An investigation of high-frequency vibration of bogie frame due to wheel/rail short-pitch irregularities and its control methodologies
Bibliographic record
Abstract
High frequency impacts arising from wheel/rail short pitch irregularities have been widely reported as main casual factor of high frequency vibration of bogie frames recent years. This study aims at exploring characteristics of high frequency vibration of a metro bogie frame and its control methodologies. Firstly, the measurements obtained from a field test were employed to demonstrate the elastic vibration of bogie frame due to rail corrugation-induced impacts. Secondly, a rigid/flexible coupled dynamic model neglecting the wheel/rail contact model was established to simulate the vibration of bogie frame. Subsequently, the control methodologies were proposed to suppress the high frequency vibration of bogie frame. The results showed that the end of bogie frame is predominated by the localized bending vibration mode at 220 Hz, which is close to the passing frequency of rail corrugation at the rubber booted short sleeper track. The structural resonance serves as the main driving force of fatigue failure for the end of bogie frame. The structural improvement through installing a stiffener at end of bogie frame can effectively suppress the local bending of end of bogie frame. The piezoelectric-based actuator could serve as an alternative method to reduce the vibration level for considered frequency range.
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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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".