Development of a regenerative friction model to enhance braking simulation with the multibody software VOCO

Moncef ToumiResearch Engineer, Railenium

Michel Sebès1, Karl Laden2, Hugues Chollet1, Mohammed Bouallaga3

IFSTTAR1, Bombardier Transportation2, Railenium3

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Contents

o Introduction

o Proposed model

o Simulation results

o Conclusion

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Contents

o Introduction

o Proposed model

o Simulation results

o Conclusion

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Introduction

A WSP device shall : Minimize the stopping distance Avoid wheel blocking Minimize extensive wheel slide Minimize wheel and rail damage

Braking tests are necessary for : Wheel Slide Protection (WSP)

device certification Vehicle homologation

On-bench tests could be used to : Reduce the number of real braking tests Reduce the cost for vehicle homologation Optimization of parameter sets before real testing

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UIC and EN Standards

DynamicBraking test

WSP certification and Vehicle Homologation

DynamicBraking test

Delivery

On-benchtest +

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IntroductionStandards to respect for WSP tests : EN15595 and UIC541-05 3rd ed

Co-simulation in a Hardware In the Loop (HIL) environment Allows to test full WSP device Hardware components

(WSP CU, speed sensors, dump valve,…) Wheelset speeds and braking efforts are exchanged

in real time between the WSP device and the simulator

The Simulator need to reproduce : Vehicle dynamics behavior Wheel-rail contact Adhesion coefficient 𝜇 ….

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Source : UIC 541-05 3rd ed.

Depends on :• Distance• Time• Slide• Vehicle speed• Temperature

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ED b

rake

forc

e+

IntroductionAdhesion coefficient in braking simulation

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𝜇 = 𝑓(𝑥, 𝑡, 𝑠, 𝑉, 𝑇)

Time t : Adhesion recovery

Slip s : Falling friction

Rail

First wheelset

Third body layer

Rolling direction

- Wheel self cleaning- Rail cleaning Adhesion recovery

Need to be estimated from measurements

Position 𝒙 : initial rail conditions

V : Train Velocity

[O. Polach, 2005]

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IntroductionThe multibody software VOCO

VOCO is the simulator chosen in this project to simulate train braking

Historically developed by IFSTTAR railway dynamic team and recently acquired by ESI Group

Classically used to study longitudinal and lateral train dynamics (critical speed, derailment,…)

Very good performance for real time simulation (important for simulation using a HIL procedure)

Allow to use variable adhesion coefficient in distance but not in time Adhesion recovery due to axle sliding is generally neglected.

VOCO performances were evaluated during a benchmarking campaign in the French project CERVIFER and during an international benchmarking campaign [1]

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[1] : Qing Wu et al., International benchmarking of longitudinal train dynamics simulators : results

Contents

o Introduction

o Proposed model

o Simulation results

o Conclusion

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Axle iAxlei+1

Braking performances(Braking distance, velocities…)

Distance

𝜇𝑚𝑎𝑥

𝜇0

Liquid film

Rail

Proposed modelGeneral resolution in VOCO and WSP algorithm

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Initial setting (Vehicle model, trackgeometry, initial speed, initial adhesion,…)

Multibodysoftware VOCO Software : Simplified WSP

algorithm

Calculate contact forces

Calculate axles and train velocities

Loo

p in

tim

e

Parameters of co-simulation and WSP system

Torques

Dynamic resolution

WSP system

velocities

Calculate recovered adhesionbetween wheel and rail

𝜇 = 𝜇max + 𝜇0 − 𝜇max 𝑒−𝑃𝑃0

𝜇0 : initial adhesion𝜇max : maximum adhesion limit𝑃0 : parameter of the model (regeneration rate)

Update adhesion 𝜇

Dissipated power at contacts

𝑃 = (𝐹𝑥𝜈𝑥 + 𝐹𝑦𝜈𝑦). 𝑉

𝐹𝑥 : longitudinal force𝐹𝑦 : transverse force

𝜈𝑥, 𝜈𝑦 : creepages

V : train speed

Adhesion recovery model

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Proposed modelInitial adhesion coefficient 𝜇0

Assumptions :• Sliding wheels• Same adhesion coefficient 𝜇0 at right and left wheel• Same static load 𝑄0 at right and left

FPD applied to first sliding axle :

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𝐽a 𝜔a = 𝐶𝐸𝐷 + 𝐶𝑏𝑠 − 𝑅0 𝐹𝑥r + 𝐹𝑥l

𝐶𝐸𝐷 : Torque due to electrodynamic brake𝐶𝑏𝑠 : Torque due to brake shoe

𝜔a =𝑑𝜔a

𝑑𝑡: Deceleration of first sliding axle

𝐽a = Inertial mass of first sliding axle𝑄0 = Static load per wheel for first sliding axle

𝑅0

𝐶𝑏𝑠 = 𝐹𝑡𝑅0

Forces and torques acting on driven axle

𝐹𝑡 = 𝜇𝑔𝑠𝐹𝑏𝑐

BC pressure

𝐶𝐸𝐷

Rolling direction

𝐹𝑥𝑟 = 𝐹𝑥𝑙 = 𝜇0𝑄0𝛍𝟎 =𝐶𝐸𝐷 + 𝐶𝑏𝑠 − 𝐽a 𝜔a

2 × 𝑅0𝑄0

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Contents

o Introduction

o Proposed model

o Simulation results

o Conclusion

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Simulation resultsInitial setting for braking simulation with VOCO

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Parameter File name/value Comments

Vehicle model DA_EFAR_10c_VOM REGIO 2N ONO Tare with 10 cars

Initial speed 160 km/h

Initialisation time 𝑇𝑖𝑛𝑖 Braking start at 𝑇𝑖𝑛𝑖

Initial adhesion 𝜇0 MUvar Estimated from measurements

Maximum adhesion 𝜇𝑚𝑎𝑥 𝜇𝑚𝑎𝑥 Estimated from measurements

Constant of the model 𝑃0 (W)

Track geometry THALIGN Alignment

Time step 3.10-4 s

- 10 cars - 11 bogies (4 driven bogies) - 22 axles - 44 wheels

Rolling direction

V11V20

- 4 cast iron shoes - 12 organic shoes

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Bogie 1Bogie 11

Simulation resultsDry conditions at V=160 km/h – Axle speeds and braking distance

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- Very good agreement between simulation and measurement for braking distance and axle speeds- Useful to set a reference braking distance for simulations and tests with low adhesion (UIC541-05)

Braking distance=915 m Braking distance=929 m

< 2 % compared to measurement

𝜇=0.3

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Simulation resultsWet conditions at V=160 km/h (6 % concentration soap - EN15595)– Axle speeds and braking distance

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- Good agreement between simulation and measurement for braking distance (<2%)- Same number of sliding axles (4 axles) but with different speed shapes due to different WSP algorithm- Need of a better WSP algorithm

Braking distance= 985 m Braking distance= 975 m

Simplified WSP algorithm

Real WSP device

P0=20 kW𝝁𝟎=variable𝝁𝒎𝒂𝒙=0.15

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Simulation resultsWet conditions at V=160 km/h (6 % concentration soap - EN15595)– Axle speeds and braking distance with different WSP limits

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s1=5%s2=10%s3=20%s4=25%

s1=3%s2=7%s3=12%s4=15%

- Reducing sliding limits of the simplified WSP algorithm improves speed profiles leading to better agreement between simulation and measurement

- Lack of agreement when train speed becomes low

P0=20 kW𝝁𝟎=variable𝝁𝒎𝒂𝒙=0.15

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Simulation resultsAdhesion recovery in dry and wet conditions at V=160 km/h– Braking distance

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- In dry conditions where adhesion coefficient is sufficiently high (when 𝜇>T/N according to Coulomb law), sliding between wheels and rail don’t occur and the amount of adhesion regeneration is very small

- Adhesion recovery don’t matter ! because initial adhesion is already high enough to prevent sliding

Stopping distance extension = 894 m

Same initial adhesion profile

- In wet conditions, adhesion is low leading to wheels sliding and high dissipated power at contact and high amount of adhesion regeneration

- Not using the adhesion recovery model leads to high braking surdistance- Adhesion recovery model is essential to simulate braking in presence of

low adhesion coefficient

Same braking distance with and withoutusing the adhesion recovery model

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P0=20 kW𝜇max=0.15

Simulation resultsWet conditions at V=160 km/h – Adhesion recovery (rail cleaning)

Start of braking

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- Adhesion recovery model is activeonly when braking start

- Final adhesion profile might beused as initial adhesion for othersimulation

Simulation resultsWet conditions at V=160 km/h – Adhesion recovery (rail cleaning)

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P0=20 kW𝝁𝒎𝒂𝒙=0.15

- With current parameter set, the contribution of 8axles in the « rail cleaning process » is sufficientto reach saturation

𝝁𝒎𝒂𝒙 = 𝟎, 𝟏𝟓

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Simulation resultsWet conditions at V=160 km/h – Adhesion recovery : Effect of P0 parameter with constant 𝝁𝐦𝐚𝐱=0.15

P0=20 kW

Medium amount of regeneration(only 5 axles are participating inthe cleaning process)

Small amount of regeneration(No adhesion recovery at all)

High amount of regeneration(First axle completely recoveradhesion to 𝜇max=0.15)

P0= 2000 kW P0=2 kW

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Contents

o Introduction

o Proposed model

o Simulation results

o Conclusion

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Conclusion

- New method is proposed to model low adhesion recovery (rail cleaning) during braking (or traction) using few parameters

- The adhesion recovery model improves considerably braking simulation in low adhesion

- The agreement between calculation and measurement can be improved by using a better WSP algorithm or real WSP device

- The self cleaning procured by the block shoes shall be considered

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Thank you for your attentionContact information :

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RaileniumMoncef Toumi(Moncef.toumi@railenium.eu)Mohammed Bouallaga(Mohammed.Bouallaga@railenium.eu)

IFSTTARMichel Sebès(Michel.sebes@ifsttar.fr)Hugues Chollet(Hugues.chollet@ifsttar.fr)

Bombardier TransportationKarl Laden(Karl.laden@rail.bombardier.com)

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IntroductionContact models and adhesion coefficient in multibody softwares

Most multibody softwares allow to use variable adhesion coefficient in distance but not in time Adhesion recovery due to axle sliding is generally neglected.

During braking (and traction), axle sliding can be high in low adhesion leading to wheels and rail cleaning.

In the multibody software VOCO, tangential contact forces are calculated using the Vermeulen & Johnson law in order to have better calculation time :

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t linF F

2

1 11 3

3 27

3

lin lin lin

n n n

linn

lin n

F F Fsi

F F F

FFsi

F F

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The expressions of 𝐹𝑙𝑖𝑛 are fitted with FASTSIM and take into account the spin creepage

Proposed modelSimplified WSP algorithm* and torque calculation

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Start : GOTO State 1

1 Fill IF (slip > s2=10%) THEN GOTO State 2

2 Hold IF (slip < s1=5%) THEN GOTO State 1IF (slip > s4=25%) THEN GOTO State 3

3 Vent IF (slip < s3=20%) THEN GOTO State 2𝑠𝑙𝑖𝑝 =

𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝑣𝑒𝑙𝑜𝑐𝑖𝑡𝑦 − 𝑎𝑥𝑙𝑒 𝑣𝑒𝑙𝑜𝑐𝑖𝑡𝑦

𝑉𝑒ℎ𝑖𝑐𝑙𝑒 𝑣𝑒𝑙𝑜𝑐𝑖𝑡𝑦

Torque increase (Fill) : 𝐶 = 𝐶𝑙𝑖𝑚(1 − 𝑒−

𝑡

𝑇𝑜𝑖)

Torque decrease (Vent) : 𝐶 = 𝑒−

𝑡

𝑇𝑜𝑑

𝑅𝑠

𝑅0

𝐹𝑡 = 𝜇𝑔𝑑𝐹𝑏𝑐

𝐶𝑑𝑏 = 𝐹𝑡𝑅𝑠

𝑅0

𝐶𝑏𝑠 = 𝐹𝑡𝑅0

Driven axle Trailer axle

𝑪lim = 𝒎𝒂𝒙(𝑪𝑬𝑫 + 𝑪𝒃𝒔) 𝑪lim = 𝒎𝒂𝒙(𝑪𝒅𝒃)

𝐹𝑡 = 𝜇𝑔𝑠𝐹𝑏𝑐

𝐹𝑏𝑐 = f(BC pressure)

BC pressure

𝐶𝐸𝐷 = 𝐹𝐸𝐷𝑅0

Rolling direction

BC pressure and electrodynamicforce 𝐹𝐸𝐷 are given frommeasurements

Disc brakeBrake shoe

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* Used for Shift2rail robin tests

Future workUsing real WSP device with a HIL procedure

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Initial setting (Vehicle model, trackgeometry, initial speed, initial adhesion,…)

Multibodysoftware VOCO

Braking performances (brakingdistance, axles velocities,…)

Simplified WSP algorithm=> Real WSP device

(Hardware)

Calculate contact forces

Calculate axles and train velocities

Loo

p in

tim

e

Parameters of co-simulation and WSP system

Torques

Dynamic resolution

WSP system

velocities

Calculate new adhesionbetween wheel and rail

𝜇 = 𝜇max + 𝜇0 − 𝜇max 𝑒−𝑃𝑃0

𝜇0 : initial adhesion𝜇max : maximum adhesion limit𝑃0 : parameter of the model

Dissipated power at contacts

𝑃 =

𝑖=1

𝑁𝐵

(𝐹𝑥𝑖𝜈𝑥𝑖)² + (𝐹𝑦𝑖𝜈𝑦𝑖)² ∙ V

𝐹𝑥 : longitudinal force𝐹𝑦 : lateral force

𝜈𝑥, 𝜈𝑦 : creepages

V : train speed

Adhesion recovery model

Source : UIC 541-05, 3rd ed. WSP test rig – class 1

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Update adhesion 𝜇

Simulation resultsDry conditions at V=200 km/h – Axle speeds and braking distance

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- Very good agreement between simulation and measurement for braking distance and axle speeds

Braking distance=1409 m Braking distance=1430 m

< 2 % compared to measurement

𝜇=0.3

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Simulation resultsWet conditions at V=160 km/h (6 % concentration soap - EN15595) – Deceleration profile

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P0=20 kW𝝁𝒎𝒂𝒙=0.15

- Good agreement between simulation and measurement for the deceleration profile

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