Dynamic Response Analysis of Medium-Speed Maglev Train with Track Random Irregularities
Bibliographic record
Abstract
In order to analyze the dynamic response of medium-speed maglev train in the speed range of 0–200 km/h, the suspension performance, suspended energy consumption, and riding comfort of the train stimulated by random track irregularities are discussed in this paper. Firstly, the model of medium-speed maglev train including car body, air spring vibration isolation system, and the suspension system is established. Then, a controller based on flux inner feedback loop and PID outer feedback loop is designed for the suspension system. The established model is stimulated by the actual track power spectrum in full speed range. The simulation results show that the fluctuation of suspension gap is less than ±4 mm. Furthermore, thanks to the adding of permanent magnet, the power consumption is significantly reduced, which is of benefit to the electromagnet heating problem and on-board levitation power supply system. The riding comfort of the train moving on the irregular track using Sperling index is assessed. The experimental results validate the effectiveness of the proposed analytical calculation model of medium-speed maglev train. It is shown that medium-speed maglev train achieved good performance, significant power reduction, and satisfactory riding comfort.
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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".