Impact load due to railway wheels with multiple flats predicted using an adaptive contact model
Bibliographic record
Abstract
Although numerous investigations have been undertaken to study the impact load between railway wheel and rail in the presence of a single wheel flat, little attention was devoted to the case of multiple flats. In practice, it is not uncommon that one wheel develops more than one flat or each wheel on the same axle has a single flat. In this study an adaptive contact model, a two-dimensional roll-plane vehicle model, and a three-dimensional track model are developed to investigate the wheel—rail impact load due to multiple flats. Unlike the commonly used Hertzian contact model, the adaptive contact model takes into account the asymmetry of the contact patch as the wheel flat enters and leaves the contact patch. Two scenarios of multiple flats are considered in this investigation. One deals with two flats on the same wheel defined by their size and relative position, and the other deals with a single flat on each wheel of the same axle. In each case, the induced impact loads are compared with those due to a single flat. The results suggest that the magnitude of impact force attributed to the second flat entering the contact region is strongly affected by the responses due to the preceding flat, depending upon the flat geometry, relative co-ordinates of the flats and the operating speed. The results further suggest that the length of a flat alone, which is commonly regarded as wheel removal criteria, may not be adequate when multiple flats are present.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".