No abstract

No abstract

No abstract

Transportation Technology Center, Inc. (TTCI), a wholly owned subsidiary of the Association of American Railroads (AAR), has developed an iterative rail wear prediction model in the NUCARS® vehicle/track interaction multibody simulation program through internal research and development efforts and with funding from Network Rail (NR) in the UK. The rail wear model was built upon the NUCARS® penetration model1 to take advantage of the wheel/rail (W/R) contact calculation methodology for conformal W/R profiles. In addition to the advantages of NUCARS vehicle and track modeling capabilities, it modifies the rail profile online based on the Wear Indices (Tγ) and penetrated W/R profile shapes in the multipoint contact patches, and automatically updates the rail profile for the next run. The penetrated wheel profile segments or “wheel footprints” are blended into the modified rail profile. The worn rail shape eventually resembles the wheel shapes in the wheel database, and the wear process results in conformal W/R profile shapes. Rail wear prediction was validated using rail wear test results based on 515 million gross tons (MGT) of heavy axle load (39-ton (35-tonne) axle loads) freight traffic accumulated from 2003 to 2007 at the Facility for Accelerated Service Testing (FAST) on the nonground test zone (Section 25, 6-degree (291-meter (m)) curve with 5 inches (127 millimeter (mm)) superelevation). A wheel database, consisting of 50 measured new, mildly worn and heavily worn FAST train wheel profiles, was used to reflect the wheel shape effects during the wear process. This model has been used to predict rail relative wear trends of ground rail profiles for NR.2,3,4 A quantified W/R gap loss function has been implemented in NR’s Track-Ex© program5 for prioritizing rail grinding.

A cost-effective method of experimentally determining rail vehicle dynamic parameters for use in analytical dynamic models has been developed by the Association of American Railroads (AAR). In this method, the rail vehicle’s rigid body modes of vibration are manually excited and the corresponding resonant frequencies are measured. Using these frequencies, the equations of motion for the system are solved for the vehicle’s mass moments of inertias, center of gravity location, and suspension stiffness. This method of dynamic parameter identification was developed at AAR’s Transportation Technology Center, Pueblo, Colorado. Compared to competing experimental methods, the AAR method offers the advantage of cost-effectiveness; it requires no expensive equipment and can be executed at remote locations. This paper describes the AAR method for rail vehicle parameter identification. An example of its use in the characterization of an iron ore rail vehicle is presented. In addition, dynamic simulation results for a transit vehicle, characterized using the AAR method, are compared to track test data for validation.