Optimizing wind protection facilities and enhancing aerodynamic performance of track vehicles in railway transition section
Bibliographic record
Abstract
Purpose This study aims to optimize windbreak facilities and enhance the aerodynamic performance of trains in typical transition sections of the Lanzhou-Xinjiang High-Speed Railway under strong wind conditions. This research focuses on mitigating car body sway caused by abrupt wind speed variations, thereby improving operational safety and passenger comfort. Design/methodology/approach A combined methodology of field investigations and numerical simulations was used. Two optimization plans were proposed: Plan 1 reduces the slope angle from 38.03° to 20.00°, while Plan 2 increases the toe-to-wall distance to 31.32 m via mountain retreat. The Scale-Resolving Hybrid method based on shear-stress transport K-Omega turbulence model has been used to conduct numerical simulations to analyze flow fields, aerodynamic loads and pressure distributions under varying wind conditions. Findings After implementing the optimization plans, the wind speed distribution within the track area significantly decreased, with maximum values of all aerodynamic load parameters markedly reduced by over 50%. Specifically, the maximum positive and negative lateral forces decreased by 59.77% and 56.70%, respectively; the maximum positive and negative rolling moments decreased by 54.74% and 57.84%, respectively; and the maximum positive and negative yaw moments decreased by 54.88% and 57.38%, respectively. Originality/value The proposed solutions provide differentiated strategies for slope angle and spatial distance adjustments, validated through high-fidelity simulations. The results offer critical insights for designing windbreak facilities in high-speed railways wind-prone regions, reducing economic losses from speed restrictions and enhancing operational efficiency. This work bridges gaps in transition section aerodynamic studies and sets a benchmark for future engineering applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".