M.
    Akdogan, G.
    Dellimore, K. H.
    Bradshaw, S. M.
    [J]. COMPUTERS & CHEMICAL ENGINEERING, 2012, 43 : 45 - 54
  • [10] Special Issue: Uncertainty Quantification Computational Fluid Dynamics Preface
    Zang, Thomas A.
    Poroseva, Svetlana
    [J]. THEORETICAL AND COMPUTATIONAL FLUID DYNAMICS, 2012, 26 (05) : 401 - 401
12345