Carrydown of liquid friction modifier
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".