Experimental identification of stochastic processes using an uncertain computational non-linear dynamical model

Anas Batou (1) , Christian Soize (1)

1 MSME - Laboratoire de Modélisation et Simulation Multi Echelle

The paper deals with the identification of a model for the external loads applied to tubes bundles through the knowledge of dynamical responses. In complex dynamical systems, such an identification is difficult due to the size of the computational model and due to the high number of parameters to be identified. A simplified computational model is constructed. Uncertaintiesare taken into account in the mean computational model using the nonparametric probabilistic approach for both the system-parameter uncertainties and the model uncertaintiesinduced by modeling errors. In addition, a probabilistic model for the stochastic loads is constructed to take into account model uncertainties in the probabilistic model of the stochastic loads. The nonlinear stochastic dynamical system submited to the uncertain stochastic loads is used to identify the probability model of its uncertainties.

Mots clés: uncertainty quantification; experimental identification; stochastic process; computational nonlinear structural dynamics; nonparametric probabilistic method; random matrix; model uncertainties; random uncertainties; modeling errors; ransom matrix

Domaines: Mécanique [physics.med-ph]; Probabilités [math.PR]; Statistiques [math.ST]

Anas Batou, Christian Soize. Experimental identification of stochastic processes using an uncertain computational non-linear dynamical model. ICIPE 2008, 6th International Conference on Inverse Problems in Engineering: Theory and Practice, Jun 2008, Dourdan, France. pp.1-8, ⟨10.1088/1742-6596/135/1/012014⟩. ⟨hal-00691506⟩