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Chen Shen received his Bachelor degree in Civil Engineering at Tongji University, Shanghai, China, in 2011. Later in 2014, he received Master degree in Transportation Engineering at the same university. He is currently a PhD candidate at the Section of Railway Engineering of Delft University of Technology in the Netherlands. His research interests include vehicle-track interaction modelling and condition monitoring of railway turnout.

  • Vehicle dynamics and train-track interaction
  • Multibody dynamic modelling

Switches & crossings: Train-track interaction, derailment, degradation and condition monitoring

Switches and crossings (S&C) or turnouts are critical sub-system in railway tracks. They enable the flexibility of rail traffic operation. Compared with ordinary rails, S&C are characterized by the complexity in geometry and structure, such as changing profiles of crossing nose, wing rail and switch blade, the discontinuities in both the switch panel and the crossing panel, as well as the sharp turn that cannot be compensated with cant. Consequently, severe impact load is induced when wheels pass the discontinuities and large lateral force occurs in the sharp turn. Besides, just as in ordinary tracks, dynamic forces arise not only from short wave irregularities mainly caused by rail top defects, but also from longer wave irregularities which are more related to the degradation of other components in S&C, such as fastenings, sleepers, ballast or subgrade. Altogether these dynamic forces can cause large stresses and strains to S&C components, leading to degradation and damages in S&C system, which may result in malfunctioning of S&C, causing wheel climb and subsequently derailment. Therefore, any deviation of track geometry, short wave or long wave, from its nominal state will lead to fast degradation of S&C components and costly maintenance and operation. Various inspection methods are available for degradation and damages of rails, fastening bolts and ballast, based on ultrasonic and eddy current testing, image analysis and other sensor technologies. However, all these methods either detect the damage at a late stage, or require the presence of visible damages for detection, limiting their capability for early detection and prognosis. Therefore, to reduce the costs of maintenance, an effective train-borne measurement system for S&C health condition monitoring, which relies on measured S&C geometry and structural response under operational loads instead of visible damages, is very much desired worldwide for diagnosis and prognosis, especially at an early stage of the degradation of both rails and other components. To achieve this goal, substantial efforts are needed to investigate the relationships between the dynamic forces and the degradation mechanisms and rates of both rails and other components, as well as the relationship between the actual degradation state of S&C and the geometry measured from a train. In this research, models at different length scales will be developed to predict the performance of S&C components under loaded conditions in relation to their damages and degradation. Scale 1 is a macro scale simulating the train-track interaction to quantify the dynamic forces and responses; scale 2 is at component level in which more detailed models will be built up to predict the performance of components. To concentrate, only two to three components of a S&C system will be selected for study at this level; and scale 3, if necessary, will be at material level to determine for scale 1 and especially scale 2 some necessary material parameters under nominal and degraded conditions. Laboratory and field test and measurement will be performed to acquire data for inputs to and validation of the models. It is expected that with this approach, quantitative relationships between measured geometry of S&C under loaded conditions and its degradation state will be found and subsequently a degradation detection technique can be proposed based on these relationships.

  • Teaching assistant for courses CIE 4870 and CIE 4871

Chen Shen, MSc

PhD Researcher

+31 (0) 15 278 13 73
Building: 23, room 1.54

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