Dynamic characteristics of a wheelset–track system under corrugation excitations in the metro operation process
Bibliographic record
Abstract
There are few systematic researches on the dynamic characteristics of a wheelset–track system under different corrugation excitations. To study the influence of different corrugations on the wheelset–track dynamic characteristics, a three-dimensional wheelset–track rolling contact model is established, and the model rationality is analyzed. Then, the wavelength and wave depth of the initial corrugation irregularity for simulation analysis are determined according to the measured data. Finally, the wheel–rail vertical force and wheel vertical vibration acceleration are selected as the output variables, and the wheelset–track dynamic responses under excitations are studied. The results show that comparing with the middle/long-wavelength irregularity, the short-wavelength irregularity will not increase the amplitude of wheel–rail dynamic action but will increase the frequency of wheel–rail dynamic action. The initial irregularity wave depth will not affect the frequency of wheel–rail dynamic action but will affect the amplitude of wheel–rail dynamic action, and the greater the amplitude of the wave depth is, the greater the impact on the dynamic responses of the wheelset–track system is. The main characteristic frequencies of output variables are close to the initial irregularity passing frequency, and the other peak frequencies are in a frequency multiplication relationship with the initial irregularity passing frequency.
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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".