Stochastic model updating of an aircraft like structure by parameter covariance matrix adjustment

Abstract

The propagation analysis of parameter uncertainty is structural dynamics has become a feasible approach to determine the probabilistic description of the vibration response of industrial scale finite element models. Though methods for uncertainty propagation have been developed extensively, the quantification of parameter uncertainty has been neglected in the past. But a correct description for the parameter variability is essential for the variation of the vibration response. This paper shows how to identify model parameter means and covariance matrix from uncertain experimental modal test data. The common gradient based approach from deterministic computational model updating was extended by an equation that accounts for the stochastic part. In detail an inverse approach for the identification of statistical parametric properties will be presented which will be applied on a numerical model of a replica of the GARTEUR SM-AG19 benchmark structure. The uncertain eigenfrequencies and mode shapes have been determined in an extensive experimental modal test campaign where the aircraft like structure was tested repeatedly while it was 130 times dis- and reassembled in between each experimental modal analysis.