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Record W4237963808 · doi:10.1115/jrc2012-74015

Lateral Creepage

2012· article· en· W4237963808 on OpenAlexaff
Nazmul Hasan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsSNC-Lavalin (Canada)
Fundersnot available
KeywordsTangentRADIUSGeometryFlangeTrack (disk drive)Slip (aerodynamics)Base (topology)Structural engineeringPhysicsMathematicsMathematical analysisEngineeringComputer science

Abstract

fetched live from OpenAlex

This is a theoretical work on lateral creepage, εy for both tangent and curved track. From the equation of motion on tangent track, the expression for the maximum lateral velocity is obtained and then followed by the basic definition of lateral creepage to obtain the maximum lateral creepage. For tangent track the maximum lateral creepage is given by: εy=y0RK in which, y 0 is wheel clearance, γ is conicity, r is nominal wheel radius, s is the track width, RK is the Klingel radius. Lateral creepage for a curve is expressed by replacing the Klingel radius with the radius of curve, R as shown below: εy=y0R A lateral slip in the running surface exists because of the wheel’s attack angle, which causes the flange to push against the inside of the railhead; consequently, a lateral slip force will be developed [1]. Clearly, this situation provides valid grounds to find a correlation between lateral creepage and the angle of attack. Exactly this is given below. For curved track: εy=0.069B2R+0.2862y0B in which, B is wheel base, R is radius of curve. This equation is suggested for the lateral creepage on the curve, as it contains both components of the angle of attack that connects all important parameters, such as radius, wheel base and wheel clearance. A threshold creepage value of 0.0045 is suggested. It is shown that a curve with a radius greater than 300 m should not produce wheel squeal noise.

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 score0.323
Threshold uncertainty score0.285

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 head0.006
GPT teacher head0.180
Teacher spread0.174 · 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
Published2012
Admission routes1
Has abstractyes

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