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Civil-Comp Conferences
ISSN 2753-3239 CCC: 7
PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo
Paper 6.15
Prediction of long-term damage evolution in crossing panels using an iterative simulation scheme B.A. PĂ„lsson1 and U. Ossberger2
1Department of Mechanics and Maritime Sciences / CHARMEC, Chalmers University of Technology, Gothenburg, Sweden
B.A. Pålsson, U. Ossberger, "Prediction of long-term damage evolution in crossing panels using an iterative simulation scheme", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on
Railway Technology: Research, Development and Maintenance",
Civil-Comp Press, Edinburgh, UK,
Online volume: CCC 7, Paper 6.15, 2024, doi:10.4203/ccc.7.6.15
Keywords: crossing, turnout, measurements, multibody simulations, dynamic vehicle-track interaction, calibration.
Abstract
This paper presents an iterative simulation methodology to compute long-term deterioration in crossing panels. The methodology takes turnout type, initial conditions and traffic load into consideration to compute the long-term change in ballast settlement and crossing geometry deterioration. The model also provides outputs in the form of structural loading such as sleeper-ballast contact pressures and bending moments in sleepers and crossing. In the methodology, the dynamic vehicle-track interaction is simulated using an MBS model with a finite element model of the crossing panel structure. To allow for computationally efficient simulation of long-term deterioration, phenomenological damage models are used. Settlements are computed using the Sato threshold model and the crossing geometry change is computed using a novel damage model derived using historical crossing geometry measurements. The methodology is demonstrated for a switch & crossing demonstrator installed in the Austrian railway network as a part of the European research programme Shift2Rail, and the measured crossing geometry change after 26 months of traffic is compared to model predictions
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