Numerical and experimental study on adaptive stiffness yaw damper for suppressing abnormal vibration of high-speed trains
Bibliographic record
Abstract
The mismatch between the parameters of the yaw damper and the equivalent conicity of wheel rail contact can lead to abnormal vibration of rail vehicles, while the stiffness variation range of traditional yaw dampers is very small, covering a limited range of equivalent conicity of wheel rail contact, resulting in the risk of carbody hunting at low conicity and bogie hunting at high conicity. To overcome the abovementioned shortcomings of traditional yaw dampers and reduce the abnormal vibration of high-speed trains under various operating conditions, this study proposes an adaptive stiffness yaw damper. The effectiveness of this solution was confirmed through roller testing rig and multi-body dynamic simulations. The results show that the device can dynamically adjust the stiffness according to the operating conditions of the vehicle, effectively reducing the carbody and bogie hunting under extreme wheel rail contact conditions, and thereby reducing the abnormal vibration of high-speed trains. At the same time, this device helps reconcile the trade-off between the curve negotiation performance and stability of vehicle, indirectly lessening the requirement for wheel–rail maintenance and reducing operational and maintenance expenses.
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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 | 0.001 |
| 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 | 0.001 | 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".