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Real-time multibody modeling and simulation of a scaled bogie test rig
journal contributionposted on 2021-03-16, 02:54 authored by Sundar ShresthaSundar Shrestha, Maksym SpiryaginMaksym Spiryagin, Qing WuQing Wu
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. © 2020, The Author(s).