Stochastic reduced-order model for dynamical structures having a high modal density in the low frequency range

Abstract : This paper is devoted to the construction of stochastic reduced-order model for dynamical structures having a high modal density in the low-frequency range. We are particularly interested in automotive vehicles which are made up of stiff parts and flexible components. This type of structure is characterized by the fact that it exhibits, in the low-frequency range, not only the classical global elastic modes but also numerous local elastic modes which cannot easily be separated from the global elastic modes. To solve this difficult problem, a new approach is proposed for constructing a reduced-order computational dynamical model adapted to the low-frequency range. Model uncertainties induced by modeling errors in the computational model are taken into account using the nonparametric probabilistic approach which is implemented in the reduced-order model. The methodology is applied on a complex computational model of an automotive vehicle.