“The results of the test so far show that it should be possible to identify the effect of track geometry deviations on vehicle ride quality response during Phase II of the project.” Charity Duran Ketchum
the characterised vehicle was equipped with accelerometers and various displacement transducers to collect passenger ride quality data. Track geometry measurements were collected within two weeks of the ride quality measurements and used as comparisons with predictions from the Nucars model and for future PBTG neural network training.
The ride quality and track geometry comparison was undertaken to determine if there was a correlation between them. Locations on the Red Line that had ride quality issues were identified from the ride quality test. The top part of Figure 1 shows the accelerations measured under the driver’s seat in the leading cab of the SLRV in this area, while the bottom section of Figure 1 shows the track geometry measured in the same area. The 2-second peak-to-peak value is approximately 0.35g. In the area where this occurs, there is a deviation in the lateral alignment. Figure 2 shows the frequency content of the acceleration data and lateral alignment of the track geometry. There are peaks at approximately 1Hz and 1.65Hz in the lateral vehicle response. In the lateral alignment of the track geometry in this area there is also a 1Hz peak corresponding to a wavelength of 28.35m. In addition there is a 1Hz response of the vehicle that correlates to the 1Hz frequency content of the lateral alignment of the track. It is possible to identify track geometry that can cause ride quality issues, such as the lateral deviations with the 28.35m wavelength, which cause a dynamic response in the vehicle. It is important to note that although these track geometry deviations do not exceed any safety criteria, they can affect passenger ride quality. To identify the track geometry issues that affect ride quality, it is imperative to take
IRJ August 2012
track geometry and ride quality measurements at the same time. Hunting may be triggered by a combination of lateral deviation, speed, and wheel/rail interaction, so will be important in the next phase of this project to investigate the potential triggers in more detail.
Vehicle model
The results of the test so far show that it should be possible to identify the effect of track geometry deviations on vehicle ride quality response during Phase II of the project. However, some work is still required to improve the vehicle model to predict this response correctly. Identifying the influence of the following factors on vehicle response will be important to accurately model
and determine track geometry triggers: wheel/rail interface, including profile
shapes and contact geometry vehicle speed, and understanding and identifying rigid
body vibration modes of the vehicle. After all the issues have been investigated, the track geometry and ride quality data collected during Phase I at Dart will be used to train neural networks to predict ride quality. The validated Dart vehicle Nucars model will be used to run simulations at different speeds to generate additional neural network training data. The neural networks will then be used to predict ride quality over measured track not used in the training, while the neural network output will be compared with Nucars simulation predictions and measured ride quality to determine the accuracy of the neural network predictions. If neural networks are determined to be a viable option for predicting ride quality, a different vehicle on a different transit system will be selected for further investigation. IRJ
NEW PRODUCTS FOR INNOTRANS Hall 23 Stand 210
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