Alternative friction models for braking train dynamics

Oprea, Razvan Andrei; Cruceanu, Cătălin; Spiroiu, Marius Adrian

doi:10.1080/00423114.2012.744459

Cited by 42 publications

(27 citation statements)

References 20 publications

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“…Another more recent review [4] regarding longitudinal train dynamics has classified air brake models into empirical models, fluid dynamics models, and empirical-fluid dynamics models. Empirical models [5][6][7][8] use mathematical equations to fit measured characteristics of air brake systems. These empirical models do not necessarily follow fluid dynamics principles behind the measured characteristics.…”

Section: Introductionmentioning

confidence: 99%

“…It is difficult to achieve real-time simulations using fluid dynamics models for long heavy haul trains (e.g., 241 vehicles) due to the large number of equations to solve. The computational efficiency issue of fluid dynamics models is also an important reason why empirical models are still commonly used [6][7][8]. Empirical-fluid dynamics models [16,17] can evidently improve the computing speed but they also inherit some limitations of the empirical models.…”

Section: Introductionmentioning

confidence: 99%

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Railway Air Brake Model and Parallel Computing Scheme

Cole

Spiryagin

et al. 2017

Journal of Computational and Nonlinear Dynamics

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This paper developed a detailed fluid dynamics model and a parallel computing scheme for air brake systems on long freight trains. The model consists of subsystem models for pipes, locomotive brake valves, and wagon brake valves. A new efficient hose connection boundary condition that considers pressure loss across the connection was developed. Simulations with 150 sets of wagon brake systems were conducted and validated against experimental data; the simulated results and measured results reached an agreement with the maximum difference of 15%; all important air brake system features were well simulated. Computing time was compared for simulations with and without parallel computing. The computing time for the conventional sequential computing scheme was about 6.7 times slower than real-time. Parallel computing using four computing cores decreased the computing time by 70%. Real-time simulations were achieved by parallel computing using eight computer cores.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Railway Air Brake Model and Parallel Computing Scheme

Cole

Spiryagin

et al. 2017

Journal of Computational and Nonlinear Dynamics

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“…Generally, the existing literature on identifying TCMs can be classified into two categories, i.e., physical-driven models and simulation-based tools. The former class of research employs principles of mechanics to describe the longitudinal movement of vehicles on the tracks, which typically involve the vehicle dynamics acceleration/deceleration estimation (Bham and Benekohal, 2002;Fadhloun et al, 2015;Oprea et al, 2013), track friction coefficient identification (Wu et al, 2014) and in-train forces by drag forces and gravitational components. For example, an efficient and comprehensive alternative model was defined in Oprea et al (2013) based on set-valued friction with Coulomb's law in order to quantify the train braking dynamics.…”

Section: Literature Reviewmentioning

confidence: 99%

Data-driven approaches for modeling train control models: Comparison and case studies

Yin

Xun

et al. 2020

ISA Transactions

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“…A non-smooth draft gear modelling approach was proposed by Oprea et al [61][62][63] The draft gear model was expressed by Equation (14).…”

Section: Polytechnic University Of Bucharestmentioning

confidence: 99%

A review of dynamics modelling of friction draft gear

Cole

Luo

et al. 2014

Vehicle System Dynamics

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Longer and heavier trains mean larger in-train forces and more complicated force patterns. Practical experience indicates that the development of fatigue failure of coupling systems in long heavy trains may differ from conventional understanding. The friction-type draft gears are the most widely used draft gears. The ever developing heavy haul transport environment requires further or new understanding of friction draft gear behaviour and its implications for train dynamics as well as fatigue damage of rolling stock. However, modelling of friction draft gears is a highly nonlinear question. Especially the poor predictability, repeatability and the discontinuity of friction make this task more challenging. This article reviews current techniques in dynamics modelling of friction draft gears to provide a starting point that can be used to improve existing or develop new models to achieve more accurate force amplitude and pattern predictions.

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Alternative friction models for braking train dynamics

Cited by 42 publications

References 20 publications

Railway Air Brake Model and Parallel Computing Scheme

Railway Air Brake Model and Parallel Computing Scheme

Data-driven approaches for modeling train control models: Comparison and case studies

A review of dynamics modelling of friction draft gear

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