Study of friction: measurement, analysis and practical implications for the wheel/rail contact
Bibliographic record
Abstract
Through a range of increasingly featured models of wheel-rail contact, vehicles and track it is possible to model in great detail the vehicle/track performance. But one aspect of the wheel/rail interaction that remains poorly understood and more poorly applied in models is the traction-creepage characteristic. Most models depend on the theory of Kalker, one that applies to “scrupulously clean surfaces”. A simple scaling factor approach to the Kalker model is available in many of the existing dynamics codes but is poorly understood and often not used. Even in the UK, with more experience than most countries in understanding prevailing friction conditions, consistent peak friction measurements of 0.23 +/- about 0.05 standard deviation are co-opted by the use of a 0.45 value in many experiments because the larger value gives ”best agreement with test force measurements and observations”. We report on a new instrument for field measurements of the traction creepage characteristic based on lateral creepage, and then show some early measurement values and compare them with those measured by a standard Salient hand pushed tribometer. Through dynamic modeling, the implications of the traction creepage relationship on forces, wear and vehicle stability are explored.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".