Effects of rail hardness on transverse profile evolution and computed contact conditions in a full-scale wheel-rail test rig evaluation
Bibliographic record
Abstract
This paper revisits a comprehensive data set generated in a prior test program using a full-scale wheel-rail test rig. The test program evaluated premium and standard grade rail steels with respect to wear and rolling contact fatigue (RCF). The current work evaluates the evolution of rail profiles throughout the test cases, taking advantage of the database of wheel and rail profiles that were collected. Contact conditions are modelled using the CONTACT library, including recent developments in the handling of conformal geometries and interfacial layers. Observations are made regarding the relative characteristics of rail profiles for each steel type, as they evolve with accumulated wheel passes, on the basis of the computed contact conditions. • Improved performance of conformal CONTACT solver, versus planar solvers • Reduced wear for higher hardness rail grades, without an inherent increase in RCF • Importance of establishing and maintaining profiles for higher hardness rail grades
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".