Real-time multibody modeling and simulation of a scaled bogie test rig

In wheel–rail adhesion studies, most of the test rigs used are simplified designs such as a single wheel or wheelset, but the results may not be accurate. Alternatively, representing the complex system by using a full vehicle model provides accurate results but may incur complexity in design. To trade off accuracy over complexity, a bogie model can be the optimum selection. Furthermore, only a real-time model can replicate its physical counterpart in the time domain. Developing such a model requires broad expertise and appropriate software and hardware. A few published works are available which deal with real-time modeling. However, the influence of the control system has not been included in those works. To address these issues, a real-time scaled bogie test rig including the control system is essential. Therefore, a 1:4 scaled bogie roller rig is developed to study the adhesion between wheel and roller contact. To compare the performances obtained from the scaled bogie test rig and to expand the test applications, a numerical simulation model of that scaled bogie test rig is developed using Gensys multibody software. This model is the complete model of the test rig which delivers more precise results. To exactly represent the physical counterpart system in the time domain, a real-time scaled bogie test rig (RT-SBTR) is developed after four consecutive stages. Then, to simulate the RT-SBTR to solve the internal state equations and functions representing the physical counterpart system in equal or less than actual time, the real-time simulation environment is prepared in two stages. To such end, the computational time improved from 4 times slower than real time to 2 times faster than real time. Finally, the real-time scaled bogie model is also incorporated with the braking control system which slightly reduces the computational performances without affecting real-time capability.

[1]  Maksym Spiryagin,et al.  Wagon model acceptance procedure using Australian standards , 2012 .

[2]  Wanming Zhai Numerical Method and Computer Simulation for Analysis of Vehicle–Track Coupled Dynamics , 2020 .

[3]  Maksym Spiryagin,et al.  Review of adhesion estimation approaches for rail vehicles , 2019 .

[4]  Maksym Spiryagin,et al.  Mechatronic Modeling of Real-Time Wheel-Rail Contact , 2013 .

[5]  Monica Malvezzi,et al.  A scaled roller test rig for high-speed vehicles , 2010 .

[6]  Petr Voltr,et al.  Velocity measurement-based friction estimation for railway vehicles running on adhesion limit: swarm intelligence-based multiple models approach , 2020, J. Intell. Transp. Syst..

[7]  Maksym Spiryagin,et al.  Train braking simulation with wheel-rail adhesion model , 2020, Vehicle System Dynamics.

[8]  Chul-Goo Kang,et al.  Real-time simulations of a railroad brake system using a dSPACE board , 2009, 2009 ICCAS-SICE.

[9]  Wanming Zhai,et al.  Locomotive dynamic performance under traction/braking conditions considering effect of gear transmissions , 2018 .

[10]  J. Bélanger,et al.  The What , Where and Why of Real-Time Simulation , 2010 .

[11]  A. Jaschinski ON THE APPLICATION OF SIMILARITY LAWS TO A SCALED RAILWAY BOGIE MODEL , 1990 .

[12]  Maksym Spiryagin,et al.  Development of a real-time bogie test rig model based on railway specialised multibody software , 2013 .

[13]  Nicola Bosso,et al.  Scale Testing Theory and Approaches , 2019 .

[14]  Nicola Bosso,et al.  Design and Simulation of Railway Vehicles Braking Operation using a Scaled Roller-Rig , 2006 .

[15]  Benedetto Allotta,et al.  Modeling and Control of a Full-Scale Roller-Rig for the Analysis of Railway Braking Under Degraded Adhesion Conditions , 2015, IEEE Transactions on Control Systems Technology.

[16]  Petr Voltr,et al.  Particle swarm optimization based parametrization of adhesion and creep force models for simulation and modelling of railway vehicle systems with traction , 2020, Simul. Model. Pract. Theory.

[17]  N. Bosso,et al.  Comparison of different scaling techniques for the dynamics of a bogie on roller rig , 2001 .

[18]  Sung Hwan Park,et al.  Modeling and control of adhesion force in railway rolling stocks , 2008, IEEE Control Systems.

[19]  Maksym Spiryagin,et al.  Friction condition characterization for rail vehicle advanced braking system , 2019 .

[20]  Y. Sun,et al.  Parallel Co-Simulation Method for Railway Vehicle-Track Dynamics , 2018 .

[21]  Nicola Bosso,et al.  Real-time implementation of a traction control algorithm on a scaled roller rig , 2013 .

[22]  Lei Xu,et al.  A three-dimensional dynamic model for train-track interactions , 2019 .

[23]  Maksym Spiryagin,et al.  Design and Simulation of Rail Vehicles , 2014 .

[24]  Simon Iwnicki,et al.  Handbook of railway vehicle dynamics , 2006 .

[25]  Bo Liang,et al.  Friction coefficient estimation using an unscented Kalman filter , 2014 .

[26]  Colin Cole,et al.  Handbook of Railway Vehicle Dynamics, Second Edition , 2019 .

[27]  Nicola Bosso,et al.  Experimental and Numerical Simulation of Wheel-Rail Adhesion and Wear Using a Scaled Roller Rig and a Real-Time Contact Code , 2014 .

[28]  Monica Malvezzi,et al.  Design and preliminary validation of a tool for the simulation of train braking performance , 2013 .

[29]  Stefano Bruni,et al.  Analysis of Wheel-Roller Contact and Comparison with the Wheel-Rail Case , 2015 .

[30]  Chong-Seok Chang,et al.  An experimental study of high speed wheel-rail adhesion characteristics in wet condition on full scale roller rig , 2019 .