Uncertainty analysis using generalized Polynomial Chaos for the identification of structural constraining fixtures

Abstract

Inverse problem solution is always prone of: (i) noise contamination sensitivity (ii) model uncertainty sensitivity (iii) the sensitivity against variation parameters’ magnitude. Our investigation consists in evaluating the performance of a method for the identification of the dynamic parameters of local constraining supports such as mass, stiffness and damping of multi-supported structure that will operate in the presence of the inevitable model parameters uncertainty. In the present contribution, the robustness of a method, that combine the measured frequency transfer functions and structural modification techniques for the identification of the dynamic parameters of local constraining, is evaluated against uncertainty in the model physical parameters. The contribution of the uncertain physical parameters to the identification procedure when using noise-free data as well as under a realistic noise level is further addressed. Results using the polynomial chaos expansion method are compared with Monte Carlo simulations. It is shown that uncertainty levels in the input data could result in large variation in the dynamic response of the multi-supported system. A sensitivity analysis is applied in order to quantify the influence of the uncertain model parameters on the identified supports parameters.