Performance-Based Track Geometry, Phase 1 (2012)

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vii Figure 30. Force-Displacement Diagram and Calculated Slope .................................................................... 32 Figure 31. Vertical Displacement and Load Measurements .......................................................................... 33 Figure 32. Force-Displacement Diagram and Calculated Slope .................................................................... 33 Figure 33. Example Ride Quality Data.......................................................................................................... 35 Figure 34a. Crest Factors for Vertical Accelerations .................................................................................... 37 Figure 34b. Crest Factors for Lateral Accelerations ...................................................................................... 37 Figure 34c. Crest Factors for Longitudinal Accelerations ............................................................................ 38 Figure 35a. MTVV Values for Vertical Accelerations .................................................................................. 39 Figure 35b. MTVV Values for Lateral Accelerations ................................................................................... 40 Figure 35c. MTVV Values for Longitudinal Accelerations .......................................................................... 41 Figure 36. Raw Track Geometry Data ........................................................................................................... 43 Figure 37. Processed Track Geometry Data for use in NUCARS ................................................................. 44 Figure 38. Measured Acceleration Data between Cedars and 8th & Corinth Station, and Measured Track Geometry Data between Cedars and 8th Figure 39. Frequency Content of Track Geometry, and Vehicle Response ................................................... 46 & Corinth Station ......................... 45 Figure 40. Yaw Acceleration Data for the A- and B-carbodies. Description of the U-shaped Yaw Vibration Mode.................................................................................................... 47 Figure 41. Sun Kink Observed During Track Inspection .............................................................................. 48 Figure 42. Measured Track Geometry in Area of Observed Sun Kinks ........................................................ 49 Figure 43. Wheel Profile Contacting Tangent Rail Profile ............................................................................ 50 Figure 44. Wheel Profile Contacting Curved Rail Profile ............................................................................. 50 Figure 45. Measured Lateral Accelerations Compared to Predicted Lateral Accelerations .......................... 51 Figure 46. Measured Vertical Accelerations Compared with Predicted Vertical Accelerations ................... 52 Figure 47. Measured Frequency Content Compared with Predicted Frequency Content .............................. 52

viii List of Tables Table 1. Ride Quality Issues Identified on the DART Red Line ................................................................... iii Table 2. Ride Quality Standards Comparison ................................................................................................. 5 Table 3. SLRV Design Specifications ............................................................................................................. 7 Table 4. Carbody Resonance Instrumentation Description ........................................................................... 10 Table 5. Instrumentation Description ............................................................................................................ 21 Table 6. Rigid Body Vibration Modes and Measured Frequencies ............................................................... 24 Table 7. Bogie Resonance Test Summary ..................................................................................................... 29 Table 8. Longitudinal Primary Suspension Stiffness ..................................................................................... 31 Table 9. Lateral Primary Suspension Stiffness .............................................................................................. 32 Table 10. Vertical Primary Suspension Stiffness .......................................................................................... 33 Table 11. ISO 2631 Ride Quality Index Boundaries ..................................................................................... 36 Table 12. Segments of Track with Ride Quality Exceptions ......................................................................... 42

1 1.0 INTRODUCTION Poor vehicle dynamic performance and poor ride quality frequently occur at track locations that do not exceed track geometry or safety standards, such as curve entry or exit, special trackwork, and track misalignments that promote yaw instability or hunting. Poor ride quality may not be an indicator of unsafe operation, but may point to an area of track or a vehicle that needs maintenance to prevent further degradation. Conversely, track geometry locations that exceed track geometry or safety standards often do not cause poor ride quality or poor vehicle performance. To optimize transit system maintenance, methods need to be developed to identify vehicle conditions and track locations that actually cause poor ride quality or vehicle performance. Track geometry measurements alone are not always an indicator of how a vehicle behaves. Predicting the vehicle dynamic response can help address the following issues: • Prioritize maintenance • Identify problem locations that do not exceed normal track geometry standards • Identify problems as they arise rather than waiting for scheduled maintenance • Identify car designs and car component wear issues that can contribute to poor vehicle performance and poor ride quality To improve and advance the current track geometry inspection practice and standards, Transportation Technology Center, Inc. (TTCI) developed a track inspection method known as performance-based track geometry (PBTG). Trained neural networks in the PBTG system relate the complex dynamic relationships that exist between vehicles and track geometry to vehicle performance.2 A transit agency can use PBTG to optimize maintenance of the track and fleet. Onboard accelerometers on the fleet and a PBTG neural network can be used to identify track locations that need work and do not require direct measurement of the track geometry. This allows monitoring of track condition between scheduled track geometry measurements. PBTG can also be used to identify cars that are beginning to deteriorate. If all cars in the fleet are equipped with PBTG accelerometers, they can be used to build a database of information for monitoring the condition of the cars and the track over time. They also identify track segments that may generate unwanted vehicle responses. PBTG is now in use by three North American freight railroads and one international railroad. Also, PBTG uses measured track geometry and the PBTG neural network to predict vehicle performance on existing track. This helps to identify locations in the track likely to cause poor ride quality or other issues related to vehicle performance, which is the way PBTG is currently being applied by North American freight railroads. 2 Li, D., A. Meddah, K. Hass, and S. Kalay. March 2006. “Relating track geometry to vehicle performance using neural network approach.” Proc. IMECHE Vol. 200 Part F: J. Rail and Rapid Transit, 220 (F3), 273- 282.

2 An indirect benefit of implementing the PBTG system can be making validated vehicle dynamics models available to a transit agency. The models can be used for many other purposes such as investigating dynamic performance problems, evaluating vehicle modifications, evaluating vehicle performance over proposed new track routes and alignments, and optimizing wheel and rail profile maintenance. In support of the Transit Cooperative Research Program (TCRP) D-7 research program, TTCI is conducting research to develop methods for evaluating track geometry that will account for transit system vehicle performance and passenger ride quality using a combination of PBTG and NUCARS®3 • Proof of Concept: Determine if PBTG will work to predict ride quality for the transit industry. modeling techniques, and on-track measurements. These studies will form the basis for determining improvements in track geometry and track maintenance practices. The overall objective for the project is to demonstrate the use of PBTG techniques for improving the ride quality of transit systems. The project is being conducted with the cooperation of Dallas Area Rapid Transit (DART). Specific deliverables of this multiphased project include: • Trained PBTG neural net algorithms for DART and one other transit system. • Methodology and recommendations for implementing PBTG techniques on other transit systems. This report addresses Phase I of this work, which consisted of the following items: • Ride Quality Literature Survey (Appendix) • Vehicle Characterization and On-track Ride Quality Testing • Track geometry measurements • NUCARS Modeling • Comparison of NUCARS simulations to on-track test results to determine whether the vehicle performance and ride quality can be linked to specific track geometry features Phase II of the project will use the NUCARS simulations and data collected on transit systems during Phase I to train PBTG neural networks, and the PBTG model’s ability to predict ride quality. Specific Phase II tasks include (1) using the Phase I NUCARS simulations and on-track test results to train PBTG neural networks to predict ride quality and (2) identifying track locations where track geometry maintenance could improve ride quality. Phase II will also include similar on-track tests, NUCARS simulations, and PBTG analyses for another transit authority using another vehicle type. 3 NUCARS is a registered trademark of Transportation Technology Center, Inc.

3 1.1 Ride Quality Literature Survey In Phase I of this work, TTCI conducted a literature survey to identify how other transit authorities around the world measure and assess passenger ride quality and passenger ride comfort. Although this project is primarily concerned with rail passenger ride quality, the survey included a review of automobile passenger ride quality analysis techniques. The research addressed passenger ride quality and comfort on transit authorities for a range of passenger rail operations, from light right systems to typical intercity transportation. Therefore, the literature survey encompassed a wide range of possible conditions related to passenger ride quality. The appendix contains the literature survey. 1.2 Vehicle Characterization and Ride Quality Testing TTCI partnered with Dallas Area Rapid Transit (DART) to participate in this research. DART provided support to the project by providing a test vehicle for TTCI to perform characterization and ride quality tests. A typical passenger rail vehicle operating on the DART system was selected and fully characterized. The data obtained from the characterization studies was used to develop a NUCARS model representing the vehicle. The characterized vehicle was equipped with instrumentation to collect passenger ride quality data using accelerometers and various displacement transducers. Track geometry measurements were collected within two weeks of ride quality measurements and used as comparisons with predictions from the NUCARS model and for future PBTG neural network training. 1.3 NUCARS Modeling NUCARS is a general multibody rail vehicle dynamics computer simulation model. It is designed to simulate the dynamic interaction of any rail vehicle with any track. The user may select any number of bodies, degrees of freedom, and connection elements to describe a vehicle and track system. NUCARS can be used to analyze the dynamic interaction of rail vehicles and track to predict stability, ride quality, vertical and lateral dynamics, and steady state and dynamic curving response. The program includes detailed nonlinear models of wheel/rail interaction and suspension response, with wheel/rail interaction based on Kalker’s complete nonlinear creep theory.4 Applications of NUCARS include vehicle design, safety performance evaluation, rail vehicle and track research, derailment investigation, and general simulation of mechanical systems. Simulations of any type of freight, passenger, transit, and locomotive rail vehicles are possible. Track simulations may include hypothetical track geometries or measured track supplied by the user, including turnouts and guard rails. 4 Kalker, J.J., 1967. “On the Rolling Contact of Two Elastic Bodies in the Presence of Dry Friction,” Doctoral Thesis, Delft University, The Delft, Netherlands.