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We investigate numerically the dynamical reactions of a moving wheelset model to real measured track irregularities. The background is to examine whether the dynamics are suitable as the input to the inverse problem: determine the true track geometry from measured wheelset dynamical reactions. It is known that the method works well for the vertical position of the rails but the computed lateral position is often flawed. We find that the lateral motion of the wheelset often may differ from the track geometry. The cases are investigated closely but the reasons remain unknown. While the wheelset dynamics reflect the larger (> 4–6 mm) aperiodic track disturbances and single large disturbances quite well, this does not seem to be the case for general smaller or periodic track irregularities or sections behind single large disturbances. The resulting dynamics of a wheelset to lateral track irregularities are in general not sufficiently accurate to be used as the basis for a description of the track irregularities.

In this paper, we investigate the lateral dynamics of a railway wheelset suspended under a moving car with linear springs and dry friction dampers. Both theoretical and numerical methods are used to complement each other. The car runs on an ideal, straight and perfect track with a constant speed. A nonlinear relation between the creepages and the creep forces is used in this paper. The nonsmoothness of this model is due to the dry friction dampers. The speed is selected as the bifurcation parameter. The one-dimensional bifurcation diagram, which gives a general view of the dynamics of the system, is presented. Both symmetric and asymmetric periodic motions, quasi-periodic motions and chaotic motions are found. In addition to bifurcations that can exist in both smooth and nonsmooth systems, a kind of sliding bifurcations that are unique to nonsmooth systems is found. Bifurcation diagrams, phase portraits, Poincaré sections and Lyapunov exponents are presented to ensure that no contradictory results are given. The influence of the conicity of the wheel tread on the Hopf bifurcation type is examined.

Several attempts of measuring the exact location of the rails by the use of ordinary vehicles have been made. While the method works reasonably well in the vertical direction, the results of the lateral measurements made with different vehicles are so widely scattered that it is virtually impossible to draw any conclusions. We may therefore ask: does a wheel set follow the track disturbances exactly? In this article we investigate the lateral dynamics of a half-car vehicle model with two-axle bogies running on a rigid tangent track with sinusoidal lateral disturbances of the rails. The wavelength, the amplitude and the phase between the rail disturbances are varied. Two different vehicle speeds are investigated. One speed is under and the other above the vehicle critical speed. In the article we show examples of axle motions that do not follow the track disturbances in phase, amplitude or period or several of these together. The results are discussed, and we must conclude that it is in general impossible to determine the track geometry from the motion of a wheel set.

We present an approach to global sensitivity analysis aiming at the reduction of its computational cost without compromising the results. The method is based on sampling methods, cubature rules, High-Dimensional Model Representation and Total Sensitivity Indices. The approach has a general applicability in many engineering fields and does not require the knowledge of the particular solver of the dynamical system. This analysis can be used as part of the virtual homologation procedure and to help engineers during the design phase of complex systems. The method is applied to a half car with a two-axle Cooperrider bogie, in order to study the sensitivity of the critical speed with respect to suspension parameters. The importance of a certain suspension component is expressed by the variance in critical speed that is ascribable to it. This proves to be useful in the identification of parameters for which the exactness of their values is critically important.

This paper describes the results of the application of Uncertainty Quantification methods to a railway vehicle dynamical example. Uncertainty Quantification methods take the probability distribution of the system parameters that stems from the parameter tolerances into account in the result. In this paper the methods are applied to a lowdimensional vehicle dynamical model composed by a two-axle bogie, which is connected to a car body by a lateral linear spring, a lateral damper and a torsional spring. Their characteristics are not deterministically defined, but they are defined by probability distributions. The model - but with deterministically defined parameters - was studied in [1], and this article will focus on the calculation of the critical speed of the model, when the distribution of the parameters is taken into account. Results of the application of the traditional Monte Carlo sampling method will be compared with the results of the application of advanced Uncertainty Quantification methods such as generalized Polynomial Chaos (gPC) [2]. We highlight the computational performance and fast convergence that result from the application of advanced Uncertainty Quantification methods. Generalized Polynomial Chaos will be presented in both the Galerkin and Collocation form with emphasis on the pros and cons of each of those approaches