Coupling effect of parameters on critical speed and hunting frequency of the linearized railway bogie system
Bibliographic record
Abstract
Critical speed and hunting frequency are critical metrics for assessing running stability, with a significant impact on railway vehicle dynamic performance. Evaluating effects of vehicle parameters on these two metrics is important for the optimization of vehicle design. This study focuses on a rigid bogie with secondary suspension, for which a lateral dynamic model accounting for lateral displacement and yaw motion is developed. Analytical formulas for critical speed and hunting frequency were derived by using perturbation procedures, Newton method and some simplified treatments. Employing the analytical formulas, a comprehensive analysis of multi-parametric influences was carried out, yielding global sensitivity indices for critical speed and hunting frequency against various parameters. The results indicate that the derived analytical formulas align well with numerical calculations, providing a basis for preliminary vehicle design optimization. There exists a coupling interaction between different parameters for their effects on critical speed and hunting frequency, which indicates the necessity of total-order global sensitivity analysis. The equivalent wheel–rail contact conicity is identified as the most sensitive factor on both critical speed and hunting frequency, while the damping of yaw damper also significantly affects the hunting frequency.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".