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Civil-Comp Conferences
ISSN 2753-3239 CCC: 1
PROCEEDINGS OF THE FIFTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo
Paper 22.8
A fast analytical tool to investigate effects of railway superstructure components on track dynamics B. Morin1,
C.J.G. Plummer2,
B. Kalyanasundaram1,
V. Michaud3,
J. Cugnoni4,
J. Oertli5,
H. Frauenrath2
and B. Van Damme1
1Laboratory for Acoustics/Noise Control, Swiss Federal Laboratories for Materials Science and Technology, Switzerland B. Morin, C.J.G. Plummer, B. Kalyanasundaram, V. Michaud, J. Cugnoni, J. Oertli, H. Frauenrath, B. Van Damme, "A fast analytical tool to investigate effects of railway superstructure components on track dynamics", in J. Pombo, (Editor), "Proceedings of the Fifth International Conference on Railway Technology: Research, Development and Maintenance",
Civil-Comp Press, Edinburgh, UK,
Online volume: CCC 1, Paper 22.8, 2022, doi:10.4203/ccc.1.22.8
Keywords: track decay rate, modelling and optimization, track dynamics.
Abstract
We present a new, detailed analytical model that takes all components of the superstructure into account and calculates a narrowband spectrum of rail accelerance and TDR, both vertically and laterally. The railpads are modelled as combinations of connections with frequency-dependent damping and stiffness. These tabular values are based on detailed finite element models of the pads, which reduce the geometry and material properties to 4 different springs. 2 for translational motion and 2 for rotational effects. The total calculation time of several minutes allows for fast evaluation of the influence of geometrical details such as the placement of slits, and the selection of ideal commercially available materials.
The fast analytical models allow us to predict the influence of stiffness and damping on the main dynamic properties of the superstructure. For vertical motion, the railpads' stiffness has the largest effect on the track decay rate, but their damping influences the pin-pin resonances. This is particularly visible in the narrow-band response spectra. The model captures structural details, but is fast enough to predict the dynamics of 200-sleeper sections in less than 5 minutes. It is therefore particularly useful for optimization purposes.
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