Wear, Available online 1 December 2013.
Joel VanderMarel, Donald T. Eadie, Kevin D. Oldknow, Simon Iwnicki.
LB Foster Rail Technologies, 4041 Remi Place, Burnaby, BC, Canada V5A4J8 and
Institute of Railway Research, University of Huddersfield, Queensgate, Huddersfield, UK.
Abstract
In this paper the authors present a predictive model of train energy requirements due to the application of a top of rail friction modifier (TOR-FM) versus dry wheel/rail conditions. Using the VAMPIRE® Pro simulation package, train energy requirements are modeled for two sets of TOR-FM frictional conditions, one using full Kalker coefficients and the other by using a Kalker coefficient of 18%. Both scenarios use a top of rail saturated coefficient of friction of 0.35. Under both TOR-FM frictional conditions, train energy savings are shown for complete laps of the Transportation Technology Center Inc.’s (TTCI) Transit Test Track (TTT) loop, and also when isolating only the tangent section of the loop. However, the magnitude of energy savings varies greatly depending on the Kalker coefficient factor used, highlighting the need to model this relationship as accurately as possible. These simulation results are compared with data obtained from a field study, in which train energy savings of 5.3% (lap) and 7.8% (tangent) are shown due to the application of TOR-FM.
Additional Information
This work presented in this paper is part of an ongoing research collaboration between L.B. Foster Rail Technologies and the Institute of Railway Research located at the University of Huddersfield. This research aims at improving the understanding of the effects of friction modifiers on the wheel / rail interface, particularly in regards to locomotive fuel consumption and wheel / rail wear.
The AutoPilot™ system, developed by L.B. Foster Rail Technologies, is a mobile railcar-mounted system capable of depositing an air atomized friction modifier (KELTRACK®) onto the top of rail surface, thereby controlling the coefficient of friction in the wheel / rail interface at an optimized level and reducing train energy, fuel usage, and emissions.
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