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Record W2045606788 · doi:10.1115/jrc2008-63023

Dynamic Analysis of Pitch Plane Railway Vehicle-Track Interactions Due to Single and Multiple Wheel Flats

2008· article· en· W2045606788 on OpenAlexaff
Rajib Ul Alam Uzzal, Subhash Rakheja, Waiz Ahmed

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsConcordia University
Fundersnot available
KeywordsBallastTrack (disk drive)Parametric statisticsCritical speedPlane (geometry)Structural engineeringVehicle dynamicsPhase (matter)Contact forceNonlinear systemAutomotive engineeringEngineeringMechanical engineeringPhysicsElectrical engineeringGeometry

Abstract

fetched live from OpenAlex

The dynamic impact forces due to the wheel defect such as a flat is a main concern for a heavy freight train operating at high speed. The present investigation employs a pitch plane vehicle model coupled with a comprehensive three-layer track system model to study the impact force generated in the wheel-rail interface due to the presence of wheel flats. The wheel-rail contact is modeled using nonlinear Hertzian contact theory. Responses in terms of wheel-rail impact load and forces transmitted to bearings, pad and ballast are evaluated in an attempt to identify desirable design and operating factors. Wheel-rail impact loads due to the presence of multiple flats either in single or different wheels in-phase or out-of-phase conditions are evaluated and analyzed. A detailed parametric study is carried out that includes the variations in selected vehicle, track, operational as well as flat parameters. The results show that the effect of multiple flats is insignificant if they are more than 45° apart. The impact due to single wheel flat can be larger than in-phase flats at each wheel due to the presence of pitch dynamics. The parametric study shows that other than speed, depth and length of the flats are most sensitive parameters, and there exists a critical length at each flat depth that leads to the largest impact load.

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 score0.072
Threshold uncertainty score0.652

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.009
GPT teacher head0.208
Teacher spread0.199 · 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
Published2008
Admission routes1
Has abstractyes

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