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Record W4283721419 · doi:10.1115/jrc2022-84116

Implementation of the Contact Roughness at the Wheel-Rail Contact Model for Locomotive Traction Studies

2022· article· en· W4283721419 on OpenAlexaff
Maksym Spiryagin, Sanjar Ahmad, Esteban Bernal, Kevin Oldknow, Ingemar Persson, Qing Wu, Colin Cole, Tim McSweeney

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsTraction (geology)Surface finishContact patchCoupling (piping)Contact geometrySurface roughnessAutomotive engineeringComputer scienceTribologyMechanical engineeringContact areaEngineeringStructural engineeringMaterials scienceGeometryComposite materialTreadMathematics

Abstract

fetched live from OpenAlex

Abstract This paper introduces a solution to deliver the relation between the contour and real contact areas considering characteristics of surface geometry based on the adaptation of a tribological method [1] for calculation of the real area of contact and the real pressure between two rough surfaces. The implementation in the wheel-rail coupling architecture is kept the same as per the previously developed wheel-rail multibody couplings [2,3] based on the modified Fastsim [4] and Extended Contact [5] algorithms, which allows switching between the previous and new developed coupling for a comparative analysis. The results obtained, and limitations, are stated in this paper.

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 score0.132
Threshold uncertainty score0.279

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 head0.023
GPT teacher head0.277
Teacher spread0.254 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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