A detailed description of the Virginia Tech-Federal Railroad Administration (VT-FRA) Roller Rig measurement capabilities, along with the efforts in establishing the accuracy, repeatability, and integrity of the results are presented. The results of a series of baseline tests are also documented in an effort to provide an indication of the type of experiments that can be achieved on the rig. The one-fourth scaled rig is intended to be used for evaluating wheel-rail contact mechanics and dynamics with a high degree of precision. The rail is represented by a roller with a diameter that is five times larger than the wheel, in order to maintain the contact ellipse distortion to less than 10 percent. The primary point of differentiation between this rig and others that have been used in the past or are presently in use is that it is able to measure the wheel-rail contact forces with far greater precision than achieved in the past. The rig is also designed such that it provides a high degree of repeatability in testing, often needed for performing design of experiments accurately. The VT-FRA rig is capable of precisely controlling the lateral positioning of the wheel and rail, rail cant angle, the wheel-rail angle of attack, and the speed of the roller and wheel independently. The latter is intended to provide precise control of the relative speed of the wheel and roller, which amounts to precisely controlling creepage. Beyond presenting the rig’s capabilities, the paper provides a discussion of the initial results from the commissioning of the rig. It is concluded that the rig is ready to be commissioned for studies that are of interest to the practitioners in the rail industry and scientists in the research community.
Recently, there has been a large demand for predicting, in real time, the performance of multiple railroad vehicle types traversing existing track as the geometry of the track is being measured. To accurately predict a railroad vehicle’s response over a specified track requires the solution of nonlinear equations of motion and extensive calculations based on the suspension characteristics of the vehicle. To realize the real time goal, codes are being implemented that use linear approximations to the fully nonlinear equations of motion to reduce computation time at the expense of accuracy. Alternatively, neural network technology has the ability to learn relationships between a mechanical system input and output, and, once learned, give quick outputs for given input. The training process can be done using measured data or using simulation data. In general, measured data is very expensive to gather due to the instrumentation requirements and is most often not available. In this paper, the use of multibody simulation code as a training tool for a neural network is presented. The example results estimate the vertical and lateral forces at the wheel-to-rail interface as a function of the geometry of the track and the suspension characteristics of the vehicle type by using a multibody code with neural network technique.
Vehicle/Track Interaction (VTI) Safety Standards aim to reduce the risk of derailments and other accidents attributable to the dynamic interaction between moving vehicles and the track over which they operate. On
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