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BOGIE 07 Con ference

September 3rd 6th , 2007

Budapest HUNGARY

Numerical simulation for improving the design ofrunning gear Part 1: improvement of vehicledynamic behaviour

Paolo BELFORTE,S. BRUNI (Politecnico di Milano - Department of Mechanical Engineering)

Michael JCKEL (Fraunhofer Institute for Structural Durability and System Reliability - LBF)


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Paolo Belforte (Politecnico di Milano - Italy)

MODTRAIN Project

2

Innovative modular vehicle concepts for an integratedEuropean railway system

6th FRAMEWORK PROGRAMME PRIORITY 6.3 Transport

4 Years Project Started January 2004

MODTRAIN project

Modular approach to train design

Interoperability: new generation rolling stock

Harmonised European criteria for rolling stockhomologation


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MODTRAIN Project

3

It consist of five different sub-projects:

MODBOGIE

MODCONTROL

MODPOWER

MODLINK

MODUSER


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INTRODUCTION: NUMERICAL SIMULATIONSTOWARDS VIRTUAL HOMOLOGATION

5

In last years, the improved calculation technologies allowed the

development ofmore detai led and accu rate num erical models ofrai l vehicle dynam ics, which can be used as a very useful tool forthe design and development of a railway stock.

With the development of new generations of HS trains,numerica l simu lat ions can g ive an im por tant contr ibut ionin order

to raise service speed and satisfy operators requirements whichclaims always for improved performance in terms of comfort andsafety

This work targets the capabilities of multi body simulationmodels in the design and verification phase of the railway running

gear.


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Paolo Belforte (Politecnico di Milano - Italy) 6

INDEX


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Vehicle model:HS concentrated power locomotive

Carbody with two motor bogies

Two motors bogie-suspended by means ofdedicated motor hangers per each bogie

VEHICLE SCHEMATISATION

41,,,,....;;;;;

T

w

T

w

T

enr

T

br

T

enf

T

bf

T

c

T

V qqxxxxxx

The equation of motion Lagrange equations txxQxQvxxQQxKxRxM VVCVnlVVmVVVVVVV ,,,,

REFERENCE SYSTEMS

W/R contact

forces

Vehicleinertia

Fixedreference

Moving referencewith constantspeed V

Moving referenceon body c.o.g .

XG

ZG

YG

Xo

ZoYo

ZGi

YGi

V

si

bi

ri

Loco of a concentrated power train

Only rigid modes also for the wheelsets problem confined to low frequency


7/32Paolo Belforte (Politecnico di Milano - Italy) 8

tread contact1

2

1

2

FN1

FL1

FT1

FL2

FT2

FN2

flange contact

rail and wheel profiles contact geometricalparametersgeometrical analysis

elastic deformation innormal direction

(penetration)

tangential & longitudinalcreepages

generalizedcontact forcestangential & longitudinalforces

(Shen-Hedrick-Elkinstheory)

normal forces(multi-hertzian model)

Wheel rail contact forces model


8/32Paolo Belforte (Politecnico di Milano - Italy)

COMPARISON A.D.Tre.S. SIMPACKEigenvalues and time histories comparison

9

Natural frequencies comparison

Z

Xz

x

z

x

V

0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

1.8

NaturalFrequency

[Hz]

Vertical Lateral Yaw Pitch Roll

ADtres

Simpack

Carbody natural frequencies

Straight track with concentrated

track defect: 5 mm lateral and 14 mrad roll;

20 m wavelength;

speed 72 km/h.

Leading Wheelset of Bogie 1:

Vertical Force at Right Wheel

60000

64000

68000

72000

76000

80000

84000

88000

92000

96000

100000

2 3 4 5 6 7

Time [s]

For

ce

[N]

Simpack

ADTreS

Leading Wheelset of Bogie 1:

Lateral Force at Right Wheel

-6000

-4000

-2000

0

2000

4000

6000

2 3 4 5 6 7

Time [s]

For

ce

[N]

Simpack

ADTreS



INDEX



Tuning procedure by sensitivity analysis

TYPE OF ANALYSIS :parametric analysis on primary

suspension parameters and bogie wheel-base:

straight track running behaviour->critical speed

curve negotiation ->steady state Q (vertical force values)

steady state Y (lateral force values)

steady state wear index



Tuning procedure by sensitivity analysis:effect of wheel-base

Vehicleconfigurations

Wheelbase[m]

Cz[kN/mm]

Cy[kN/mm]

AD 3 10 18V1 2.7 10 18V2 2.5 10 18

Reducing the wheelbase the critical

speed decreases

Reducing the wheelbase the vehicle has a

better steering behaviour



Tuning procedure by sensitivity analysis:effect of wheel-base

Vehicleconfigurations

Wheelbase[m]

Cz[kN/mm]

Cy[kN/mm]

AD 3 10 18V1 2.7 10 18V2 2.5 10 18

Reducing the wheelbase the track shiftforce is lightly increased

Wear index is lower in case of reducedwheelbase

Radius curve [m]



INDEX



Analysis of technological options: virtual dynamic

homologation simulation acc. to EN14363

Vehicle configurations taken into account for EN14363 full

analysisVehicle

configurationsBogie

Wheelbase[m]

Longitudinal axleboxstiffness[kN/mm]

Lateral axleboxstiffness [kN/mm]

Reference 3 10 18

V1 3 30 15

V2 2.5 30 15

Three curve ranges are considered:

Small radius curve (250 400 m);

Medium-small radius curve (400 600 m); Large radius curve (600 2500 m) .



Virtual dynamic homologation procedure: main

curving indexes

TRACK SHIFT FORCEEN14363 limit

EN14363 limit

Y/Q

VERTICAL FORCE

EN14363 limit

Main parameters are obtained for all vehicle configurations

Vi t l d i h l ti d



Virtual dynamic homologation procedure:

critical speed and wear index.

WEAR INDEX CRITICAL SPEED

Additional information is the wear index which can be used for the

evaluation of the aggressiveness of the vehicle.

S iti it l i d tt di ti



Sensitivity analysis and scatter prediction

Numerical simulation can be used even for the evaluation of the

impact of the scatter variation of vehicles parameters on runningbehaviour.

S iti it l i d tt di ti



Sensitivity analysis and scatter prediction:effect of damper parameters

Exemplary Simulation Results (12 Parameters Varied Simultaneously): example ofthe correlation of the damper parameters with vertical wheel/rail contact forces.

0 2 4 6 8 10 12

x 104

9.6

9.65

9.7

9.75

9.8x 10

4

D11

0.5 1 1.5 2 2.5 3

x 104

9.6

9.65

9.7

9.75

9.8x 10

4

Max.normalforceF

max

[N]

Damper coefficient D1 [Ns/m] Damper coefficient D2 [Ns/m]

Strong correlation No correlation

Scatter

of

output

Secondarysuspension:

verticaldamper

(left)

Primarysuspension:

verticaldamper (left

front)

Each point: Output for one sample-set (simulation)

INDEX



INDEX

Methodolog for the assessment of technological options



Full factorial approach:

Dynamic performances analysis in straight track: vehicle stability Dynamic performances analysis in curved track: curving performance

Nine configuration are taken asreference, according to the full

factorial approach

NUMERICALSIMULATIONS

CURVINGPERFORMANCE

OPTIMIZATION

STRAIGHTTRACK

Methodology for the assessment of technological options:FULL FACTORIAL APPROACH

Methodology for the assessment of technological options:


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Methodology for the assessment of technological options:FULL FACTORIAL APPROACH

Definition of factor and factor levels: bogie wheelbase: 3 m - 2.75m - 2.5 m; lateral axlebox stiffness:10-25-40 kN/mm; longitudinal axlebox stiffness: 10-30-50

kN/mm.

ANOVA method : distinction random andsystematic variation polinomial equationof full factorial plan where coefficients a aredetermined applying the least square

analysis

2

128

2

217

2

26

2

15

21423121

xxxxxx

xxxxy

aaaa

aaaa

polynomial equation thatdescribes the full factorial plan

Reduced number ofconfigurations

Evaluate the influence of a simultaneous variation of parameters

RESULTS IN STRAIGHT TRACK: critical speed as a


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RESULTS IN STRAIGHT TRACK: critical speed as afunction of bogie wheelbase and axle boxes stiffness

Higher axlebox stiffness, leads toan increase of the critical speed

Higher bogie wheelbase stabilisesthe vehicle running dynamics

BW = 2.5 mBW = 2.75 m

BW = 2.5 m

BW = 3 m

265 km/h245 km/h

230 km/h

24%

RESULTS IN CURVEDTRACK: wear rate as a function of


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Reducing bogie wheelbase -> lower wear rate

Increasing axlebox stiffness -> higher wear rate

Leading outer wheel frictional work: small radius curve

20%

18 kJ

14 kJ

BW = 3 m

BW = 2.5 m

RESULTS IN CURVEDTRACK: wear rate as a function ofbogie wheelbase and axle boxes stiffness

OPTIMIZATION: results with different


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Wear index based optimisation

Reference vs. Opt.1: reduced wear 2%increased critical speed 5%

Solution Bogiewheelbase

[m]

Cz[kN/mm]

Cy[kN/mm]

Wear[kJ]

Criticalspeed [km/h]

Reference 3 10 18 12300 210

Opt. 1 2.75 10 21.5 12069 221

Two different optimisation functions were used.

Combined optimisation:

Reference vs. Opt. 2: increased critical speed of 16 %

increased wear of 4%


[m]

Cz

[kN/mm]

Cy

[kN/mm]

Wear

[kJ]


Reference 3 10 18 12300 210

Opt. 2 3 10 37.2 12578 256

OPTIMIZATION: results with differentoptimization functions

)max(),,_( _ WIspeedcryz CCkkbasewf ba

CONCLUSIONS


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CONCLUSIONS

Numerical simulation can be used in order to complement

physical testing for homologation;

Montecarlo approach coupled with multi-body simulations canaccount for the effect of scatter in component performances onride safety;

Numerical simulations can also be used for optimising vehicleperformances still meeting the constraints imposed by ride safety.


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Thanks for you r attent ion

Paolo [email protected]

Stefano [email protected]

BOGIE 07 ConferenceSeptember 3rd - 6th, 2007

Budapest HUNGARY

Michael [email protected]

33Methodology for the assessment of technological options:


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33Methodology for the assessment of technological options:SIMULATIONS PARAMETERS

STRAIGHT TRACKPer each configuration:

MB simulations increasing speed (steps 5 km/h)

Evaluation of rms values

Evaluation of prescribed limits & identification of critical speed

Simulation parameters:

W/R profile: theo. Rail / worn wheel cant 1:40 Track irreg: ERRI LOW

The overall assessment of one vehicle configuration requires at least 50 simulations

RMS calculation:Fourier trasform of the last 10 s of the simulation

Frequency f0 corrisponding to the maximum spectrumvalue identified Time history filtered with a band-pass filter f02 Hz

220 225 230 235 240 245 2500

1

2

3

4

5

6

7Leading bogie - Critical speed - RMS Lateral acc. criterion

limit lateral acceleration EN 14363: 4.83m/s 2 Vlim 240km/h

rms(y

)[m/s2]

V [km/h]

Lead. axle

Trail. axle



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34

CURVED TRACKSimulation parameters

Steady state condition for different radius curve (300 2500 m) randomcombination of

Track irregularity

W/R profile

Cant deficiency

Methodology for the assessment of technological options:SIMULATIONS PARAMETERS

Three tests zone:

small radius curves [250 -400 m];

small radius curves [400 600m];

radius curves [600 2500m];

For each zone -> 30 sections -> data collected with simulations

0 10 20 30 40 50 60 70 80 90 1000

0.5

1

1.5

2

2.5

3

[sample number]

[Sf i

j]



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35Methodology for the assessment of technological options:OPTIMISATION PROCEDURE

)max(),,_( wwCSyz CCCCwbbogief ba

Best vehicle w.r.t stability and wear optimisation function

Ccs & Cww critical speed and minimum frictional work

a & b weighting coefficient

All the indexes prescribed in the standard were consideredas constrains

Results -- CURVED TRACK:


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Results CURVED TRACK:Guiding force as function of bogie wheelbase and axle boxes stiffness

Low bogie wheelbase has positive effects on the vehicle curving behaviour

Longitudinal stiffness reduces the bogie steering capability

BW = 2.5mBW = 3m

Leading outer wheel guiding force: small radius curve

37Results -- OPTIMISATION


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37Results OPTIMISATION

Best vehicle parameters : optimisation procedure result

high lateral stiffness and high boogie wheelbase

Ref vs Opt.1: Increased critical speed of 16 %

Increased wear of 4%

Ref vs Opt.2: Increased critical speed of 16 %

decreased wear of 2%


[m]

Cz[kN/mm]

Cy[kN/mm]

Wear[kJ]


Reference 3 10 18 12300 210

Opt.1 3 10 37.2 12578 256

Opt.22.75 10 21.5 12069 221

INDEX


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INDEX

)max(),,_( _ WIspeedcryz CCkkbasewf ba

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