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Record W4229018343 · doi:10.1177/09544097221076258

Carrydown of liquid friction modifier

2022· article· en· W4229018343 on OpenAlex
Hatef Rahmani, Dmitry V. Gutsulyak, L.J.E. Stanlake, Boris Stoeber, Sheldon Green

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part F Journal of Rail and Rapid Transit · 2022
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsL.B. Foster Rail Technologies (Canada)University of British Columbia
Fundersnot available
KeywordsTrack (disk drive)Enhanced Data Rates for GSM EvolutionTangentOpticsMaterials scienceAcousticsPhysicsEngineeringMechanical engineeringGeometryMathematics

Abstract

fetched live from OpenAlex

A small-scale laboratory apparatus was built to study liquid friction modifier (LFM) behavior in a top-of-rail application. A field experiment was also carried out to complement the laboratory findings. KELTRACK® (a water-based LFM) was used as the test fluid. Laser-induced fluorescence served to measure the LFM thickness left on the track after the passage of the wheel. The lab experiments show that the LFM cannot withstand the high wheel-rail contact pressure in the nip present in cargo rail situations. As a result, the liquid is squeezed out laterally and attaches to the edges of the wheel contact band. This “edge liquid” is then carried down the track on the wheel. Gravimetric measurements of the wheel contact band confirm this observation, and show that only a minute amount of liquid is carried through the nip in the valleys between surface roughness features. In the field experiment, the LFM is applied from a trackside unit on a tangent section of the track. About 500 m downstream of the application point, the track has a curved section. LFM cannot be detected anywhere on the track a short distance (∼200 m) past the application unit. However, LFM is detectable on the curved sections of the track up to approximately 2 km from the application unit. This LFM on the curved track is believed to be due to transfer of the “edge liquid” from the wheel to the track, caused by the movement of the contact band as the train rounds a curve. The presence of LFM on the curved track far downstream is consistent with prior measurements of reduced lateral force on the curved track downstream of LFM application sites.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.332
Threshold uncertainty score0.439

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 head0.007
GPT teacher head0.176
Teacher spread0.169 · 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