Implementation of Rail Temperature Predictions on Amtrak. Authors

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1 Implementation of Rail Temperature Predictions on Amtrak Authors Radim Bruzek ENSCO, Inc Port Royal Road, Springfield, VA Phone: (703) Michael Trosino The National Railroad Passenger Corporation (Amtrak) 30th Street Station, Philadelphia, PA Phone: (215) Robert Wilson U.S. DOT, Federal Railroad Administration, Office of Research Development and Technology 55 Broadway, Cambridge, MA Phone: (617) Leith Al-Nazer U.S. DOT, Federal Railroad Administration, Office of Safety 1200 New Jersey Avenue SE, Washington, DC Phone: (202) Number of Words 2872 ABSTRACT This paper describes the implementation and use of daily rail temperature prediction reports by National Railroad Passenger Corporation (Amtrak). The rail temperature reports are generated by ENSCO, Inc. with a prediction model using weather forecast data and predefined track parameters whose development was sponsored the Federal Railroad Administration s (FRA s) Office of Research, Development and Technology (RDT). Following the validation of the models accuracy using ground truth rail temperatures measured in 2012 at 21 sites throughout its railroad network, Amtrak began using the rail temperature prediction reports in The reports are disseminated to both Amtrak track maintenance management and field personnel three times a day and provide advance notice of probable rail temperatures that would trigger heat inspections, and heat slow orders. These predictions help in planning resource allocation that may be required as part of Amtrak s Track Buckling Counter Measures program on the Northeast Corridor (NEC), Empire, Michigan and Springfield lines. 286 AREMA 2016

2 1. INTRODUCTION Derailments attributed to track buckles are not frequent, however they can be serious and costly incidents when they do occur. Track buckling is caused by high compressive forces in the rail due to thermal expansion of steel. There are multiple factors affecting the buckling phenomenon, mainly Rail Neutral Temperature (RNT), lateral track stability and the actual rail temperature. There are additional factors such as initial geometry conditions (especially alignment), traffic patterns, train braking, rail fastening and anchoring, and proximity to fixed assets such as bridge ends, switches and grade crossings. Track buckling is mostly associated with continuously welded rail (CWR) although it can occur on jointed rail as well. Proper CWR management is an important part of track buckling prevention and railroads are required to have in effect and comply with a CWR plan approved by the FRA. The plan has to address the installation, adjustment, maintenance, inspections of CWR and CWR joints, and training programs. One of the most important aspects of CWR management is achieving and maintaining a desired RNT, a temperature at which the rail is in a zero stress state. Its value is typically chosen somewhere in the range of temperatures that the rail is expected to experience in order to minimize the risk of heat related track buckles during hot weather months and pull-aparts during cold weather months. The anecdotal knowledge in the industry is that the RNT does not stay constant and tends to deteriorate (decrease) over time due to traffic and maintenance. As actual rail temperature rises above the RNT, the risk of a track buckle increases. Track with higher lateral resistance can withstand higher compressive stresses and therefore higher rail temperatures before it buckles. To minimize the risk and consequences of derailments on a day to day basis, heat inspections are conducted when rail temperatures are elevated. Slow orders are typically placed on track where rail temperatures reach given safety thresholds. 2. AMTRAK S TRACK BUCKLING COUNTER MEASURES PROGRAM In order to minimize risk of track buckles Amtrak has implemented a track buckling counter measures program. As part of the program Amtrak has installed a set of 30 wayside measure sites equipped with rail temperature sensors and a full commercially available weather station along with communication link and power. The stations are located at approximately 20 mile increments along the portions of the NEC right of way where the speed is greater than 90mph and also on Empire, Michigan and Springfield lines. The equipment records rail temperature and weather parameters in real time and provides this information to the personnel responsible for implementing Amtrak s heat order policy. Figure 1: Locations of Amtrak Wayside Measurement Sites AREMA

3 In 2008, rail temperature measured in real time was substituted for ambient air temperature as the trigger for heat slow orders. When high temperatures are anticipated designated personnel will monitor rail temperature and other relevant weather parameters, coordinate the placement of general heat related speed restrictions and any applicable latent heat restrictions at work zones and disturbed track locations and follow up with track supervisors to ensure that special heat inspections are initiated in each heat affected territory. The heat order policy is defined by the following structure: 1) Rail temperature of 128F (or 95F ambient air where wayside measurement station not installed or malfunctioned): Alert: Approaching thresholds values 2) Rail temperature >130F (or 98F ambient air where wayside measurement station not installed or malfunctioned): Level 2 alarm: apply 100mph general heat speed restriction, apply latent heat restrictions as defined in Amtrak Track Maintenance Manual (MW1000) for affected work zones, special heat inspections initiated in affected areas 3) Rail temperature >140F (or 105F ambient air where wayside measurement station not installed or malfunctioned): Level 1 alarm: apply 80mph general heat speed restriction, latent heat restrictions as defined in MW 1000 for affected work zones remain in effect, continue special heat inspections in affected areas More restrictive heat restrictions can be applied at specific locations where experience has shown that the track has compromised lateral stability or that problems may occur due to local track conditions. In addition, in 2013, Amtrak began using rail temperature prediction reports. The reports are generated by ENSCO, Inc. with a prediction model using weather forecast data and predefined track parameters whose development was sponsored the Federal Railroad Administration s (FRA s) Office of Research, Development and Technology (RDT). They provide advance notice of probable rail temperatures that would trigger heat inspections and heat slow orders. The reports are provided to Amtrak as part of a pilot program funded by the FRA RDT. 3. RAIL TEMPERATURE MODEL The rail temperature prediction model was developed with the motivation to more accurately predict rail temperature than the constant offset from ambient temperature method. The model uses the National Weather Service (NWS) data as weather forecast inputs and utilizes several additional weather and material parameters in addition to ambient air temperature: Intensity of solar radiation Solar angle Wind speed Sky temperature Heat absorptivity and emissivity of rail The model is based on heat transfer principles and calculates rail temperature based on the amount of energy absorbed from the sun and emitted via radiation and convection. Energy absorbed and emitted via conduction on the rail-tie-ballast interface is not considered. Detailed descriptions of the technical approach can be found in references (1) and (2). The predictions are granular; output is provided in 9x9 km grids which allow slow orders to be issued for specific milepost ranges. The model produces rail temperature prediction in 30-minute increments. The predictions are updated every three hours. Currently, the prediction model is using weather forecasts for the next 15 hours. The processing itself, however, takes almost three hours, therefore predicted rail temperature is available for 12 hours in advance for the 48 contiguous U.S. states. The predictions can be, however, expanded further in time or geographically with increased computational power. ENSCO, Inc. in cooperation with FRA RDT, CSXT and Amtrak conducted analyses to verify and validate the rail temperature prediction process by comparing the rail temperatures predicted by the 288 AREMA 2016

4 model and actual rail temperatures measured throughout the CSXT and Amtrak railroad network over the course of spring and summer of The dataset for all locations combined contained almost 160,000 hours of rail temperature measurements. The results showed overall good correlation of the model s predictions with the actual rail temperature measurements where over 50% and over 80% of the predictions were within 5F and 10F of the measure values, respectively. Limited number of large over prediction errors (of 30F and higher) also occurred. As over predictions they do not pose a safety risk of incorrect non-issue of heat related slow order. The majority of these large scale errors are inherited from the weather forecast and are related to inaccurate solar radiation forecast and unexpected cloud cover. Detailed results are available in references (3) and (4). 4. DAILY AND WEEKLY RAIL TEMPERATURE PREDICTION REPORTS There are two sets of reports generated: 1) Set of three daily prediction reports that provide a rail temperature outlook for a specific day and location 2) Weekly correlation report providing a summary of correlation between rail temperatures measured by the wayside stations and predicted by the model in the previous week The reports in PDF format contain predictions for 30 of the model s grids that correspond with locations of Amtrak wayside rail temperature measurement sites as shown in Figure 1. The architecture of the report generating service allows for an easy addition of more locations of interest or removal of current locations in the future. There are three daily prediction reports generated and distributed at 5:00 AM Eastern Time, 8:00 AM Eastern Time, and 11:00 AM Eastern Time. Since the model s outlook is only 12 hours ahead considering the three hour processing time, three reports are needed following each update of the weather forecast parameters to provide information about the progression of rail temperature throughout the entire day until 11:00 PM. The first report, however, contains prediction until 5:00 PM which includes the daily peak rail temperature in most cases. The two following reports refine the prediction for the daily peak and give additional information on the rate of the decrease in rail temperature later in the day till 8:00 PM and 11:00 PM respectively. It is important to note that because the processing of the weather parameters to produce the predicted rail temperature takes almost three hours of computation time, each report is generated for weather forecast parameters received three hours earlier. That is the 5:00 AM report is based on the 2:00 AM forecast parameters, 8:00 AM report is based on the 5:00 AM forecast parameters and the 11:00 AM report is based on the 8:00 AM forecast parameters. First pages of each report contain a summary table of times when the predicted rail temperature exceeds a given threshold for each site along with a total number of hours exceeding the given threshold for all the sites combined. The summary table is followed by individual plots with the predicted rail temperature at each of the 30 sites. The report also indicates whether the rail temperature is elevated at the end of the reported prediction period. Figure 2 illustrates the summary table described above. The example given is from the first generated report at 5:00 AM, and the table has the same format for all three reports. AREMA

5 Figure 2: Summary Table of Start and End Times of Elevated Rail Temperatures 290 AREMA 2016

6 Figure 3 illustrates the predicted rail temperature plot along with additional information for an individual site from the first generated report at 5:00 AM. The figure represents a page in the report that is generated for each of the 30 individual sites. Figure 3: Predicted Rail Temperature Plot for an Individual Site for 5:00 AM Report The subsequent reports at 8:00 AM and 11:00 AM also contain additional summary information about the changes from previous report in the start times, end times and durations of rail temperatures elevated above a given threshold. The plots for individual sites overlay the rail temperatures predicted for the current report with the temperatures reported by the previous reports to allow the user visually observe changes in prediction with updated weather parameters. AREMA

7 Figure 4: Predicted Rail Temperature Plot for an Individual Site for 8:00 AM Report 292 AREMA 2016

8 Figure 5: Predicted Rail Temperature Plot for an Individual Site for 11:00 AM Report In addition to the daily reports a weekly correlation report is generated every Monday morning. This report contains overlays of rail temperatures predicted by the model and measured at the 30 individual wayside measurement stations for the previous week as well as results of basic statistical analysis of the agreement between the two measured and predicted rail temperatures. An automated data transfer has been established between Amtrak and ENSCO to receive the rail temperature measurements from the 30 wayside locations on a weekly basis for comparison with the predictions. The weekly correlation report provides an ongoing evaluation of the performance of the rail temperature prediction model. Figure 6 shows an example of the weekly report. The first table gives overall correlation results for each individual sensor measurement with the predicted rail temperature. Mean, standard deviation and the maximum and minimum value of the difference between the prediction and the measurement are calculated. The remaining tables summarize temperatures exceeding given threshold through the week. AREMA

9 Figure 6: Example of a Weekly Correlation Report for an Individual Measurement Site 5. APPLICATION OF RAIL TEMPERATURE PREDICTIONS The reports are disseminated to both Amtrak track maintenance management and field personnel via and provide advance notice of probable rail temperatures that would trigger heat inspections and heat slow orders. The predictions help in planning resource allocation that may be required as a response to rail temperatures measured by the wayside stations but also provide redundant information on rail temperatures in case of wayside measurement site malfunction. Predicted rail temperatures offer an advantage in accuracy over relying on ambient air temperature only. On clear days with ample sunshine rail temperature can rise to much higher levels than on cloudy and rainy days. Also, the offset between daily peak ambient air and rail temperatures tends to statistically increase with increased rail temperature as shown in reference (5). The prediction model has also a high reliability to provide uninterrupted service 294 AREMA 2016

10 as it is a fully virtual system without a need of any sensors installed on the rail. The value of the prediction reports would be increased by a longer outlook of the model to give additional time for resource allocation planning. Ideally, the outlook would be increased enough to allow distribution of the rail temperature prediction for a given day on a previous evening. While such extension of the rail temperature prediction outlook is possible it would require significant expansion of computational power to process weather forecast parameters with a corresponding longer outlook. Additionally, the uncertainty of weather forecasts increase significantly with a longer outlook. Therefore the accuracy of the rail temperature predictions would most likely decrease as errors in the weather forecasts would be inherited by the rail temperature prediction model. 6. CONCLUSION Rail temperature prediction reports were implemented on Amtrak as part of a pilot program funded by the FRA RDT in Following Amtrak s changes in heat order policy in 2008 this represents another progressive step in track buckling mitigation. Rail temperature predictions offer additional layer of information that complement the network of rail temperature measurements. The advance notice of probable rail temperature help in planning resource allocation that may be required as part of Amtrak s Track Buckling Counter Measures program on the Northeast Corridor (NEC), Empire, Michigan and Springfield lines. The predictions also provide redundant information on rail temperatures in case of wayside measurement site malfunction allowing designated personnel not to rely only on ambient air temperatures. The value of the prediction reports to the railroad would be increased by a longer outlook of the model. In that case, the benefit of more advance warning would have to be weighed against an expected decrease in the accuracy of the predictions. Overall, the rail temperature prediction model can help railroads manage track buckle risk by being better prepared for high rail temperatures, slow orders and heat inspections. 7. ACKNOWLEDGEMENTS The development of the rail temperature prediction model and implementation of the rail temperature prediction reports at Amtrak was funded by FRA RDT. 8. REFERENCE 1. Zhang, Y., Clemenzi, J., Kesler, K., Lee. S., 2007, Real Time Prediction of Rail Temperature, Proceedings of AREMA 2007 Annual Conference, Sept 9-12, Chicago, IL 2. Federal Railroad Administration, 2008, Development of Rail Temperature Prediction Model, Research Results, RR Bruzek, R., Al-Nazer, L., Biess, L., Kreisel, L., 2014, Rail Temperature Prediction Model as a Tool to Issue Advance Heat Slow Orders, Proceedings of the AREMA 2014 Annual Conference, September 28-October 1, Chicago, IL. 4. Bruzek, R., Trosino, M., Al-Nazer, L., 2014, Targeted Heat Slow Orders using Rail Temperature Predictions, Proceedings of the 2014 APTA Rail Conference, June 15-18, Montreal, QC, Canada. 5. Bruzek, R., Trosino, M., Kreisel, L., Al-Nazer, L., 2015, Rail Temperature Approximation and Heat Slow Order Best Practices, Proceedings of the 2015 Joint Rail Conference, March 23-26, San Jose, CA. LIST OF FIGURES Figure 1: Locations of Amtrak Wayside Measurement Sites...1 Figure 2: Summary Table of Start and End Times of Elevated Rail Temperatures... 4 Figure 3: Predicted Rail Temperature Plot for an Individual Site for 5:00 AM Report... 5 Figure 4: Predicted Rail Temperature Plot for an Individual Site for 8:00 AM Report... 6 Figure 5: Predicted Rail Temperature Plot for an Individual Site for 11:00 AM Report... 7 Figure 6: Example of a Weekly Correlation Report for an Individual Measurement Site... 8 AREMA

11 Implementation of Rail Temperature Predictions on Amtrak Radim Bruzek (ENSCO) Mike Trosino (Amtrak) Robert Wilson (FRA RDT) Leith Al-Nazer (FRA RRS) 296 AREMA 2016

12 Background Thermal expansion/contraction of rail an increasing concern due to large longitudinal loads Rail Neutral Temperature the temperature at which the rail longitudinal force is zero Pull Apart broken rail in the winter Track buckles in the summer NEUTRAL TEMPERATURE Track Buckling Risk RAIL TEMPERATURE BUCKLING STRENGTH Track Buckling Risk Track Buckling Risk NEUTRAL TEMPERATURE LATERAL RESISTANCE NEUTRAL TEMPERATURE LATERAL RESISTANCE RAIL TEMPERATURE RAIL TEMPERATURE Track Buckling Risk Factors: Rail Temperature significantly above Neutral Temperature / High longitudinal forces Weakened track conditions / lateral resistance Train loads/dynamics Amtrak s Approach to Counter Buckling Installed 30 wayside measurement sites Rail temperature sensors and a weather station at each site Provides information to personnel responsible for heat order policy in real time Locations: North East Corridor Empire line Michigan line Springfield line Placed in ~20 mile increments Amtrak s Approach to Counter Buckling Measured rail temperatures are used to issue heat slow orders since Rail Temperature Threshold General Speed Restriction Comment 128F NONE ALERT 130F 100 MPH LEVEL 2 ALARM 140F 80 MPH LEVEL 1 ALARM Restrictions are applied for the block(s) governed by station exceeding the threshold. Heat inspections are performed under level 2 and 1 alarms AREMA

13 Amtrak s Approach to Counter Buckling Rail temperature prediction reports implemented since 2013 Reports are generated by ENSCO using a model whose development sponsored by FRA RDT The reports provide advance warning of rail temperatures that would trigger heat inspections and heat slow orders. The benefits are: planning resource allocation redundancy in case of measurement site malfunctions Rail Temperature Prediction Model Based upon fundamental heat transfer theory k A G s s s Solar Radiation h A T T A T T conv c r Convection r r sky Sky Radiation dtr cv dt Rail Temperature Prediction Model Uses Weather Data Model from the ENSCO Aerospace Division CSX 23 Wayside Sites Model Validation Amtrak 21 Wayside Sites Parameters used: Air Temperature Solar radiation Solar angle Wind speed Sky temperature Heat absorptivity and emissivity of rail Prediction model output: 30,000 9x9 km grids across the USA that can be converted to subdivisions and mileposts 30-minute time increments 12 hour ahead rail temperature forecast Model predictions were compared to actual wayside measurements On average there was a 5F error (during daylight hours model typically over predicts - conservative) Rail Temperature Model Validation Daily Report Examples Air Temperature Solar Radiation First Daily Prediction report sent out at 5am Provides prediction until 5pm Predicted Value Measured Value 298 AREMA 2016

14 Daily Report Examples Daily Report Examples Second Daily Prediction report follows at 8am Extends prediction until 8pm Third Daily Prediction report follows at 11am Extends prediction until 11pm Weekly Correlation Report Application of Prediction Reports Weekly report generated Monday morning Compares predicted and measured temperatures for the past week 1. Daily and weekly reports are generated for each of the 30 Amtrak sites 2. Predictions are disseminated to both Amtrak track maintenance management and field personnel via 3. Provides advance notice of probable rail temperatures that would trigger heat inspections and heat slow orders 4. Helps in resource and personnel allocation planning 5. Provides redundant information in case of wayside measurement site malfunction. Model is a virtual system, no need for equipment on rail it is more reliable to provide uninterrupted service Conclusions 1. Amtrak is the first to implement the rail temperature prediction model in daily operations 2. Amtrak is using a hybrid approach the prediction model and wayside measurements working together a) The prediction model provides advance warning, redundancy and allows for resource allocation planning b) Wayside measurement sites trigger actual speed restrictions and inspections 3. The prediction outlook can be increased with high performance computing hardware. AREMA

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