Braking force distribution strategy for virtual rail trains based on I-curve
Bibliographic record
Abstract
The virtual rail trains have attracted much attention in recent years thanks to their high passenger capacity and low construction cost. In this paper, braking force distribution strategy for virtual rail trains with three-car and six-axle was studied. Based on the principle of axle load proportion distribution, braking force distribution method of the four-wheel single car under curve braking was studied by applying the I-curve theory. The distribution laws of centrifugal force between axles during curve braking were analyzed, the lateral force provided by the tires of each axle can be calculated, and the remaining tire adhesion can be set as the upper limit of braking force, which can ensure the maximum utilization of tire adhesion. The method was extended to multi-series trains by analyzing the additional influence of hinge points on the lateral force, and a braking force distribution strategy considering the car body hinge coupling relationship was proposed. Finally, the effectiveness of the strategy was verified by hardware-in-the-loop tests. The research results provide guidelines on the brake control of newly emerged virtual rail trains.
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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 | 0.000 |
| 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) | 0.000 | 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".