Using Wheel Impact Load Detector Data to Understand Wheel...
Transcript of Using Wheel Impact Load Detector Data to Understand Wheel...
Brandon J. Van Dyk, Marcus S. Dersch, J. Riley Edwards, Conrad Ruppert, Jr., and
Christopher P.L. Barkan
Transportation Research Board 93rd Annual Meeting
Washington, D.C.
14 January 2014
Using Wheel Impact Load Detector Data
to Understand
Wheel Loading Environment
Slide 2Using WILD Data to Understand Wheel Loading Environment
Outline• Objectives of quantifying load amplification
• Wheel load distribution on shared infrastructure
– Causes of load amplification
• Identification of load amplification factors
– Dynamic wheel load factors
– Impact factors
• Wheel loads on curved track
• Conclusions and Acknowledgements
Slide 3Using WILD Data to Understand Wheel Loading Environment
Objectives
• Use wheel impact load detector data to understand
wheel loading environment, leading to improved design
of track structure that reflects actual loading demands
• Characterize and quantify increase above static wheel
load due to several factors
– Temperature
– Speed
– Irregularities
• Identify dynamic and impact wheel load factors
• Summarize alternative data collection methods
Slide 4Using WILD Data to Understand Wheel Loading Environment
Wheel Impact Load Detectors (WILD)
• Sixteen sets of strain gauges to detect
full rotation of most wheels
• For each wheel,
• Labels by vehicle type
• Measures speed, nominal (static)
wheel load, and peak wheel load
Slide 5Using WILD Data to Understand Wheel Loading Environment
WILD Data Provided by Amtrak and UP
Slide 6Using WILD Data to Understand Wheel Loading Environment
Traffic Distribution – Nominal Wheel Loads
Source: Amtrak – Edgewood, MD (November 2010)
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Nominal Vertical Load (kips)
Freight LocomotivesIntermodal Freight CarsPassenger CoachesPassenger LocomotivesOther Freight Cars
Empty
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Loaded
Cars
10 kips ≈ 45 kN
Slide 7Using WILD Data to Understand Wheel Loading Environment
Traffic Distribution – Peak Wheel Loads
Source: Amtrak – Edgewood, MD (November 2010)
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Peak Vertical Load (kips)
Freight LocomotivesIntermodal Freight CarsPassenger CoachesPassenger LocomotivesOther Freight Cars
10 kips ≈ 45 kN
Slide 8Using WILD Data to Understand Wheel Loading Environment
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Nominal vs. Peak Vertical Load
Source: Amtrak – Edgewood, MD (November 2010)
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Peak Vertical Load (kips)
Freight Locomotives
Passenger Locomotives
Non-Intermodal Freight Cars
10 kips ≈ 45 kN
Slide 9Using WILD Data to Understand Wheel Loading Environment
UNLOADED FREIGHT CARS
PASSENGER COACHES
LOADED FREIGHT CARS
Source: Amtrak – Edgewood, MD (November 2010)
Effect of Traffic Type on Peak Wheel Load
10 kips ≈ 45 kN
Slide 10Using WILD Data to Understand Wheel Loading Environment
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Peak Vertical Load (kips)
AprilOctoberJanuaryJuly
Seasonal Variation of Freight Wheel Loads
Source: Union Pacific – Gothenburg, NE (2010) 10 kips ≈ 45 kN
Slide 11Using WILD Data to Understand Wheel Loading Environment
0.0%
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Peak Vertical Load (kips)
AprilOctoberJanuaryJuly
Source: Union Pacific – Gothenburg, NE (2010)
Seasonal Variation of Highest Freight Wheel Loads
10 kips ≈ 45 kN
Slide 12Using WILD Data to Understand Wheel Loading Environment
Dynamic vs. Impact Load
• Static load – load of vehicle at rest
• Quasi-static load – static load at speed, independent
of time
• Dynamic load – high-frequency effects of wheel/rail
interaction, dependent on time
– E.g., 𝑫𝒚𝒏𝒂𝒎𝒊𝒄 𝑭𝒂𝒄𝒕𝒐𝒓 = 𝟏 +𝟑𝟑 𝒔𝒑𝒆𝒆𝒅 (𝒎𝒑𝒉)
𝟏𝟎𝟎 𝒅𝒊𝒂𝒎𝒆𝒕𝒆𝒓 (𝒊𝒏.)
• Impact load – high-frequency and short duration load
caused by track and vehicle irregularities
– E.g., increase of 200% (found in AREMA Chapter 30)
Slide 13Using WILD Data to Understand Wheel Loading Environment
Effect of Speed on Wheel Load
Source: Amtrak – Edgewood, MD (November 2010) 10 kips ≈ 45 kN, 10 mph ≈ 16 kph
Slide 14Using WILD Data to Understand Wheel Loading Environment
Comparison of Dynamic Wheel Load Factors
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Speed (mph)
TalbotIndian RailwaysEisenmannORE/BirmannGerman RailwaysSouth African RailwaysClarkeWMATASadeghiAREMA C30
10 mph ≈ 16 kph
Slide 15Using WILD Data to Understand Wheel Loading Environment
Dynamic Wheel Load Factors
Source: Amtrak – Edgewood, MD (November 2010) 10 mph ≈ 16 kph
Slide 16Using WILD Data to Understand Wheel Loading Environment
Other Effects on Peak Wheel Load
Source: Amtrak – Mansfied, MA (November 2010) Passenger Coaches
10 kips ≈ 45 kN, 10 mph ≈ 16 kph
Poor Wheel
Condition
Slide 17Using WILD Data to Understand Wheel Loading Environment
More than a Dynamic Factor: Impact Factor
0.4%
𝑰𝒎𝒑𝒂𝒄𝒕 𝑭𝒂𝒄𝒕𝒐𝒓 (𝑰𝑭) =𝑷𝒆𝒂𝒌 𝑳𝒐𝒂𝒅
𝑺𝒕𝒂𝒕𝒊𝒄 𝑳𝒐𝒂𝒅
Source: UPRR – Gothenburg, NE (January 2010) 10 kips ≈ 45 kN
Slide 18Using WILD Data to Understand Wheel Loading Environment
Thoughts on Impact Factor
• AREMA Chapter 30 Impact Factor (300%) exceeds
majority of locomotive and loaded freight car loads
– Greater impact factor may be necessary for lighter
rolling stock (passenger coaches and unloaded
freight cars)
– Wheel condition significantly affects load
– Speed causes highest impacts to be higher
• Evaluating effectiveness of impact factor dependent
on static weight of car
Slide 19Using WILD Data to Understand Wheel Loading Environment
Other Factors Affecting Wheel Loads
• Moisture and temperature
• Position within the train
• Curvature
• Grade
• Track quality
Instrumented Wheel Set
Truck Performance Detector
UIUC Instrumentation Plan
Need alternative
data collection
methods
Slide 20Using WILD Data to Understand Wheel Loading Environment
Alternative Data Collection Methods
• Instrumented Wheel Set
– Vehicle-mounted; collects data at 300 Hz
– Measures vertical and lateral loads in tangent, curved,
and graded sections
• Truck Performance Detector
– Wayside detector in tangent and curved sections
– Measures vertical and lateral loads of each wheel
• UIUC Instrumentation Plan (thus far implemented at TTC)
– Instrumented track in tangent and curved sections
– Continuously measures each wheel in multiple locations
for vertical load, lateral load, and various deflections
Slide 21Using WILD Data to Understand Wheel Loading Environment
IWS: Wheel Loads on Left-Handed Curve
Source: AAR (2006)
CURVE
10 kips ≈ 45 kN
Slide 22Using WILD Data to Understand Wheel Loading Environment
Lateral Loads within Left-Handed Curve
Source: AAR (2006) 10 kips ≈ 45 kN, 0.1 in = 2.54 mm
Slide 23Using WILD Data to Understand Wheel Loading Environment
Conclusions
• Wheel impact load detectors can be used to characterize
the loading environment, leading to improved track design
• Colder temperatures do not increase the majority of the
wheel loads; winter conditions do increase highest
impact loads
• Dynamic and impact wheel load factors can be compared
and objectively evaluated, resulting in improved decision-
making in design
• The use of technology typically reserved for monitoring
mechanical health can also provide increased insight into
track design and maintenance
Slide 24Using WILD Data to Understand Wheel Loading Environment
Acknowledgements
• Funding for this research has been provided by the
Federal Railroad Administration (FRA)
• Industry Partnership and support has been provided by
– Union Pacific Railroad
– BNSF Railway
– National Railway Passenger Corporation (Amtrak)
– Amsted RPS / Amsted Rail, Inc.
– GIC Ingeniería y Construcción
– Hanson Professional Services, Inc.
– CXT Concrete Ties, Inc., LB Foster Company
– TTX Company
• For assistance in data acquisition
– Steve Crismer, Jonathan Wnek (Amtrak)
– Steve Ashmore, Bill GeMeiner, Michael Pfeifer (Union Pacific)
– Teever Handal, (PRT), Kevin Koch (TTCI), Jon Jeambey (TTX)
• For assistance in data processing and interpretation
– Alex Schwarz, Andrew Stirk, Anusha Suryanarayanan (UIUC)
FRA Tie and Fastener BAA
Industry Partners:
Slide 25Using WILD Data to Understand Wheel Loading Environment
Questions
Brandon Van Dyk
Technical Engineer
Vossloh Fastening Systems America
e-mail: [email protected]
J. Riley Edwards
Senior Lecturer and Research Scientist
Rail Transportation and Engineering Center – RailTEC
University of Illinois at Urbana-Champaign
e-mail: [email protected]