Influence of a flexible wheelset on the dynamic responses of a high-speed railway car due to a wheel flat
Bibliographic record
Abstract
Large magnitude impact loads caused by wheel flats may excite various vibration modes of wheelsets employed in high-speed trains and thereby contribute considerably to the dynamic response of vehicles. In this study, the wheelset is modeled as a flexible body using the modal approach, which is integrated to a multibody dynamic model of the high-speed train coupled with a flexible track slab model. The multibody dynamic model is formulated for a typical high-speed train consisting of a car body, two bogie frames, and four wheelsets. The track is modeled considering the rail as a Timoshenko beam discretely supported on a flexible track slab. The effects of the wheelset flexibility on the dynamic response are illustrated through comparisons with those obtained with a rigid wheelset considering different vehicle speeds and sizes of the wheel flat. Subsequently, the effects of wheel flats on the vehicle–track system are evaluated in terms of the wheel–rail impact forces, axle-box vertical acceleration, and dynamic stress developed in the wheelset due to a haversine wheel flat. The results suggest that the wheelset flexibility can lead to significantly higher axle-box vibration and wheelset axle stress compared to a rigid wheelset in the presence of a wheel flat.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".