Presentation is loading. Please wait.

Presentation is loading. Please wait.

Numerical simulation for improving the design of running gear – Part 1: improvement of vehicle dynamic behaviour Paolo BELFORTE, S. BRUNI (Politecnico.

Similar presentations


Presentation on theme: "Numerical simulation for improving the design of running gear – Part 1: improvement of vehicle dynamic behaviour Paolo BELFORTE, S. BRUNI (Politecnico."— Presentation transcript:

1 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) LBF!!

2 MODTRAIN Project MODTRAIN project “ 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 Interoperability: new generation rolling stock Harmonised European criteria for rolling stock homologation

3 It consist of five different sub-projects:
MODTRAIN Project It consist of five different sub-projects: MODBOGIE MODCONTROL MODPOWER MODLINK MODUSER

4 SubProject leader is ANSALDOBREDA
MODBOGIE SubProject 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 INTRODUCTION: NUMERICAL SIMULATIONS TOWARDS “VIRTUAL HOMOLOGATION”
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 INDEX

7 Vehicle model: HS concentrated power locomotive
REFERENCE SYSTEMS VEHICLE SCHEMATISATION Loco of a concentrated power train Fixed reference Moving reference with constant speed V Moving reference on body c.o.g . XG ZG YG Xo Zo Yo ZGi YGi V si bi ri Carbody with two motor bogies Two motors bogie-suspended by means of dedicated motor hangers per each bogie Only rigid modes also for the wheelsets  problem confined to low frequency The equation of motion  Lagrange equations Vehicle inertia W/R contact forces

8 Wheel rail contact forces model
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)

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

10 INDEX

11 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’

12 Tuning procedure by sensitivity analysis: effect of wheel-base
Vehicle configurations Wheelbase [m] Cz [kN/mm] Cy AD 3 10 18 V1 2.7 V2 2.5 Reducing the wheelbase the critical speed decreases Reducing the wheelbase the vehicle has a better steering behaviour

13 Cz Cy Tuning procedure by sensitivity analysis: effect of wheel-base
Vehicle configurations Wheelbase [m] Cz [kN/mm] Cy AD 3 10 18 V1 2.7 V2 2.5 Radius curve [m] Reducing the wheelbase the track shift force is lightly increased Wear index is lower in case of reduced wheelbase

14 INDEX

15 Vehicle configurations taken into account for EN14363 full analysis
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] Reference 3 10 18 V1 30 15 V2 2.5 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 Analysis of technological options: ‘virtual dynamic homologation’ simulation acc. to EN14363
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 ‘Virtual dynamic homologation’ procedure: main curving indexes
Main parameters are obtained for all vehicle configurations TRACK SHIFT FORCE Y/Q EN14363 limit EN14363 limit EN14363 limit VERTICAL FORCE

18 ‘Virtual dynamic homologation’ procedure: critical speed and wear index.
Additional information is the wear index which can be used for the evaluation of the aggressiveness of the vehicle. WEAR INDEX CRITICAL SPEED

19 Parametrical analysis results Steady state analysis
CRITICAL SPEED GUIDING FORCE TRACK SHIFT FORCE WEAR INDEX

20 Sensitivity analysis and scatter prediction
Numerical simulation can be used even for the evaluation of the impact of the scatter variation of vehicle’s parameters on running behaviour.

21 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. Secondary suspension: vertical damper (“left”) Primary suspension: vertical damper (“left front”) Each point: Output for one sample-set (simulation) Scatter of output Max. normal force Fmax [N] D11  Strong correlation  No correlation Damper coefficient D1 [Ns/m] Damper coefficient D2 [Ns/m]

22 INDEX

23 Full factorial approach:
Methodology for the assessment of technological options: FULL FACTORIAL APPROACH 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 Trial and error -> procedura semplificata. Prima faccio tuning, problema di ottimo vincolato, ossi a soddisfare i limiti della 14363, andando ad tenere come parametro di ottimizzazione o l’usura o la somma pesata di stabilità ed usura

24 Evaluate the influence of a simultaneous variation of parameters
Methodology for the assessment of technological options: FULL FACTORIAL APPROACH Evaluate the influence of a simultaneous variation of parameters 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 a are determined applying the least square analysis Reduced number of configurations Diciamo parametri del veicolo come variabili della minimizzazione, con 3x3 casi, quindi 3 piani fattoriali diversi per i 3 passi. Wheel-base è variabile discreta. I vincoli sono il fatto di rispettare gli indici della EN14363 e che la velocità critica sia almeno 220 km/h (quando faccio solo WW). polynomial equation that describes the full factorial plan

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

26 Leading outer wheel frictional work: small radius curve
RESULTS IN CURVEDTRACK: wear rate as a function of bogie wheelbase and axle boxes stiffness Leading outer wheel frictional work: small radius curve BW = 3 m 18 kJ BW = 2.5 m 14 kJ 20% Reducing bogie wheelbase -> lower wear rate Increasing axlebox stiffness -> higher wear rate

27 OPTIMIZATION: results with different optimization functions
Two different optimisation functions were used. Wear index based optimisation Solution Bogie wheelbase [m] Cz [kN/mm] Cy Wear [kJ] Critical speed [km/h] Reference 3 10 18 12300 210 Opt. 1 2.75 21.5 12069 221 Reference vs. Opt.1: reduced wear 2% increased critical speed 5% Combined optimisation: Solution Bogie wheelbase [m] Cz [kN/mm] Cy Wear [kJ] Critical speed [km/h] Reference 3 10 18 12300 210 Opt. 2 37.2 12578 256 A seconda della funzione da ottimizzare si trovano soluzioni diverse. Questa è la media pesata ??? VERIFICARE!!!!!! Reference vs. Opt. 2: increased critical speed of 16 % increased wear of 4%

28 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 Thanks for your attention
BOGIE ’07 Conference September 3rd - 6th, Budapest – HUNGARY Paolo BELFORTE Stefano BRUNI Michael JÖCKEL

30 COMPARISON A.D.Tre.S. – SIMPACK Eigenvalues comparison
Natural frequencies computation Natural frequencies with linearised contact forces (Kalker’s linear theory) Fx = -f33*x Fy = -f11*h-f12*f Mz= f12*h-f22*f Carbody natural frequencies Carbody natural frequencies

31 COMPARISON A.D.Tre.S. – SIMPACK Time domain comparison
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 COMPARISON A.D.Tre.S. – SIMPACK Time domain comparison
Curved track without track defect: R=2000 m, a.n.c m/s2, speed 185 km/h; Outer wheel Inner wheel SPK ADTreS Vertical force WS1 [N] 106956 105372 59744 61360 Vertical force WS2 [N] 110446 108298 56315 58431 Lateral force WS1 [N] 18320 19620 -1941 -2175 Lateral force WS2 [N] 21266 20880 7250 6573 Longitudinal force WS1 [N] 7085 7165 -7111 -7165 Longitudinal force WS2 [N] 6163 3315 -6166 -3315

33 Methodology for the assessment of technological options: SIMULATIONS PARAMETERS
STRAIGHT TRACK Per 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 spectrum value identified Time history filtered with a band-pass filter f0±2 Hz

34 Methodology for the assessment of technological options: SIMULATIONS PARAMETERS
CURVED TRACK Simulation parameters Steady state condition for different radius curve (300 – 2500 m) – random combination of Track irregularity W/R profile Cant deficiency 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 Best vehicle w.r.t stability and wear  optimisation function
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 a & b  weighting coefficient All the indexes prescribed in the standard were considered as constrains

36 Leading outer wheel guiding force: small radius curve
Results -- CURVED TRACK: Guiding force as function of bogie wheelbase and axle boxes stiffness Leading outer wheel guiding force: small radius curve BW = 3m BW = 2.5m Low bogie wheelbase has positive effects on the vehicle curving behaviour Longitudinal stiffness reduces the bogie steering capability

37 Results -- OPTIMISATION
Best vehicle parameters : optimisation procedure result Solution Bogie wheelbase [m] Cz [kN/mm] Cy Wear [kJ] Critical speed [km/h] Reference 3 10 18 12300 210 Opt.1 37.2 12578 256 Opt.2 2.75 21.5 12069 221 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% high lateral stiffness and high boogie wheelbase

38 INDEX


Download ppt "Numerical simulation for improving the design of running gear – Part 1: improvement of vehicle dynamic behaviour Paolo BELFORTE, S. BRUNI (Politecnico."

Similar presentations


Ads by Google