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BOGIE 07 Conference September 3rd – 6th, 2007 Budapest – HUNGARY Numerical simulation for improving the design of running gear – Part 1: improvement of.

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Presentation on theme: "BOGIE 07 Conference September 3rd – 6th, 2007 Budapest – HUNGARY Numerical simulation for improving the design of running gear – Part 1: improvement of."— Presentation transcript:

1 BOGIE 07 Conference September 3rd – 6th, 2007 Budapest – HUNGARY Numerical simulation for improving the design of running gear – Part 1: improvement of vehicle dynamic behaviour Paolo BELFORTE, S. BRUNI (Politecnico di Milano - Department of Mechanical Engineering) Michael JÖCKEL (Fraunhofer Institute for Structural Durability and System Reliability - LBF)

2 Paolo Belforte (Politecnico di Milano - Italy) MODTRAIN Project 2 Innovative modular vehicle concepts for an integrated European railway system Innovative modular vehicle concepts for an integrated European railway system 6th FRAMEWORK PROGRAMME PRIORITY 6.3 – Transport 4 Years Project – Started January 2004 Modular approach to train design Modular approach to train design Interoperability: new generation rolling stock Interoperability: new generation rolling stock Harmonised European criteria for rolling stock homologation Harmonised European criteria for rolling stock homologation

3 Paolo Belforte (Politecnico di Milano - Italy) MODTRAIN Project 3 It consist of five different sub-projects: MODBOGIE MODBOGIE MODCONTROL MODCONTROL MODPOWER MODPOWER MODLINK MODLINK MODUSER MODUSER

4 Paolo Belforte (Politecnico di Milano - Italy) MODBOGIE SubProject 4 ModBogie Subproject has 11 partners: S.I.: ANSALDOBREDA / ALSTOM / BOMBARDIER / SIEMENS; Wheelset manufacturer: LUCCHINI SIDERMECCANICA; Operators: DB / TRENITALIA / SNCF; Research Institutes / Universities: POLIMI / LBF-IWM / D2S. SubProject leader is ANSALDOBREDA ModBogie SubProject is dedicated to the optimization of the bogie, leading to: improved performances in terms of energy efficiency; enhanced bogie design for fulfill more demanding operational requirements; wider dynamic performances with reduced environmental impact and maintenance costs.

5 Paolo Belforte (Politecnico di Milano - Italy) INTRODUCTION: NUMERICAL SIMULATIONS TOWARDS VIRTUAL HOMOLOGATION 5 In last years, the improved calculation technologies allowed the development of more detailed and accurate numerical models of rail vehicle dynamics, which can be used as a very useful tool for the design and development of a railway stock. With the development of new generations of HS trains, numerical simulations can give an important contribution in order to raise service speed and satisfy operators requirements which claims always for improved performance in terms of comfort and safety This work targets the capabilities of multi body simulation models in the design and verification phase of the railway running gear.

6 Paolo Belforte (Politecnico di Milano - Italy) 6 INDEX

7 Paolo Belforte (Politecnico di Milano - Italy) 7 Vehicle model: HS concentrated power locomotive Carbody with two motor bogies Two motors bogie-suspended by means of dedicated motor hangers per each bogie VEHICLE SCHEMATISATION The equation of motion Lagrange equations REFERENCE SYSTEMS W/R contact forces Vehicle inertia Fixed reference Moving reference with constant speed V Moving reference on body c.o.g. XGXG ZGZG YGYG XoXo ZoZo YoYo ZGiZGi Y Gi V i i i Loco of a concentrated power train Only rigid modes also for the wheelsets problem confined to low frequency

8 Paolo Belforte (Politecnico di Milano - Italy) 8 rail and wheel profiles contact geometrical parameters geometrical analysis elastic deformation in normal direction (penetration) tangential & longitudinal creepages generalized contact forces tangential & longitudinal forces (Shen-Hedrick-Elkins theory) normal forces (multi-hertzian model) Wheel rail contact forces model

9 Paolo Belforte (Politecnico di Milano - Italy) COMPARISON A.D.Tre.S. – SIMPACK Eigenvalues and time histories comparison 9 Natural frequencies comparison Carbody natural frequencies Straight track with concentrated track defect: 5 mm lateral and 14 mrad roll; 20 m wavelength; speed 72 km/h.

10 Paolo Belforte (Politecnico di Milano - Italy) 10 INDEX

11 Paolo Belforte (Politecnico di Milano - Italy) 11 Tuning procedure by sensitivity analysis TYPE OF ANALYSIS : parametric analysis on primary suspension parameters and bogie wheel-base: straight track running behaviour straight track running behaviour -> critical speed curve negotiation curve negotiation -> steady state Q (vertical force values) steady state Y (lateral force values) steady state wear index

12 Paolo Belforte (Politecnico di Milano - Italy) 12 Tuning procedure by sensitivity analysis: effect of wheel-base Vehicle configurations Wheelbase [m] Cz [kN/mm] Cy [kN/mm] AD31018 V V Reducing the wheelbase the critical speed decreases Reducing the wheelbase the vehicle has a better steering behaviour

13 Paolo Belforte (Politecnico di Milano - Italy) Tuning procedure by sensitivity analysis: effect of wheel-base Vehicle configurations Wheelbase [m] Cz [kN/mm] Cy [kN/mm] AD31018 V V Reducing the wheelbase the track shift force is lightly increased Wear index is lower in case of reduced wheelbase Radius curve [m]

14 Paolo Belforte (Politecnico di Milano - Italy) 14 INDEX

15 Paolo Belforte (Politecnico di Milano - Italy) 15 Analysis of technological options: virtual dynamic homologation simulation acc. to EN14363 Vehicle configurations taken into account for EN14363 full analysis Vehicle configurations Bogie Wheelbase [m] Longitudinal axlebox stiffness [kN/mm] Lateral axlebox stiffness [kN/mm] Reference31018 V V Three curve ranges are considered: Small radius curve (250 – 400 m); Medium-small radius curve (400 – 600 m); Large radius curve (600 – 2500 m).

16 Paolo Belforte (Politecnico di Milano - Italy) Analysis of technological options: virtual dynamic homologation simulation acc. to EN For each curve ranges a number of 30 sections, is considered. Per each section, a combination of the following parameters is chosen: Curve geometric parameters such as radius curve, cant and length of transition curve; Wheel – rail profiles; Track irregularity (different small level one track irregularities); Speed, chosen randomly, imposing a cant deficiency of 110% of the admissible for at least 20% of the complete simulation set.

17 Paolo Belforte (Politecnico di Milano - Italy) 17 Virtual dynamic homologation procedure: main curving indexes TRACK SHIFT FORCE EN14363 limit Y/Q VERTICAL FORCE EN14363 limit Main parameters are obtained for all vehicle configurations

18 Paolo Belforte (Politecnico di Milano - Italy) 18 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.

19 Paolo Belforte (Politecnico di Milano - Italy) 19 Parametrical analysis results Steady state analysis GUIDING FORCE TRACK SHIFT FORCE WEAR INDEX CRITICAL SPEED

20 Paolo Belforte (Politecnico di Milano - Italy) 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 running behaviour.

21 Paolo Belforte (Politecnico di Milano - Italy) Sensitivity analysis and scatter prediction: effect of damper parameters Exemplary Simulation Results (12 Parameters Varied Simultaneously): example of the correlation of the damper parameters with vertical wheel/rail contact forces. D11 Max. normal force F max [N] Damper coefficient D 1 [Ns/m]Damper coefficient D 2 [Ns/m] Strong correlation No correlation Scatter of output Secondary suspension: vertical damper (left) Primary suspension: vertical damper (left front) Each point: Output for one sample-set (simulation)

22 Paolo Belforte (Politecnico di Milano - Italy) 22 INDEX

23 Paolo Belforte (Politecnico di Milano - Italy) 23 Full factorial approach: Dynamic performances analysis in straight track: vehicle stability Dynamic performances analysis in curved track: curving performance Nine configuration are taken as reference, according to the full factorial approach NUMERICAL SIMULATIONS CURVING PERFORMANCE OPTIMIZATION STRAIGHT TRACK Methodology for the assessment of technological options: FULL FACTORIAL APPROACH

24 Paolo Belforte (Politecnico di Milano - Italy) Methodology for the assessment of technological options: FULL FACTORIAL APPROACH Definition of factor and factor levels: bogie wheelbase: 3 m m m; lateral axlebox stiffness: kN/mm; longitudinal axlebox stiffness: kN/mm. ANOVA method : distinction random and systematic variation polinomial equation of full factorial plan where coefficients are determined applying the least square analysis polynomial equation that describes the full factorial plan Reduced number of configurations Evaluate the influence of a simultaneous variation of parameters

25 Paolo Belforte (Politecnico di Milano - Italy) RESULTS IN STRAIGHT TRACK: critical speed as a function of bogie wheelbase and axle boxes stiffness Higher axlebox stiffness, leads to an increase of the critical speed Higher bogie wheelbase stabilises the vehicle running dynamics BW = 2.5 m BW = 2.75 m BW = 2.5 m BW = 3 m 265 km/h 245 km/h 230 km/h 24%

26 Paolo Belforte (Politecnico di Milano - Italy) 26 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 of bogie wheelbase and axle boxes stiffness

27 Paolo Belforte (Politecnico di Milano - Italy) Wear index based optimisation Reference vs. Opt.1: Reference vs. Opt.1: reduced wear 2% increased critical speed 5%Solution Bogie wheelbase [m] Cz[kN/mm]Cy[kN/mm]Wear[kJ] Critical speed [km/h] Reference Opt Two different optimisation functions were used. Combined optimisation: Reference vs. Opt. 2: Reference vs. Opt. 2: increased critical speed of 16 % increased wear of 4%Solution Bogie wheelbase [m] Cz[kN/mm]Cy[kN/mm]Wear[kJ] Critical speed [km/h] Reference Opt OPTIMIZATION: results with different optimization functions

28 Paolo Belforte (Politecnico di Milano - Italy) CONCLUSIONS Numerical simulation can be used in order to complement physical testing for homologation; Montecarlo approach coupled with multi-body simulations can account for the effect of scatter in component performances on ride safety; Numerical simulations can also be used for optimising vehicle performances still meeting the constraints imposed by ride safety.

29 Paolo Belforte (Politecnico di Milano - Italy) 29 Thanks for your attention Paolo BELFORTE Stefano BRUNI BOGIE 07 Conference September 3rd - 6th, 2007 Budapest – HUNGARY Michael Michael JÖCKEL

30 Paolo Belforte (Politecnico di Milano - Italy) COMPARISON A.D.Tre.S. – SIMPACK Eigenvalues comparison 30 Natural frequencies computation Carbody natural frequencies Natural frequencies with linearised contact forces (Kalkers linear theory) Fx = -f33* Fy = -f11* -f12* Mz= f12* -f22* Carbody natural frequencies

31 Paolo Belforte (Politecnico di Milano - Italy) COMPARISON A.D.Tre.S. – SIMPACK Time domain comparison 31 Straight track with concentrated track defect: 5 mm lateral and 14 mrad roll; 20 m wavelength; speed 72 km/h. The discrepancies between lateral forces computed in Simpack and ADTreS are due to the quasi elastic interpolation adopted SIMPACK and not used in the simulation algorithm by Polimi

32 Paolo Belforte (Politecnico di Milano - Italy) COMPARISON A.D.Tre.S. – SIMPACK Time domain comparison 32 Curved track without track defect: R=2000 m, a.n.c m/s 2, speed 185 km/h; Outer wheelInner wheel SPKADTreSSPKADTreS Vertical force WS1 [N] Vertical force WS2 [N] Lateral force WS1 [N] Lateral force WS2 [N] Longitudinal force WS1 [N] Longitudinal force WS2 [N]

33 Paolo Belforte (Politecnico di Milano - Italy) 33 Methodology for the assessment of technological options: SIMULATIONS PARAMETERS STRAIGHT TRACK Per each configurationPer each configuration: MB simulations increasing speed (steps 5 km/h) Evaluation of rms values Evaluation of prescribed limits & identification of critical speed Simulation parameters: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 spectrum value identified Time history filtered with a band-pass filter f0±2 Hz

34 Paolo Belforte (Politecnico di Milano - Italy) 34 CURVED TRACK Simulation parameters Steady state condition for different radius curve (300 – 2500 m) – random combination of Track irregularity W/R profile Cant deficiency Methodology for the assessment of technological options: SIMULATIONS PARAMETERS Three tests zone: small radius curves [ m]; small radius curves [400 – 600m]; radius curves [600 – 2500m]; For each zone -> 30 sections -> data collected with simulations

35 Paolo Belforte (Politecnico di Milano - Italy) 35 Methodology for the assessment of technological options: OPTIMISATION PROCEDURE Best vehicle w.r.t stability and wear optimisation function Ccs & Cww critical speed and minimum frictional work & weighting coefficient All the indexes prescribed in the standard were considered as constrains

36 Paolo Belforte (Politecnico di Milano - Italy) 36 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 = 2.5m BW = 3m Leading outer wheel guiding force: small radius curve

37 Paolo Belforte (Politecnico di Milano - Italy) 37 Results -- OPTIMISATION Best vehicle parameters : optimisation procedure result high lateral stiffness and high boogie wheelbase Ref vs Opt.1: Ref vs Opt.1: Increased critical speed of 16 % Increased wear of 4% Ref vs Opt.2: Ref vs Opt.2: Increased critical speed of 16 % decreased wear of 2%Solution Bogie wheelbase [m] Cz[kN/mm]Cy[kN/mm]Wear[kJ] Critical speed [km/h] Reference Opt Opt

38 Paolo Belforte (Politecnico di Milano - Italy) 38 INDEX


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