wheel damage prediction for iron ore vehicles using vehicle …/file/wheel damage prediction...

Wheel Damage Prediction for Iron Ore Vehicles

using Vehicle Dynamics SimulationSebastian Stichel ; Saeed H-Nia KTH-Royal Institute of Technology, Stockholm, [email protected]

mailto:[email protected]

Background

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LOCOMOTIVE

• Locomotive wheels are regularly checked in the workshop (ca. every 26 000

km) for RCF and possible reprofiling.

• Wheel life is about 300 000-400 000 km.

• According to a recent field study at least 75% of the wheels of the locomotives

are reprofiled after 50 000km due to excessive surface RCF

WAGON

• Wheels regular Inspections: every 80 000 km

• Wheel Re-profiling: 250 000 km

• Wheel Life: 1 000 000 km

Background

9/11/2018 3A. Ekberg. 20161214-LKAB-Lokhjul. A report on. Iron Ore line – Damage on loco wheels. Chalmers University of Technology. (2016).

Hertzian line contact

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• Small strains• Homogeneous, isotropic, linearly elastic bodies• Half-space assumption• Constant curvature of the bodies in contact

(quadratic surfaces at the vicinity of contact)• Quasi-identical material.

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𝜏𝜏1 =12

𝜎𝜎𝑥𝑥 − 𝜎𝜎𝑧𝑧 2 + 4𝜏𝜏𝑥𝑥𝑧𝑧212

Hertzian line contact

𝜏𝜏1 𝑥𝑥, 0 = 𝜇𝜇𝜇𝜇0

At the surface (𝑧𝑧 = 0)

FIsurf

All wheel rotations with positive FIsurf value

Calculating RCF risk with Shakedown theory

6

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Effect of friction on RCF

0

10

20

30

40

50

60

70

80

90

100

0,3 0,4 0,5 0,6 0,7 0,8

RC

F PR

OB

AB

ILIT

Y [%

]

FRICTION COEFFICIENT

Inner WheelOuter Wheel

𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃 𝑃𝑃𝑖𝑖 𝐽𝐽𝐽𝐽𝑃𝑃𝐽𝐽𝑖𝑖𝐽𝐽𝑃𝑃 𝐽𝐽𝑜𝑜𝑜𝑜𝐽𝐽𝑃𝑃𝑃𝑃 & 𝑜𝑜𝐽𝐽𝜇𝜇𝑃𝑃𝑃𝑃 𝑇𝑇𝐽𝐽𝐽𝐽𝑖𝑖𝑃𝑃𝑃𝑃𝑇𝑇 2014

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Effect of Track gauge widening on RCF

2 28 10

7580

0

10

20

30

40

50

60

70

80

90

1430 1435 1440 1445 1450 1455

RC

F P

RO

BA

BIL

ITY

[%]

TRACK GAUGE [MM]

𝑃𝑃𝐽𝐽𝑃𝑃𝑃𝑃𝑃𝑃𝑖𝑖𝑇𝑇𝑃𝑃𝑃𝑃 𝑃𝑃𝑖𝑖 𝐼𝐼𝐼𝐼𝐼𝐼𝐼𝐼 𝐶𝐶𝐽𝐽𝑖𝑖𝑜𝑜. 2013

Wheel damage calculation methodology

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Wheel-rail contact response

Wear & RCF calculation

Profile updating

Desired distance?

Sim. set design *Veh.-trc. Sim.

New wear-stepFinished

* www.GENSYS.se

Simulation set. design: Load-collective

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Load collective method (Simulation setups):

• Measured track design and irregularities• Stochastic friction level along the simulations with normal distribution around 0.4• Realistic speed profile as a function of track geometry• Measured rail profiles • Realistic traction and braking effort based on measured data and topography of the line• Verified MBS model of the track and the vehicle• Measured track stiffness• …



Profile updating

Sim. set design Veh.-trc. Sim.

-15 -10 -5 0 5 10 150

5

10

15

20

25

30Sweden

Gradient per mil

%

Surface shear stress

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Profile updating


• The damage calculation model considers:• Residual stresses based on Melan’s theorem• Partial slip condition at the contact patch

• The numerical model to calculate the shear stresses at the surface (i.e. FaStrip):• Non-linear growth of shear stresses from the stick zone to the slip zone• Fast as FASTSIM• Accurate in high spin and various contact geometries• Accurate slip velocity with respect to CONTACT

*M. Sichani, R. Enblom & M. Berg, An alternative to FASTSIM for tangential solution of wheel-rail contact. Vehicle System Dynamics, 54 (2016): 748-764.

𝜎𝜎𝑥𝑥 + 𝜎𝜎𝑥𝑥 𝑟𝑟 − 𝜎𝜎𝑧𝑧2𝜇𝜇0

2

+𝜏𝜏𝑥𝑥𝑧𝑧𝜇𝜇0

2

≤𝑘𝑘𝜇𝜇0

2


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Wear: Archard formula:𝑊𝑊 = 𝑘𝑘 𝑃𝑃�𝑆𝑆

𝐻𝐻, 𝑆𝑆 = 𝑜𝑜(𝑣𝑣𝑥𝑥,𝑣𝑣𝑦𝑦,𝜑𝜑, 𝜏𝜏,𝑉𝑉)



Profile updating


RCF:

• Check the exceedance of shear stress 𝜏𝜏𝑧𝑧𝑥𝑥2 + 𝜏𝜏𝑧𝑧𝑦𝑦2

in each contact mesh from the yield limit in shear.• Considers the effect of wear on RCF (Burstow)

01

1

Results-Wear

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Wagon: 150 000 km

Loco

MiW Rail Technology AB provided the wheel and rail profiles used in this study.



Profile updating


Results-Wear

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Comparison of 25th and 99.85th percentiles of the flange heights of more than 130 measured worn wheel profiles with the median values of the simulated worn profiles for various cases for all the locomotive axles.

MiW Rail Technology AB provided the wheel and rail profiles used in this study.



Profile updating


RCF results (𝑁𝑁𝑟𝑟)

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Simulated RCF results for various operational cases after 50 000km; maximum value for the colour-bar is set to 300 000 RCF number.

RCF results (𝑁𝑁𝑟𝑟)

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The most repeated crack locations observed on the wheel profiles during 5 years since 2011 with a background photo of a wheel taken in Dec. 2011 after 35 000 km running distance cracks with steeper angles (red) and the cracks with shallow angles (white). The histograms of the simulated crack angels are shown with the corresponding colors.

*A. Ekberg. 20161214-LKAB-Lokhjul. A report on. Iron Ore line – Damage on loco wheels. Chalmers University of Technology. (2016).

*

Wheel life prediction model

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𝑁𝑁𝑓𝑓 𝑡𝑡 =1

∑𝑖𝑖=1𝑘𝑘=9𝑃𝑃𝑖𝑖𝑁𝑁𝑓𝑓 𝑖𝑖

*Fatigue life: 𝑁𝑁𝑓𝑓

𝑁𝑁𝑓𝑓 𝑖𝑖 is calculated for each of the nine simulation cases

𝑃𝑃𝑖𝑖 is the contribution of each curve length to the total line length

*Kabo E., Ekberg A., Torstensson PT. and Vernersson T. Rolling contact fatigue prediction for rails and comparisons with test rig results. Proceedings of the Institution of Mechanical Engineers Part F-Journal of Rail and Rapid Transit. 2010; 224: 303-317.

𝑁𝑁𝑓𝑓 =10

𝐹𝐹𝐼𝐼𝑠𝑠𝑠𝑠𝑟𝑟𝑓𝑓4

Wheel life prediction model: results

9/11/2018 18* A. Ekberg. 20161214-LKAB-Lokhjul. A report on. Iron Ore line – Damage on loco wheels. Chalmers University of Technology. (2016).

Measurements*Cumulative Distribution Function

Future work

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• Non-elliptical contact

• Slip control and wheel damage

2 4 6 8 10

1000

2000

3000

4000

5000

dv [%]En

ergy

[J/m

]

µ = 0.3µ = 0.25µ = 0.2

• Active control of wheel damage

KTH ROYAL INSTITUTEOF TECHNOLOGY

Thank you very much for your attention!

Wheel Damage Prediction for Iron Ore Vehicles�using Vehicle Dynamics SimulationBackgroundBackgroundHertzian line contactHertzian line contactSlide Number 6Effect of friction on RCFEffect of Track gauge widening on RCFWheel damage calculation methodologySimulation set. design: Load-collectiveSurface shear stressWear & RCF calculationResults-WearResults-WearRCF results ( 𝑁 𝑟 )RCF results ( 𝑁 𝑟 )Wheel life prediction modelWheel life prediction model: resultsFuture workThank you very much for your attention!

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