PARAMETER SELECTIONS FOR STOCHASTIC UNCERTAINTY IN DYNAMIC MODELS OF SIMPLE AND COMPLICATED STRUCTURES

Stochastic model updating allows manufacturing variabili ty and modelling uncertainty to be considered, so a set of analytical models with randomised pa rameters can be updated to match upon a set of experimental data of nominally identical test p ieces. In this paper, stochastic model updating in the presence of variability in two sets of very di fferent structures are investigated. The first set consists of nominally identical (simple) flat pl ates, while the second set comprises of (more complicated) formed structures. A series of experi m ntal work is conducted on these structures and a perturbation method is employed to update t heir FE models to match their experimental counterparts. A Monte-Carlo propagation meth od is used to generate scatter plots of analytical cloud, before and after updating is performed . The main objective of this paper is to observe how updating can be adequately performed on the two s ets of very different structures. Stochastic model updating is conducted with different comb inations of parameters, and it is found that geometrical features (such as thickness) alone c a not converge the predicted outputs to the measured counterparts, hence material properties (f or instance, Young’s modulus and shear modulus) must be included in the updating process.