2E23 Friction management as an integral part of the railway system(Infrastructure)
Bibliographic record
Abstract
Friction Management has become an integral part in daily operations of many railways. Friction Management includes the combined application of a gauge face lubricant and a top-of-rail (TOR) friction modifier. This paper will deal with the friction modifier aspect of friction management. The general concept of a friction modifier for TOR application will be highlighted and explained. The key properties of a friction modifier include optimized friction conditions between wheel and rail and positive friction/traction characteristics over a wide creepage range and different application rates. Furthermore, the effects of a friction modifier are discussed in this paper by referring to extensive laboratory and track testing. The reduction of lateral forces will reduce wear and damage of track components, namely the rail and wheel. The positive friction characteristics will impact squeal noise appearance, vibration emissions and corrugation development. The optimized friction conditions will mitigate derailment potential, improve ride quality and will allow for reductions in energy and fuel consumption. This differentiates a friction modifier from a gauge face lubricant that simply aims at reducing the coefficient of friction to a minimum value. Finally this paper will highlight the importance of considering friction management as a part of the railway system and not as a stand-alone solution in order to achieve the maximum system benefit.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".