High -speed Train Running Safety under Random Wind Effect
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Bibliographic record
Abstract
The aerodynamic performance of the high-speed train deteriorates in the strong wind environment when the railway vehicles have been made increasingly lighter to reach higher speeds, affecting their running safety. To explore the differences in the running safety of high-speed trains under different wind load models, the time history curve of random wind was simulated considering the random characteristic of wind using the discretizing and synthesizing random flow generation method based on the Von-Karman spectrum. Next, the aerodynamic characteristics of trains were solved and analyzed using the Star-CCM+ solver through the improved delayed detached eddy simulation technique. On this basis, the indexes of the train running safety were compared and evaluated by using the multi-body system dynamics simulation software SIMPACK. Results demonstrate that the mean values of the aerodynamic load coefficients of trains under different wind fields have little difference. The pulsation of the random wind effect becomes stronger with the increase in the yaw angle. The standard deviation of the lateral force coefficient under random wind reaches 0.238 at the yaw angle of 20, greatly reflecting the aerodynamic load changes under the pulsation characteristics of a random wind effect. In terms of train running safety, the peak value of each index far exceeds the mean value under the random wind load, and the influence of the pulsation of random wind load on the train running safety indexes is enhanced with the increase in the yaw angle. When the yaw angle is 20, the peak value of the train overturning coefficient exceeds 68% of the mean value, indicating that sufficient safety margin should be reserved in the evaluation of train running safety, and it is more reasonable to evaluate train running safety under a random wind field. The change curves of the running safety indexes under random wind should be filtered, and the appropriate filtering frequency is 20 Hz. The proposed method provides a scientific basis for more accurately evaluating the train running safety under crosswind.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it