Identification of a wheel–rail adhesion coefficient from experimental data during braking tests

Malvezzi, Monica; Pugi, Luca; Papini, Susanna; Rindi, Andrea; Toni, P.

doi:10.1177/0954409712458490

Cited by 27 publications

(11 citation statements)

References 11 publications

(24 reference statements)

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“…However, too small maximum adhesion torque and coupling damping could deteriorate the DCW's self-steering performance in large radius curves and tangent lines. Generally, the maximum adhesion torque determines the work conditions of the DCW, and it depends on the wheel/rail adhesion conditions affected by many factors [19][20][21][22], such as normal load, sliding speed, temperature of the two bodies, contact geometry, weather conditions, and the presence of rain, snow, and dead leaves. On the other hand, with the reduction of the maximum adhesion torque, the friction power decreases (Fig.…”

Section: Discussionmentioning

confidence: 99%

Analysis of steering performance of differential coupling wheelset

Chi

Zeng

et al. 2014

J. Mod. Transport.

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In order to improve the curving performance of the conventional wheelset in sharp curves and resolve the steering ability problem of the independently rotating wheel in large radius curves and tangent lines, a differential coupling wheelset (DCW) was developed in this work. The DCW was composed of two independently rotating wheels (IRWs) coupled by a clutch-type limited slip differential. The differential contains a static pre-stress clutch, which could lock both sides of IRWs of the DCW to ensure a good steering performance in curves with large radius and tangent track. In contrast, the clutch could unlock the two IRWs of the DCW in a sharp curve to endue it with the characteristic of an IRW, so that the vehicles can go through the tight curve smoothly. To study the dynamic performance of the DCW, a multi-body dynamic model of single bogie with DCWs was established. The self-centering capability, hunting stability, and self-steering performance on a curved track were analyzed and then compared with those of the conventional wheelset and IRW. Finally, the effect of coupling parameters of the DCW on the dynamic performance was investigated.

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Section: Discussionmentioning

confidence: 99%

Analysis of steering performance of differential coupling wheelset

Chi

Zeng

et al. 2014

J. Mod. Transport.

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“…These cases are used merely for demonstration purposes. Adhesion characteristics and studies for braking scenarios [27,28] are also important. Wheel-rail contact and adhesion models used in this paper are also able to conduct braking studies.…”

Section: • In Simulation 2 and Simulation 3 Adhesion Modelmentioning

confidence: 99%

Train energy simulation with locomotive adhesion model

Spiryagin

Cole

2020

Rail. Eng. Science

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Railway train energy simulation is an important and popular research topic. Locomotive traction force simulations are a fundamental part of such research. Conventional energy calculation models are not able to consider locomotive wheel-rail adhesions, traction adhesion control, and locomotive dynamics. This paper has developed two models to fill this research gap. The first model uses a 2D locomotive model with 27 degrees of freedom and a simplified wheel-rail contact model. The second model uses a 3D locomotive model with 54 degrees of freedom and a fully detailed wheel-rail contact model. Both models were integrated into a longitudinal train dynamics model with the consideration of locomotive adhesion control. Energy consumption simulations using a conventional model (1D model) and the two new models (2D and 3D models) were conducted and compared. The results show that, due to the consideration of wheel-rail adhesion model and traction control in the 3D model, it reports less energy consumption than the 1D model. The maximum difference in energy consumption rate between the 3D model and the 1D model was 12.5%. Due to the consideration of multiple wheel-rail contact points in the 3D model, it reports higher energy consumption than the 2D model. An 8.6% maximum difference in energy consumption rate between the 3D model and the 1D model was reported during curve negotiation.

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“…In engineering, the term is widely used to describe the tangential force induced in the wheel-rail contact. The adhesion is the relation between the longitudinal tangential force and normal force in wheel-rail interaction [3]. However, friction is the force that appear as resistance when one body is sliding/moving on another [4].…”

Section: Structuring a Training Data Setmentioning

confidence: 99%

Reliability of Training Data Sets for ML Classifiers: A Lesson Learned from Mechanical Engineering

Juric

Danilchanka²,

Mousavi³

2020

Proceedings of the Annual Hawaii International Conference on System Sciences

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The popularity of learning and predictive technologies, across many problem domains, is unprecedented and it is often underpinned with the fact that we efficiently compute with vast amounts of data and data types, and thus should be able to resolve problems, which we could not in the past. This view is particularly common among scientists who believe that the excessive amount of data, we generate in real life, is ideal for performing predictions and training algorithms.However, the truth might be quite different.The paper illustrates the process of preparing a training data set for an ML classifier, which should predict certain conditions in mechanical engineering. It was not the case that it was difficult to define and choose classifiers, in order to secure safe predictions. It was our inability to create a safe, reliable and trustworthy training data set, from scientifically proven experiments, which created the problem. This places serious doubts on the way we use learning and predictive technologies today. It remains debatable what the next step should be. However, if in ML algorithms, and classifiers in particular, the semantic which is built-in data sets, influences classifier's definition, it would be very difficult to evaluate and rely on them, before we understand data semantics fully. In other words, we still do not know how the semantic, sometimes hidden in a data set, can adversely affect algorithms trained by them.

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Identification of a wheel–rail adhesion coefficient from experimental data during braking tests

Cited by 27 publications

References 11 publications

Analysis of steering performance of differential coupling wheelset

Analysis of steering performance of differential coupling wheelset

Train energy simulation with locomotive adhesion model

Reliability of Training Data Sets for ML Classifiers: A Lesson Learned from Mechanical Engineering

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