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Rail Temperature Prediction for Track Buckling Warning
Yu-Jiang ZHANG
ENSCO, Inc., 5400 Port Royal Road, Springfield, VA 22151
Phone: (703) 321 4506 Fax: (703) 321 7619
E-Mail: [email protected]
Leith AL-NAZER
U.S. DOT, Federal Railroad Administration, Office of Research and Development
RDV-31 Mail Stop 20, 1120 Vermont Ave., NW, Washington, DC 20590
Phone: (202) 493-6384 Fax: (202) 493-6333
E-Mail: [email protected]
August 29 - September 1, 2010
AREMA 2010 Annual Conference
Orlando, FL
ABSTRACT
The Federal Railroad Administration sponsored the development of a model and system for
predicting rail temperatures using real time weather forecast data and predefined track
parameters. The objective is to aid railroads in making more informed decisions on slow speed
orders when the rail temperatures are expected to exceed certain limits.
The system has been validated using both forecast and locally observed weather data.
The predicted rail temperatures were found to be within reasonable ranges, giving confidence in
the validity of the model algorithm. When weather forecast data is used as inputs, the deviations
in predicted rail temperatures grow larger as the uncertainties are inherited from the weather
forecast data.
The system was tested on Amtrak's Northeast Corridor (NEC) for several periods in
2007 and 2008. In late 2008, the demonstration was extended to selected locations on Union
Pacific (UP) and Burlington Northern Santa Fe (BNSF) tracks in Western and Midwestern
states. The validation analysis is mainly focused on several locations in Amtrak's NEC where
multiple weather/rail temperature stations were installed, data recorded and made available for
this project.
Recent developments have included further refinements of the model algorithm by
incorporating additional parameters and addressing broader track conditions. The future effort
could focus on addressing uncertainties in weather forecast data in order to optimize the
performance of the rail-weather system.
In this paper, major improvements in the model algorithm are discussed; demonstration
results are presented; and the main features of the real-weather system are introduced.
1. INTRODUCTION
Track buckling related derailments are very costly to the railroad industry. According to the FRA
Office of Safety database, there were over 150 derailments related to track buckles and/or sun
kinks between 2005 and 2009, resulting in over $43 million in damages.
There are guidelines in AREMA's Manual for Railway Engineering for rail installation and
track maintenance practices for continuous welded rails (CWR) to minimize the risk of track
buckling. However, with all the guidelines followed, precautions still need to be taken in
circumstances when a combination of factors becomes conducive to track buckling. For
example, on hot summer days railroads will impose slow orders when ambient temperatures
reach a certain limit. Some railroads issue slow orders based on the ambient temperature from
weather forecasts while others send out track inspectors to measure rail temperatures before
issuing slow orders.
The rationale of issuing slow orders using ambient temperature is based on the belief
that the rail temperature may rise 35 °F above the ambient temperature. If the rail temperature
becomes substantially higher than the stress free rail temperature, or rail neutral temperature
(RNT), longitudinal forces can build up and accentuate the risk of track buckling.
The practice of slow orders is effective in reducing track buckling related derailments and
associated costs. However, excessive slow orders and subjective inspections cost the railroad
industry many millions of dollars each year. Excessive slow orders can also be an issue for high
density tracks, possibly impacting the nation’s economic well-being.
For more reliable determination of slow orders and assessment of track buckling risk, the
FRA has sponsored the development of a model for predicting rail temperatures based on real-
time meteorological forecast data. The model was validated using both forecast and locally
observed weather data. The predicted rail temperatures were found to be within reasonable
ranges. The model has been implemented into a web-based rail-weather application. It was
demonstrated and tested on Amtrak's NEC in 2007. Further trials were conducted at selected
locations on UP and BNSF networks in late 2008. Most of these test locations have local
weather stations and other wayside measurement systems continuously collecting weather and
track data including rail temperature. The model has been implemented and is predicting rail
temperatures for the entire US continent.
The model is based on a transient heat transfer process in which the energy balance
continuously changes, causing the rail temperatures to rise or fall [1].
This paper presents major improvements in the model algorithm made through recent
research efforts. It also discusses some sample results produced by the model using measured
weather data. Future challenges with respect to model improvements will also be highlighted.
The main features of the rail-weather system are briefly introduced.
2. RAIL STRESSES AND BUCKLING RISK
Track buckling has been a phenomenon associated with CWR since they were introduced in
Europe in the 1950s. CWR track can buckle in both the vertical and lateral planes. A track is
vulnerable to buckling when the rails are subject to longitudinal compressive stresses,
coexisting with a combination of other influence factors, such as lack of track support,
deterioration of ties, imperfections in track geometry, and excitation of trains.
The stress state of CWR is related to the instantaneous rail temperature and RNT, a
temperature at which the rails are stress free. The initial RNT is set at rail installation to a level
higher than the anticipated rail temperature range to compensate the thermal expansion and to
lower buckling risk. It varies among railroads but can be as high as 110 °F (43 °C). When the
instantaneous rail temperature is below the RNT, the rails will be subject to tensile stresses.
Otherwise the rails will have compressive stresses.
Although the rail stresses can result from several sources, the single largest source of
compressive stresses in rails is the thermal expansion, which is attributed to the difference
between instantaneous rail temperature and the RNT. Instantaneous rail temperature varies
significantly from day to day and at different times of the day. It can exceed 150 °F on hot
summer days. The RNT is supposed to remain relatively stable. However, once the track is
subject to train traffic, the RNT can change over time and even at different times of the day.
Measurements on a tangent concrete-tie track on Amtrak's NEC showed that the RNT could
vary about 10 oF on any day. The variation in RNT can be even greater on both lateral and
vertical curves. The RNT variation on vertical curves is mainly due to train braking or
acceleration and that on lateral curves is attributed to track "breathing" caused by train traffic
and weather conditions.
Factors influencing track buckling risk has been graphically depicted in the past research [1]
as shown in Figure 1. Buckling risk rises for any or a combination of these factors:
• At curves where the RNT may have dropped
• High rail temperatures
• Track misalignments
• Train hunting or unfavorable vehicle/track interactions
• Train braking and acceleration
• Track tamping and insufficient stabilizing
• Spot track repairs and maintenance which can cause either the RNT or the track
strength to change
• Lack of track support: localized weak track foundation, lack of shoulder ballast
• Abrupt changes in track stiffness after a prolong rainy period in poorly drained areas
• Transitions between regular track and bridges, tunnel and crossings.
Compressive rail longitudinal stress
Insufficient lateral track panel shift resistance
Tracking Buckling
Loss of shoulder ballast
Other factors
Maintenance disturbance Traffic
Rail temperature
Rail Neutral temperature
Weather Rail size, shape, orientation, and other physical/ environmental factors
External excitation & alignment imperfections
Traffic & environmental parameters
Figure 1 Factors Influencing Track Buckling
It is worth emphasizing that a combination of several of the factors shown above is
important in promoting track buckling. Train presence is a key contributor to track buckling
because the energy input will accentuate the track buckling forces. Theoretical analyses and
tests indicated that without train energy, the rail temperature would have to rise significantly
above the RNT to buckle an average track [2].
Track buckling can happen in a few of seconds under train load. Unfortunately, it is difficult
to pinpoint the track defects after a derailment because much of the evidence is often destroyed
during the rescue and clean up process before the investigation begins. There have always
been debates on whether track buckling caused the train to derail or the train derailment
buckled/damaged the track. Regardless, track owners have devoted significant efforts into
managing track buckling risks. Railroads have instituted heat management programs involving
measuring and monitoring rail temperatures [3]. Maintenance practices for buckling prevention
have been adopted and continuously improved by the railroad industry [3]-[7]. Extensive
research into buckling theory [2], [8]-[10] has helped the development of computerized tools for
buckling risk analysis [11]. Despite all these efforts, track buckling related derailments still
occur. The number of track buckling/sun kink caused derailments in the US between 2005 and
2009 is shown in Figure 2.
Number of Track Buckling Caused Derailments2005 - 2009
27
55
35
15
21
0
10
20
30
40
50
60
2005 2006 2007 2008 2009
Year
Figure 2 Track Buckling/Sun Kink Caused Derailments
(based on the FRA rail incident database)
Of the 153 derailment accidents shown in Figure 2, approximately two-thirds occurred
between 1 pm and 4 pm. It is worth mentioning that the ambient temperature at the time of
derailments ranged from 40 to 106 oF. In issuing slow orders, a common practice is to assume
the rail temperature would rise 30 °F above the ambient temperature. This assumption is proven
to be valid only for the near extreme scenarios. The measured rail temperature and weather
data showed that rail temperature could rise 40 oF above the ambient temperature on clear and
calm days, and only go several degrees above the ambient temperature on clear but windy
days. Of course, the change of RNT could have contributed to a large portion of these buckling
incidents. Unfortunately, the RNT at time of derailment cannot be determined once the track has
buckled, resetting the RNT to a new value.
The model discussed in this paper provides physical, quantitative indication on how high the
rail temperature will likely be for a specific set of weather conditions for any given hour of the
day. For this, more weather parameters in addition to ambient temperature have to be
incorporated into the rail temperature prediction model.
3. RAIL TEMPERATURE PREDICTION MODEL
3.1 Development and Validation of the Model Algorithm
The technical approach chosen for the model is to quantify the rail heating process due to the
sun. The model makes use of weather forecast data from a numerical weather model to project
rail temperatures for the next 12 hours at 30 minute intervals. The technical details of the model
have been reported elsewhere [1] & [12].
The model algorithm was found to be valid as evidenced in the predicted results reported in
[1]. Figure 3 shows some sample predictions using measured weather parameters from the
weather station established for model development.
The model was implemented and tested in several continuous periods between 2007 and
2008 for 10 US northeast states covered by Amtrak's NEC. Amtrak installed rail-weather
stations at two locations which provided data for model validation.
7/26/06 7/27/06 7/28/06 7/29/06 7/30/06 7/31/060
10
20
30
40
50
60
Predicted Rail Temp (°C)Measured Rail Temp (°C)Air Temp (°C)Wind Speed (m/s)
7/26/06 7/27/06 7/28/06 7/29/06 7/30/06 7/31/060
10
20
30
40
50
60
Predicted Rail Temp (°C)Measured Rail Temp (°C)Air Temp (°C)Wind Speed (m/s)
Figure 3 Predicted Rail Temperatures Compared with Measured Data [1]
The model generated rail temperature predictions using weather forecast data from a high-
resolution mesoscale metrological model. Predictions for 10 days in September 2007 are given
in Figure 4. As expected, the spread in the predictions was wider than that generated using
locally measured weather data. The predicted rail temperature peak for September 2, 2007, was
about 10 °F higher than the recorded value. Detailed examination of the data revealed that for
this particular day the weather forecast over predicted the solar radiation but under predicted
wind speed. These are two of the three core weather parameters in the rail temperature model.
The combination of over predicting the former and under predicting the latter contributed to the
seemingly large error for this particular day. A review of the rail temperature predictions for the
summer of 2007 further revealed that the model performed reasonably well in predicting the
timing of peak rail temperatures. For most days, the predicted time of peak rail temperature was
within 45 minutes of the time at which it was observed in measured data. The deviation can be
attributed to several sources including weather forecast, locality parameters and limitations of
the model itself.
Measered vs Predicted Using Forecast Weather Data for Monmouth Junction, NJ
40
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120
140
9/1/
07 0
:00
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/07
0:00
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0
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/07
0:00
Time
o F
Air Temp Measured Rail T Predicted Rail T
Figure 4 Predicted Rail Temperatures using Forecast Data, Sep. 2007
Further efforts were devoted to improving the model algorithm. Areas of improvement for the
model include:
• More precise definition of rail surface area exposed to radiation, convection and
conduction heat transfer;
• Refinements of surface emissivity and convection coefficient;
• Inclusion of heat exchange between rail and ballast; and
• Inclusion of solar angles based on time of the day and day of the year for
computation of solar energy radiated into the rails
These changes were implemented in a stand alone program which was used for model
development. The improved model output is shown in Figure 5 for the same 10 days of
September 2007. The predictions for September 2, 2007, matched the recorded values
reasonably well. However, the model appeared to over predicted rail temperature for September
1, 2007. The recorded weather data showed that solar radiation fluctuated violently during the
hottest period of that day. The sudden and steep drops in solar radiation may not always be felt
by the model due to the 15 minute sampling interval for the recorded weather parameters being
input into the model. On the other hand, the solar radiation forecast for this particular day were
lower than the measured values, but without the sudden dips. Therefore, the model prediction
using forecast data seemed to match the recorded data better, although it was a result of error
cancellation.
9/1/07 9/2/07 9/3/07 9/4/07 9/5/07 9/6/07 9/7/07 9/8/07 9/8/07 9/10/070
40
50
60
70
80
90
100
110
120
Predicted Temp (°F)Actual Temp (°F)Air Temp (°F)
9/1/07 9/2/07 9/3/07 9/4/07 9/5/07 9/6/07 9/7/07 9/8/07 9/8/07 9/10/070
40
50
60
70
80
90
100
110
120
Predicted Temp (°F)Actual Temp (°F)Air Temp (°F)
Figure 5 Predicted Rail Temperatures Using Recorded Data, Sep. 2007
The new model algorithm was also applied to seven locations where recorded weather data
and rail temperature data was available. These locations are on Amtrak's NEC, from
Washington, DC, to Boston, MA. Model predictions for Monmouth Junction, NJ, for 13 days of
August 2009 are shown in Figure 6.
60
80
100
120
140
Predicted Temp (°F)Actual Temp (°F)Air Temp (°F)Wind Speed (m/s)
8/8/
09
8/9/
09
8/10
/09
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/09
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/09
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/09
40
8/14
/09
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/09
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/09
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/09
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/09
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/09
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Predicted Temp (°F)Actual Temp (°F)Air Temp (°F)Wind Speed (m/s)
8/8/
09
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09
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/09
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/09
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40
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/09
Figure 6 Predicted Rail Temperatures Using Recorded Data, Aug. 2009
The results seem to indicate that the model has captured the major modes of the heating
and cooling process of the rails exposed to the energy from the sun and surroundings.
However, on some days the model predictions still deviate from the actual rail temperatures,
even using recorded weather parameters. Factors influencing the accuracy of the model
predictions have been discussed previously [1]. Since the model has been developed with
practical application in mind, the parameters that are difficult to obtain from typical weather
service providers have not been extensively investigated. Locality parameters, such as
surrounding condition, blockage of wind by trees or structures, and elevation of the track, have
not be built into the model as those parameters can vary from one location to another.
Rail material properties and surface conditions also influence the predicted results but their
effects do not appear to be very significant. Wind is one of the dominant parameters affecting
rail heating/cooling process. The wind speeds at the measurement point 30 feet above surface
and at the rail surface differ considerably. The direction of the wind for a particular location may
change every few minutes. There are models for interpolating wind speeds at different heights
above the ground. However, the huge variation in locality parameters often renders these
mo
epth for heat conduction and
computing the body volume for heat transfer is extremely onerous.
t bordering
rail
dels invalid for the tens of thousands of the 9x9 km weather grids all over the US.
Attempts were also made to include the heat exchange between the rails and ballast. The
ballast itself was found to be more complicated to model than the rails because the uneven
surface of the rock particles makes it difficult to select radiation and convection coefficients. In
addition to the heat exchange with the rails, the ballast also conducts heat to and from the
underlying subballast and subgrade layers. Choosing appropriate d
3.2 Rail Temperature Prediction System
In parallel with the model algorithm improvement, the rail temperature predication system was
extended to cover the entire continental US. Figure 7 shows the configuration of the system. A
file server receives weather forecast data for 12 hours at 30 minutes intervals. A data processer
residing on the server processes and feeds the weather data to the rail temperature prediction
model. The predicted rail temperature and key weather parameters are stored in the database
which also resides on the same computer server. An internet server hosts the web-based rail-
weather application which retrieves data from the database and displays the results on the map-
based user interface. The user interface contains the interactive US rail network as well as the
base US geographical map layer. The weather forecasts cover the entire US continent for
continuous 9x9 km weather grids. For the rail-weather application, the grids no
road tracks were filtered out and a discrete base rail-weather map was created.
When accessing the application through internet, a user will be presented with a color coded
rail network map of the US continent. When the map is zoomed to a certain level, contiguous
9x9 km grids covering rail tracks become visible. The color of the grids was pre-programmed
according to the predicted rail temperatures, with red for alarm level, yellow for warning level,
and green for normal or safe level. The application also predicts buckling risks for different types
of rail track. This was done by referencing a risk matrix for various combinations of track
structure characteristics and rail temperatures. The risk matrix was built using the CWR SAFE
software developed by the FRA through Volpe National Transportation Systems Center.
File/DB Server
DB server
Weather ForecastReal time
12 hrs, 30 min int.
Rail T. Prediction Model
Data processor
Weather Data Files
Predicted Rail Temperature/Buckling Risk
File/DB Server
DB server
Weather ForecastReal time
12 hrs, 30 min int.
Rail T. Prediction Model
Data processor
Weather Data Files
Predicted Rail Temperature/Buckling Risk
Figure 7 Current Rail Weather System
The differences between locally observed weather parameters and forecast weather
parameters highlight the needs to have more frequent weather forecast updates in order to
reduce the uncertainties in rail temperature predictions. The requirement for short forecast
intervals and high-resolution weather forecast has posed challenges for long-term forecasts.
The current 12 hour forecasts are limited by the scope of the research project. Longer term
recasts would translate into lower accuracy and resolution in rail temperature predictions. fo
4. SUMMARY
A rail temperature prediction model has been developed and implemented as a web-based
application. The algorithm was verified and calibrated using locally measured weather and rail
temperature data. After several periods of field trials, the model has been further improved by
refining several key parameters using data from additional wayside stations. The predicted
results from the improved model seem to capture the trend of the rail temperature. Especially,
the model predicted the times of the rail temperature peak reasonably well. This can help
railroads reduce the window of slow orders. However, challenges still remain for the model to
accurately account for locality parameters. It is impractical to build the locality data into the
model due to vast spread of the data. In addition, for scientific rationale the model has been
validated using measured weather data. When it is applied to the entire US continent, the
spread in predicted rail temperature is expected to widen. Further, the real time application uses
weather forecast data to predict rail temperatures forward. The system will inevitably inherit the
uncertainties from the weather forecast data. Dealing with these uncertainties appears to reside
in means other than in the mechanistic modeling.
on, Maintenance,
strumentation and Analysis Support for Railroad Safety Research program.
1. Z
roceedings of AREMA 2007 Annual Conference, September 9-12,
2. K
oncepts" Transportation Research Board Proceedings, No.
3. T licy", Proceedings
5. ACKNOWLEDGEMENTS
The research work presented in this paper is sponsored by the U.S. Department of
Transportation’s FRA Office of Research and Development, under the Operati
In
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AREMA committee 4 and 5. He had over 30 publications in internal journals and
conferences.
Office. He holds a bachelor’s degree in
electric engineering from the University of Virginia.
Brief Biography – Yu-Jiang Zhang, PhD
Mr. Yu-Jiang Zhang is a Staff Scientist at ENSCO, Inc. located in Springfield Virginia. He
has managed various R&D projects including vehicle/track performan
ement, track condition analyses, and maintenance planning.
Before joining ENSCO in 2001, Yu-Jiang worked as a senior engineer with Zeta-Tech
Associates, Inc.. In addition, he has held positions as technical manager, researcher and
college lecturer in three countries in railway and transportation fields. Yu-Jiang has a bachelor's
degree in automotive engineering, and a master's degree and a Ph.D. in Civil Engineering. He
is a member of
Brief Biography – Leith Al-Nazer
Leith Al-Nazer is a program analyst in the Track Research Division at the Federal
Railroad Administration, a position he has held for two years. Prior to joining the FRA, Leith
was a patent examiner at the Patent and Trademark
al
