Train 3D: the technique for inclusion of three-dimensional models in longitudinal train dynamics and its application in derailment studies and train simulators

Pogorelov, Dmitry; Yazykov, Vladislav; Lysikov, Nikolay; Öztemel, Ercan; Arar, Ömer Faruk; Rende, Ferhat Şükrü

doi:10.1080/00423114.2016.1273532

Cited by 29 publications

(22 citation statements)

References 27 publications

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“…For computing questions that have naturally independent computing tasks, the partitioning process is straightforward. Some examples of such computing questions can be: (1) vector and matrix operations [15]; (2) traffic data processing [16]; (3) individual fitness evaluations in optimisations [12]; and (4) signal processing for multiple channels [17]. However, when the parallelised computing tasks are not independent, most commonly in FEA [7], Computational Fluid Dynamics (CFD) [18] and Discrete Element Analysis (DEA) [19], special techniques are needed to partition the computing question into parallelisable computing tasks.…”

Section: Domain Decomposition Methods (Ddm)mentioning

confidence: 99%

“…As multithreading deals with multiple threads that are in the same process, it understandably uses the shared memory parallel computing model as shown in Figure 3(b). Applications using Pthreads have been reported from [49,50]; and Reference [15] used WinAPI threads. Besides Pthreads and WinAPI threads, JAVA and Matlab also provide high level multithreading API.…”

Section: Multithreadingmentioning

confidence: 99%

“…Note that multiple contact points can exist for one wheel-rail interface. The forking process was also used by Kogut and Ciurej [11] and Pogorelov et al [15] to simulate vehicletrack-soil interaction dynamics and locomotive-train interaction dynamics respectively.…”

Section: Vsdmentioning

confidence: 99%

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Parallel computing in railway research

Spiryagin

Cole

et al. 2018

International Journal of Rail Transportation

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Available computing power for researchers has been increasing exponentially over the last decade. Parallel computing is possibly the best way to harness computing power provided by multiple computing units. This paper reviews parallel computing applications in railway research as well as the enabling techniques used for the purpose. Nine enabling techniques were reviewed and Message Passing Interface, Domain Decomposition and Hadoop & Apache are the top three most widely used enabling techniques. Seven major application topics were reviewed and iterative optimisations, continuous dynamics and data & signal analysis are the most widely reported applications. The reasons why these applications are suitable for parallel computing were discussed as well as the suitability of various enabling techniques for different applications. Computing time speed-ups that were reported from these applications were summarised. The challenges for applying parallel computing for railway research are discussed. ARTICLE HISTORY

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Section: Domain Decomposition Methods (Ddm)mentioning

confidence: 99%

Section: Multithreadingmentioning

confidence: 99%

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Parallel computing in railway research

Spiryagin

Cole

et al. 2018

International Journal of Rail Transportation

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“…Jerry Evans and Mats Berg [6] discussed appropriate modelling choices for suspension components, wheel/rail contact conditions and modeling input. Dmitry Pogorelov and Viasdislav [7] considered a technique referred to as the 'Train 3D method' for simulation of trains as coupled derailed spatial and simplified one-dimensional models of rail vehicles to evaluate safety factors with dependence on the train operation regime. R.Kovalev and V.N Yazykov [8] presented nonstiff method for computing the nonelliptical contact problem that can apply to the wheel rail contact problem.…”

Section: Introductionmentioning

confidence: 99%

Modeling and Simulation of Collision-Causing Derailment to Design the Derailment Containment Provision Using a Simplified Vehicle Model

et al. 2019

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As the operating speed of a train increases, there is a growing interest in reducing damage caused by derailment and collision accidents. Since a collision with the surrounding structure after a derailment accident causes a great damage, protective facilities like a barrier wall or derailment containment provision (DCP) are installed to reduce the damage due to the secondary collision accident. However, the criteria to design a protective facility such as locations and design loads are not clear because of difficulties in predicting post-derailment behaviors. In this paper, we derived a simplified frame model that can predict post derailment behaviors in the design phase of the protective facilities. The proposed vehicle model can simplify for various frames to reduce the computation time. Also, the actual derailment tests were conducted on a real test track to verify the reliability of the model. The simulation results of the proposed model showed reasonable agreement to the test results.

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“…Such models have attracted significant attention and have been reported recently. [29][30][31] However, none of these has addressed the issue of re-adhesion control in detail. Since the railway vehicle dynamic model has nonlinear nature, fuzzy logic control algorithms should also be considered in the design and analysis of the adhesion control subject.…”

Section: Introductionmentioning

confidence: 99%

Comparison of the re-adhesion control strategies in high-speed train

Uyulan

Gökaşan

Boğosyan

2017

Proceedings of the Institution of Mechanical Engineers, Part I:

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Excessive driving force applied to the trains leads to an inadequate utilization of the adhesion phenomenon occurred at the wheel-rail contact, and an unnecessary power consumption, while inadequate driving force causes the train to run inefficiently. For this reason, the necessity of re-adhesion control in the safe and reliable operation, in the balance of energy consumption, is indisputable. A comparison of the two re-adhesion control strategies, one of which is robust adaptive and the other of which is the modified super-twisting sliding mode, has been presented in this article. These control algorithms developed suppress the wheel slip on time and maintain optimal traction performance after readhesion under the nonlinear properties of the traction system and the uncertainties of the adhesion level at the wheelrail interface. Due to the complex nonlinear relationship between the adhesion force and the slip angular velocity, such a problem becomes a hard problem to overcome as long as the optimal slip ratio is not known. An optimal search strategy has also been developed to estimate and to track the desired slip angular velocity. By means of the proposed strategies, the traction motor control torque is automatically adjusted so as to ensure that the train operates away from the unstable slip zone but adjacent to the optimal adhesion region, and the desired traction capability is attainable once adhesion is regained. Mathematical analyzes are also provided to ensure the ultimate boundedness of the algorithms developed. The effectiveness of the proposed re-adhesion strategies is validated through the theoretical analysis and numerical simulations conducted in MATLAB and Simulink. As a result of consecutive simulations, modified supertwisting algorithm has shown better performance as compared to the robust adaptive one in tracking the optimal slip velocity as wheel-rail contact conditions switch suddenly.

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Train 3D: the technique for inclusion of three-dimensional models in longitudinal train dynamics and its application in derailment studies and train simulators

Cited by 29 publications

References 27 publications

Parallel computing in railway research

Parallel computing in railway research

Modeling and Simulation of Collision-Causing Derailment to Design the Derailment Containment Provision Using a Simplified Vehicle Model

Comparison of the re-adhesion control strategies in high-speed train

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