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Suspension components play key roles in the running behaviour of rail vehicles, and therefore, mathematical models of suspension components are essential ingredients of railway vehicle multi-body models. The aims of this paper are to review existing models for railway vehicle suspension components and their use for railway vehicle dynamics multi-body simulations, to describe how model parameters can be defined and to discuss the required level of detail of component models in view of the accuracy expected from the overall simulation model. This paper also addresses track models in use for railway vehicle dynamics simulations, recognising their relevance as an indispensable component of the system simulation model. Finally, this paper reviews methods presently in use for the checking and validation of the simulation model.

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Stability assessment of rail vehicles is probably the most widespread form of dynamic analysis in railway vehicle engineering. The computer simulations using fully non-linear threedimensional vehicle models constructed in a modern multi-body simulation tool allow detailed non-linear stability analysis for the specified conditions. However, high sensitivity to the wheel/ rail contact conditions and different definitions of stability in mechanics and in railway practise can lead to significant differences between prediction and the measurement. Different methods of non-linear stability analysis, which may be used in industrial applications, are introduced and compared on selected examples of contact geometry wheel set/track with high equivalent conicity. The comparisons show that the linearization of the contact geometry wheel set/track can enable a better assessment of the non-linear stability analyses. A decreasing equivalent conicity function in the range of amplitudes below 3 mm leads to supercritical Hopf bifurcation with small limit cycles and consequently to largest differences between the methods compared. When excluding the contact geometries leading to supercritical Hopf bifurcation, the results achieved are closer each other, but still with differences in the range of up to 10 per cent. This uncertainty in the stability prediction caused by the method applied must be taken into consideration, in addition to other uncertainties related to vehicle parameters, modelling, etc.

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