This work addresses the problem of the reliability of simulations for realistic nonlinear systems, by using efficient techniques for the analysis of the propagation of the uncertainties of the model parameters through the dynamics of the system. We present the sensitivity analysis of the critical speed of a railway vehicle with respect to its suspension design. The variance that stems from parameter tolerances of the suspension is taken into account and its propagation through the dynamics of a full car with a couple of two-axle Cooperrider bogies running on curved track is studied.
Modern Uncertainty Quantification methods, such as Stochastic Collocation and Latin Hypercube, are employed in order to assess the global uncertainty in the computation of the critical speed. The sensitivity analysis of the critical speed to each parameter and combination of parameters is then carried out in order to quantify the importance of different suspension components. This is achieved using combined approaches of sampling methods, ANOVA expansions, Total Sensitivity Indices and Low-dimensional Cubature Rules.