Model identification in computational stochastic dynamics using experimental modal data

Batou, Anas; Soize, Christian; Audebert, S.

doi:10.1016/j.ymssp.2014.05.010

Cited by 25 publications

(17 citation statements)

References 43 publications

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“…One of the goals is to evaluate the ability of some methods, such as the LOK theory and the generalized probabilistic approach of uncertainties [52], to quantify, not only data uncertainties, but also model uncertainties, especially in the case of high values of uncertainties.…”

Section: Resultsmentioning

confidence: 99%

Uncertainties in structural dynamics: overview and comparative analysis of methods

Daouk

Louf

Dorival

et al. 2015

Mechanics & Industry

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-When studying mechanical systems, engineers usually consider that mathematical models and structural parameters are deterministic. However, experimental results show that these elements are uncertain in most cases, due to natural variability or lack of knowledge. Therefore, engineers are becoming more and more interested in uncertainty quantification. In order to improve the predictability and robustness of numerical models, a variety of methods and techniques have been developed. In this work we propose to review the main probabilistic approaches used to model and propagate uncertainties in structural mechanics. Then we present the Lack-Of-Knowledge theory that was recently developed to take into account all sources of uncertainties. Finally, a comparative analysis of different parametric probabilistic methods and the Lack-Of-Knowledge theory in terms of accuracy and computation time provides useful information on modeling and propagating uncertainties in structural dynamics.

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Section: Resultsmentioning

confidence: 99%

Uncertainties in structural dynamics: overview and comparative analysis of methods

Daouk

Louf

Dorival

et al. 2015

Mechanics & Industry

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“…He et al [30] proposed a novel method for load bounds identification for uncertain structures in the frequency domain by applying the Moore-Penrose pseudo-inversion and the truncated total least squares (TTLS). Batou et al [31][32][33] used experimental measurements of responses and an uncertain computational model to identify the random loads acting on fuel assemblies. On the basis of Bayesian approach, Zhang and Antoni [34] reconstructed the force and obtained the Bayesian credible intervals, which are built from its posterior probability density function by dealing with model uncertainty and measurement noise.…”

Section: Introductionmentioning

confidence: 99%

Multi-source random excitation identification for stochastic structures based on matrix perturbation and modified regularization method

Zhang

2019

Mechanical Systems and Signal Processing

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In view of the characteristic of pseudo excitation and effects of the regularization parameter  on the residual norm and solution norm, a novel modified regularization method is proposed to solve the ill-posed problem and mitigate the error propagation of random dynamic loads identification. Compared with Moore-Penrose pseudo inverse and Tikhonov regularization methods that are sensitive to the selection of measurement locations, the proposed modified regularization method can always identify the loads accurately and stably regardless of measurement locations. In addition, the identified loads always match the actual ones from low to high frequency domains using the proposed modified regularization method. Furthermore, the matrix perturbation method is combined with the modified regularization method to analyze the multisource loads acting on the uncertain structure. Several practical engineering examples are conducted to demonstrate that the lower and upper bounds of identified forces can be obtained, which clearly validates the effectiveness and feasibility of the proposed methods in the application of complicated structures.

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“…The objective of this paper is to present the numerical analysis, the computational aspects, and the validation of an extension (recently proposed in [6,7]) of the nonparametric probabilistic approach of uncertainties [2,5,8] in computational linear structural dynamics for viscoelastic composite structures. The proposed methodology, which is devoted to the development of a nonparametric probabilistic tool for the stochastic modeling of the uncertainties in computational viscoelastic models, is a strict extension of the previous works, in particular those devoted to experimental validations (see for instance [9,10,11,12,13,14,15,16,17,18,19,20,21] and references included).…”

Section: Introductionmentioning

confidence: 99%

Uncertainty quantification in computational linear structural dynamics for viscoelastic composite structures

Capillon

Desceliers

Soize

2016

Computer Methods in Applied Mechanics and Engineering

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This paper deals with the analysis of a stochastic reduced-order computational model in computational linear dynamics for linear viscoelastic composite structures in the presence of uncertainties. The computational framework proposed is based on a recent theoretical work that allows for constructing the stochastic reduced-order model using the nonparametric probabilistic approach. In the frequency domain, the generalized damping matrix and the generalized stiffness matrix of the stochastic computational reduced-order model are random matrices. Due to the causality of the dynamical system, these two frequencydependent random matrices are statistically dependent and are linked by a compatibility equation induced by the causality of the system, involving a Hilbert transform. The computational aspects related to the nonparametric stochastic modeling of the reduced stiffness matrix and the reduced damping matrix that are frequency-dependent random matrices are presented. A dedicated numerical approach is developed for obtaining an efficient computation of the Cauchy principal value integrals involved in those equations for which an integration over a broad frequency domain is required. A computational analysis of the propagation of uncertainties is conducted for a composite viscoelastic structure in the frequency range. It is shown that the uncertainties on the damping matrix have a strong influence on the observed statistical dispersion of the stiffness matrix.

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Model identification in computational stochastic dynamics using experimental modal data

Cited by 25 publications

References 43 publications

Uncertainties in structural dynamics: overview and comparative analysis of methods

Uncertainties in structural dynamics: overview and comparative analysis of methods

Multi-source random excitation identification for stochastic structures based on matrix perturbation and modified regularization method

Uncertainty quantification in computational linear structural dynamics for viscoelastic composite structures

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