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Record W624952588

Wheel RCF issues in coal operations in Australia and the USA

2014· article· en· W624952588 on OpenAlexaboutno aff
Scott Simson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsTreadDisc brakeBrakeSlip (aerodynamics)Groove (engineering)TribologyEngineeringAutomotive engineeringForensic engineeringMechanical engineeringMaterials scienceComposite material
DOInot available

Abstract

fetched live from OpenAlex

Bradken has been asked by its clients to investigate wheel RCF (Rolling Contact Fatigue) occurring in standard gauge coal rollingstock. In particular high impact wheel occurrence from rail squat like surface initiated sub surface RCF. In the USA similar RCF issues have been under investigation by the AAR with a considerable amount of publications in the last five years. In the USA wheel RCF issues has been linked to sharper curvatures and profile mismatch. The USA study has drawn a link to asymmetric wheel wear with worn rail profiles exhibiting the shapes of the asymmetric worn wheels. These worn rails then exhibit deteriorating contact stresses with most wheels increasing the likelihood of rolling contact fatigue. Canadian experience associates wheel RCF shelling with the occurrence of metal pickup in tread braking. Bradken and other parties have investigated RCF occurrences and the wheel steel manufacture in Australian coal operations. The higher material hardness has appeared to increase wheel RCF occurrence over wear. Brake shoe metal pickup and slip damage appear closely associated with RCF occurrences. The uni-directional Australian operations develop distinct worn wheel profiles. Worn Profiles include asymmetry and a groove beyond the normal running surface. The wheel profile maintenance in Australia has not been subject to optimisation and is far removed from the international best heavy haul practice.

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 score0.029
Threshold uncertainty score0.498

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.235
Teacher spread0.225 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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