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Record W4385855430 · doi:10.1139/tcsme-2023-0078

Analysis of rail corrugation characteristics in the vehicle braking section of metro

2023· article· en· W4385855430 on OpenAlexvenueno aff
Zhiqiang Wang, Yichun Xu

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2023
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsSlip (aerodynamics)Transverse planeInstabilityMechanicsFinite element methodThreshold brakingRelative motionMaterials scienceStructural engineeringEngineeringAutomotive engineeringPhysicsBrake

Abstract

fetched live from OpenAlex

To study the evolution characteristics of corrugation in the vehicle braking section of the metro, a three-dimensional wheel–rail transient contact finite element model is established, and the contact stick–slip characteristics and rail wear distribution features are analyzed. The results show that, under the braking condition, the interface has stick–slip motion, indicating that the system is in a state of instability; the longitudinal relative slip is unevenly distributed; the peak position of the initial corrugation has a large longitudinal relative slip; and the transverse relative slip in the initial corrugation area is significantly larger than that in other areas, indicating that the initial corrugation will continue to develop and have a remarkable transverse wear trend. Under the nonbraking condition, there is no stick–slip motion at the interface, and the longitudinal and transverse relative slip values are less than the corresponding results under the braking condition. The cause of corrugation in the vehicle braking section is the wheel–rail unstable stick–slip motion triggered by the vehicle braking, and the fixed operating mode of the train provides the wavelength-fixing condition for the development of the initial corrugation, which leads to its continuous deterioration.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.395
Threshold uncertainty score0.517

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.200
Teacher spread0.190 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations3
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueTransactions of the Canadian Society for Mechanical EngineeringSame topicRailway Engineering and DynamicsFrench-language works237,207