Uncertainty-based experimental validation of nonlinear reduced order models

Murthy, Raghavendra; Wang, X.Q.; Perez, Ricardo A.; Mignolet, Marc P.; Richter, Lanae A.

doi:10.1016/j.jsv.2011.10.022

Cited by 19 publications

(21 citation statements)

References 14 publications

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“…The resulting stochastic nonlinear computational model is characterized by a single scalar dispersion parameter, quantifying the level of uncertainty in the stiffness properties which can easily be identified with experiments. Experimental validations based on this theory can be found in [31,32] for slender elastic bodies, e.g. beams.…”

Section: Introductionmentioning

confidence: 99%

Post-buckling nonlinear static and dynamical analyses of uncertain cylindrical shells and experimental validation

Capiez‐Lernout

Soize

Mignolet

2014

Computer Methods in Applied Mechanics and Engineering

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The paper presents a complete experimental validation of an advanced computational methodology adapted to the nonlinear post-buckling analysis of geometrically nonlinear structures in presence of uncertainty. A mean nonlinear reduced-order computational model is first obtained using an adapted projection basis. The stochastic nonlinear computational model is then constructed as a function of a scalar dispersion parameter, which has to be identified with respect to the nonlinear static experimental response of a very thin cylindrical shell submitted to a static shear load. The identified stochastic computational model is finally used for predicting the nonlinear dynamical post-buckling behavior of the structure submitted to a stochastic ground motion.

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Section: Introductionmentioning

confidence: 99%

Post-buckling nonlinear static and dynamical analyses of uncertain cylindrical shells and experimental validation

Capiez‐Lernout

Soize

Mignolet

2014

Computer Methods in Applied Mechanics and Engineering

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“…A similar coupling but of the structural dynamics and thermal aspects, the two in reduced order model format, has also been proposed and validated in [18][19][20][21]. In addition, validation studies with experiments have been carried out for different types of panels [3,[23][24]. The introduction of uncertainty in the reduced order model has finally been formulated and implemented [25,26].…”

Section: Introductionmentioning

confidence: 99%

Prediction of displacement and stress fields of a notched panel with geometric nonlinearity by reduced order modeling

Perez

Wang

Mignolet

2014

Journal of Sound and Vibration

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The focus of this investigation is on a first assessment of the predictive capabilities of nonlinear geometric reduced order models for the prediction of the large displacement and stress fields of panels with localized geometric defects, the case of a notch serving to exemplify the analysis. It is first demonstrated that the reduced order model of the notched panel does indeed provide a close match of the displacement and stress fields obtained from full finite element analyses for moderately large static and dynamic responses (peak displacement of 2 and 4 thicknesses). As might be expected, the reduced order model of the virgin panel would also yield a close approximation of the displacement field but not of the stress one. These observations then lead to two "enrichment" techniques seeking to superpose the notch effects on the virgin panel stress field so that a reduced order model of the latter can be used. A very good prediction of the full finite element stresses, for both static and dynamic analyses, is achieved with both enrichments.

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“…The resulting stochastic nonlinear computational model is characterized by a single scalar dispersion parameter, quantifying the level of uncertainty in the stiffness properties which can easily be identified with experiments. Experimental validations based on this theory can be found in (Capiez-Lernout, Soize, & Mignolet 2012, Murthy, Wang, Perez, Mignolet, & Richter 2012 for slender elastic bodies, e.g. beams.…”

Section: Introductionmentioning

confidence: 99%

Computational nonlinear stochastic dynamics with model uncertainties and nonstationary stochastic excitation

Capiez‐Lernout¹,

Soize²,

Mignolet³

2014

Safety, Reliability, Risk and Life-Cycle Performance of Structures and Infrastructures

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The construction of advanced numerical methodologies for the prediction of the dynamical behavior of complex uncertain structures represents an important current challenge. In the present work, structures undergoing large displacements and high strains are investigated. Of particular interest is the analysis of the post-buckling dynamics of a cylindrical shell submitted to an horizontal seismic excitation. The nominal (i.e. without uncertainties) computational model of the cylindrical shell is large, i.e. comprising about 4 200 000 degrees of freedom, obtained with the finite element method using three-dimensional solid elements. A nonlinear reduced-order modeling is first carried out. Then, model uncertainties (on geometry, material properties, etc.) are introduced using probabilistic methods and the corresponding stochastic reduced-order nonlinear computational model is obtained. The identification of its parameters is next carried out using nonlinear static post-buckling data. Finally, a numerical nonlinear dynamic analysis of the uncertain shell is performed in a seismic context, for which the base of the cylindrical shell is submitted to a prescribed rigid shear displacement, modeled through a centered non-stationary Gaussian second-order stochastic process. The stochastic displacement field is then calculated and the effects of uncertainties and of nonlinearities are analyzed in details.

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Uncertainty-based experimental validation of nonlinear reduced order models

Cited by 19 publications

References 14 publications

Post-buckling nonlinear static and dynamical analyses of uncertain cylindrical shells and experimental validation

Post-buckling nonlinear static and dynamical analyses of uncertain cylindrical shells and experimental validation

Prediction of displacement and stress fields of a notched panel with geometric nonlinearity by reduced order modeling

Computational nonlinear stochastic dynamics with model uncertainties and nonstationary stochastic excitation

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