Uncertainties in structural dynamics for composite sandwich panels

International audienceThis paper concerns uncertainties in structural dynamics for composite sandwich panels constituted of two thin carbon-resin skins and one high stiffness closed-cell foam core. Each skin is constituted of two unidirectional plies [60/-60]. Such light composite sandwich panels, manufactured with a same process, generally present a significant dispersion for their Frequency Response Functions (FRF) in the Low-Frequency (LF) and Medium-Frequency (MF) ranges. The objectives of this paper are (1) to study the dispersion due to the process by using experiments (2) to develop a predictive mean mechanical model based on the use of the laminated composite thin plate theory in dynamics and (3) to use a nonparametric probabilistic approach for data and model uncertainties to improve the predictability of the mean model in the MF dynamics

Vibroacoustics of a cavity coupled with an uncertain composite panel

International audienceThis paper deals with uncertainties in vibroacoustics of a bounded cavity whose wall is constituted of a rigid wall and of a deformable part constituted of a Composite Sandwich Panel (CSP). Such a CSP has two thin carbon-resin skins and one high stiffness closed-cell foam core. The objectives of this paper is (1) to study the robustness of acoustic response with respect to the dispersion of the CSP induced by the manufacturing process, (2) to develop a predictive mean mechanical model of the vibroacoustic system and (3) to use a nonparametric probabilistic approach for data and model uncertainties of the CSP in order to analyze the robustness of the mean vibroacoustics model in the LF and MF bands to predict internal acoustic level

Probabilistic approach for model and data uncertainties and its experimental identification in structural dynamics: Case of composite sandwich panels

International audienceThis paper deals with the experimental identification and the validation of a non-parametric probabilistic approach allowing model uncertainties and data uncertainties to be taken into account in the numerical model developed to predict low- and medium-frequency dynamics of structures. The analysis is performed for a composite sandwich panel representing a complex dynamical system which is sufficiently simple to be completely described and which exhibits, not only data uncertainties, but above all model uncertainties. The dynamical identification is experimentally performed for eight panels. The experimental frequency response functions are used to identify the non-parametric probabilistic approach of model uncertainties. The prediction of the low- and medium-frequency dynamical responses obtained with the stochastic system is compared with the experimental measurements

Identification et validation expérimentale d'un modèle stochastique des incertitudes en vibroacoustique d'un panneau composite.

National audienceWe present a probabilistic model allowing uncertainties induced by modeling errors and system-parameter uncertainties to be taken into account for a multi-layer composite sandwich panel. The sensitivity of the internal noise inside a bounded cavity coupled with the panel is analyzed with respect to uncertainties. Eight composite panels have been constructed by using the same manufacturing process. Experimental measurements of the vibration and acoustic responses have been performed in the low- and medium- frequency ranges. These measurements allow the probabilistic model of uncertainties to be identified.We present the probabilistic numerical model and its comparison with the experiments for validation

Influence of the track geometry variability on the train behavior

International audienceThis paper is devoted to the development of a stochastic modeling of the track geometry and its identiication with experimental measurements. This modeleing, which has to integrate the statistical and spatial variabilities and dependencies , is a keyu issue when using simulation for conception, maintenance or certification purposes

Statistical inverse problems for non-Gaussian vector valued random fields with a set of experimental realizations

International audienceThe railway track irregularities, which is a four dimensions vector-valued random field, are the main source of excitation of the train. At first, using a revisited Karhunen-Loève expansion, the considered random field is approximated by its truncated projection on a particularly well adapted orthogonal basis. Then, the distribution of the random vector that gathers the projection coefficients of the random field on this spatial basis is characterized using a polynomial chaos expansion. The dimension of this random vector being very high (around five hundred), advanced identification techniques are introduced to allow performing relevant convergence analysis and identification. Based on the stochastic modeling of the non- Gaussian non-stationary vector-valued track geometry random field, realistic track geometries, which are representative of the experimental measurements and representative of the whole railway network, can be generated. These tracks can then be introduced as an input of any railway software to characterize the stochastic behavior of any normalized train

Karhunen-Loève based sensitivity analysis

International audienceThe identification of the most dangerous combinations of excitations that a non-linear mechanical system can be confronted to is not an easy task. Indeed, in such cases, the link between the maximal values of the inputs and of the outputs is not direct, as the system can be more sensitive to a problematic succession of excitations of low amplitudes than to high amplitudes for each kind of excitations. This work presents therefore an innovative method to identify the combined shapes of excitations that are the most correlated to problematic responses of the studied mechanical system

Modeling the track geometry variability

International audienceAt its building, the theoretical new railway line is supposed to be made of perfect straight lines and curves. This track geometry is however gradually damaged and regularly subjected to maintenance operations. The analysis of these track irregularities is a key issue as the dynamic behaviour of the trains is mainly induced by the track geometry. In this context, this work is devoted to the development of a stochastic modeling of the track geometry and its identification with experimental measurements. Based on a spatial and statistical decomposition, this model allows the spatial and statistical variability and dependency of the track geometry to be taken into account. Moreover, it allows the generation of realistic track geometries that are representative of a whole railway network. These tracks can be used in any deterministic railway dynamic software to characterize the dynamic behavior of the train

Experimental validation of a nonparametric probabilistic model of nonhomogeneous uncertainties for dynamical systems

International audienceThe paper deals with an experimental validation of a nonparametric probabilistic model of nonhomogeneous uncertainties for dynamical systems. The theory used, recently introduced, allows model uncertainties and data uncertainties to be simultaneously taken into account. An experiment devoted to this validation was specifically developed. The experimental model is constituted of two simple dural rectangular plates connected together with a complex joint. In the mean mechanical model, the complex joint, which is constituted of two additional plates attached with 40 screw-bolts, is modeled by a homogeneous orthotropic continuous plate with constant thickness, as usual. Consequently, the mean model introduces a region (the joint) which has a high level of uncertainties. The objective of the paper is to present the experiment and the comparisons of the theoretical prediction with the experiments