Impact analysis due to multiple wheel flats in three-dimensional railway vehicle-track system model and development of a smart wheelset
Bibliographic record
Abstract
Wheel flats can create high-magnitude impact forces at the wheel/rail interface, these can induce high levels of local stress leading to fatigue damage, and failure of various vehicle and track components. With demands for increased load and speed levels, the issue of a strategy for effective maintenance and in-time replacement of defective wheel-flat-containing wheels has become an important concern for heavy haul operators. A comprehensive coupled vehicle/track model is generally used to predict the impact forces and the resulting component stresses in the presence of multiple flats. This paper considers the dynamic impact responses due to the presence of multiple flats. The characteristics of the bounce, pitch, and roll motions of the bogie due to a flat on a single wheel are investigated. The effect of multiple flats on the peak acceleration of a wheel is investigated for different sizes and relative positions of the flats, i.e. in-phase and out-of-phase conditions. This paper further presents the development of a smart wheelset for the detection of wheel flats for two different load conditions; it is based on a derived relationship between the peak wheel acceleration, vehicle speed and flat size.
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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 | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".