On the treatment of uncertainties in structural mechanics and analysis

Schuëller, G.I.

doi:10.1016/j.compstruc.2006.10.009

Cited by 165 publications

(87 citation statements)

References 6 publications

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“…In that sense, the analysis of uncertainty in EMC testing using Monte Carlo Methods is an alternative to resolve many of the problems associated to the GUM uncertainty framework, including non-symmetrical measurement uncertainty distributions, non-linearity within the measurement system, input dependency and systematic bias [5]. Nowadays, the Monte Carlo Method (MCM) is recognized as a practical alternative by the Joint Committee for Guides in Metrology (JCGM) of the Bureau International des Poids et Mesureson (BIPM) and it has been included in the GUM as a supplement, since 2008 [6], and it has been widely used within many scientific disciplines, such as metrology, geodesy, optics, hydrology, electronics, structural mechanics, and various others [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Comparison of the Gum and Monte Carlo Methods for the Uncertainty Estimation in Electromagnetic Compatibility Testing

Azpúrua¹,

Tremola²,

Barrios³

2011

PIER B

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Abstract-The rigorous uncertainty estimation in Electromagnetic Compatibility (EMC) testing is a complex task that has been addressed through a simplified approach that typically assumes that all the contributions are uncorrelated and symmetric, and combine them in a linear or linearized model using the error propagation law. These assumptions may affect the reliability of test results, and therefore, it is advisable to use alternative methods, such as Monte Carlo Method (MCM), for the calculation and validation of measurement uncertainty. This paper presents the results of the estimation of uncertainty for some of the most common EMC tests, such as: the measurement of radiated and conducted emissions according to CISPR 22 and radiated (IEC 61000-4-3) and conducted (IEC 61000-4-6) immunity, using both the conventional techniques of the Guide to the Expression of Uncertainty in Measurement (GUM) and the Monte Carlo Method. The results show no significant differences between the uncertainty estimated using the aforementioned methods, and it was observed that the GUM uncertainty framework slightly overestimates the overall uncertainty for the cases evaluated here. Although the GUM Uncertainty Framework proves to be adequate for the particular EMC tests that were considered, generally the Monte Carlo Method has features that avoid the assumptions and the limitations of the GUM Uncertainty Framework.

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Section: Introductionmentioning

confidence: 99%

Comparison of the Gum and Monte Carlo Methods for the Uncertainty Estimation in Electromagnetic Compatibility Testing

Azpúrua¹,

Tremola²,

Barrios³

2011

PIER B

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“…Such an approach is very well adapted and very efficient to take into account the uncertainties on the computational model parameters as soon as the probability theory can be used. Many works have been published in this field and a state-of-the-art can be found, for instance, in [1,2,3,4,5,6,7,8,9,10,11]. Nevertheless, the parametric probabilistic approach does not allow the modeling uncertainties to be taken into account (see for instance [12,13]).…”

Section: Types Of Approach For Stochastic Modeling Of Uncertaintiesmentioning

confidence: 99%

Stochastic modeling of uncertainties in computational structural dynamics—Recent theoretical advances

Soize

2013

Journal of Sound and Vibration

139

106

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To cite this version:Christian Soize. Stochastic modeling of uncertainties in computational structural dynamics -Recent theoretical advances. Journal of Sound and Vibration, Elsevier, 2013, 332 (10) AbstractThis paper deals with a short overview on stochastic modeling of uncertainties. We introduce the types of uncertainties, the variability of real systems, the types of probabilistic approaches, the representations for the stochastic models of uncertainties, the construction of the stochastic models using the maximum entropy principle, the propagation of uncertainties, the methods to solve the stochastic dynamical equations, the identification of the prior and the posterior stochastic models, the robust updating of the computational models and the robust design with uncertain computational models. We present recent theoretical advances in this field concerning the parametric and the nonparametric probabilistic approaches of uncertainties in computational structural dynamics for the construction of the prior stochastic models of both the uncertainties on the computational model parameters and on the modeling uncertainties, and for their identification with experimental data. We also present the construction of the posterior stochastic model of uncertainties using the Bayesian method when experimental data are available.

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“…Many works have been published and a state-of-the-art can be found out, for instance, in Refs. [35][36][37][38][39][40].…”

Section: Types Of Approach For Stochastic Modeling Of Uncertaintiesmentioning

confidence: 99%

Advanced Computational Dissipative Structural Acoustics and Fluid-Structure Interaction in Low-and Medium-Frequency Domains. Reduced-Order Models and Uncertainty Quantification

Ohayon¹,

Soize²

2012

International Journal of Aeronautical and Space Sciences

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This paper presents an advanced computational method for the prediction of the responses in the frequency domain of general linear dissipative structural-acoustic and fluid-structure systems, in the low-and medium-frequency do mains and this includes uncertainty quantification. The system under consideration is constituted of a deformable dissipative structure that is coupled with an internal dissipative acoustic fluid. This includes wall acoustic impedances and it is surrounded by an infinite acoustic fluid. The system is submitted to given internal and external acoustic sources and to the prescribed mechanical forces. An efficient reduced-order computational model is constructed by using a finite element discretization for the structure and an internal acoustic fluid. The external acoustic fluid is treated by using an appropriate boundary element method in the frequency domain. All the required modeling aspects for the analysis of the medium-frequency domain have been introduced namely, a viscoelastic behavior for the structure, an appropriate dissipative model for the internal acoustic fluid that includes wall acoustic impedance and a model of uncertainty in particular for the modeling errors. This advanced computational formulation, corresponding to new extensions and complements with respect to the state-of-the-art are well adapted for the development of a new generation of software, in particular for parallel computers. Key words:Computational mechanics, Structural acoustics, Vibroacoustic, Fluid-structure interaction, Uncertainty quantification, Reduced-order model, Medium frequency, Low frequency, Dissipative system, Viscoelasticity, Wall acoustic impedance, Finite element discretization, Boundary element method Nomenclature a ijkh = elastic coefficients of the structure b ijkh = damping coefficients of the structure c 0 = speed of sound in the internal acoustic fluid c E = speed of sound in the external acoustic fluid f = vector of the generalized forces for the internal acoustic fluid f S = vector of the generalized forces for the structure g = mechanical body force field in the structure i = imaginary complex number i k = wave number in the external acoustic fluid n = number of internal acoustic DOF n s = number of structure DOF n j = component of vector n n = outward unit normal to Abstract This paper presents an advanced computational method for the prediction of the responses in the frequency domain of general linear dissipative structural-acoustic and uid-structure systems, in the low-and medium-frequency domains and this includes uncertainty quantication. The system under consideration is constituted of a deformable dissipative structure that is coupled with an internal dissipative acoustic uid. This includes wall acoustic impedances and it is surrounded by an innite acoustic uid. The system is submitted to given internal and external acoustic sources and to the prescribed mechanical forces. An efcient reduced-order computational model is constructed by using a nite element discret...

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On the treatment of uncertainties in structural mechanics and analysis

Cited by 165 publications

References 6 publications

Comparison of the Gum and Monte Carlo Methods for the Uncertainty Estimation in Electromagnetic Compatibility Testing

Comparison of the Gum and Monte Carlo Methods for the Uncertainty Estimation in Electromagnetic Compatibility Testing

Stochastic modeling of uncertainties in computational structural dynamics—Recent theoretical advances

Advanced Computational Dissipative Structural Acoustics and Fluid-Structure Interaction in Low-and Medium-Frequency Domains. Reduced-Order Models and Uncertainty Quantification

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