Experimental identification of an uncertain computational dynamical model representing a family of structures

International audienceWe are interested in constructing an uncertain computational model representing a family of structures and in identifying this model using a small number of experimental measurements of the first eigenfrequencies. The prior probability model of uncertainties is constructed using the generalized probabilistic approach of uncertainties which allows both system-parameters uncertainties and model uncertainties to be taken into account. The parameters of the prior probability model of uncertainties are separately identified for each type of uncertainties, yielding an optimal prior probability model. The optimal prior stochastic computational model allows a robust analysis for the family of structures to be carried out

Construction and experimental identification of an uncertain model in computational dynamics using a generalized probabilistic approach of uncertainties

International audienceWe are interested in constructing an uncertain model of a nominal motor CSS of pressurized water reactor using a generalized probabilistic approach of uncertainties and in identifying this model using experimental measurements of the first eigenfrequencies. This generalized probabilistic approach of uncertainties allows both model-parameter uncertainties and model uncertainties to be taken into account and identified separately in the context of the experimental modal anaysis. Finally, the identified uncertain model allows statistics on quantities of interest to be estimated

Computational dynamics in low- and medium-frequency ranges. Reduced-order model and uncertainty quantification

Reduced-order models, substructuring techniques and uncertainty quantification are important aspects in computational dynamics, fluid-structure interactions, vibrations and vibroacoustics. In this framework, we will present the following new aspects: (i) Stochastic reduced-order model in low-frequency dynamics in presence of numerous local elastic modes, for which the high modal density makes the use of the classical modal analysis method not suitable; (ii) New ingredients useful for the nonparametric stochastic modeling of uncertainties (a) for structures with uncertain boundary conditions and/or coupling between substructures and (b) for linear viscoelastic structures in the medium-frequency range; (iii) Bayesian posteriors of uncertainty quantification in computational structural dynamics for low- and medium-frequency ranges. In addition to some illustrations which will be given, three industrial applications will be presented