Mitigation of crosswind effects on high-speed trains using vortex generators
Bibliographic record
Abstract
Vortex generators can enhance the operational safety of high-speed trains and offer effective anti-rolling performance. This paper investigates the influence of vortex generator installation angles on the aerodynamic characteristics of trains. The Improved Delayed Detached Eddy Simulation method is used to analyze the leeward side vortex structure. It is found that when the angle between the vortex generators and the relative wind is 30°, the rolling moment of the train is minimized, as it significantly reduces side forces while preventing excessive growth of lift force inducing rolling moment. The reduction in rolling moment of the train by vortex generators is attributed to the suppression of leeward side trailing vortices of the train, which delays flow separation at the roof of the train, inducing a downward trend in the separated flow. Dynamic Mode Decomposition reveals that vortex generators do not alter the stability of near-body trailing vortices but enhance the pulsatile characteristics of far-body trailing vortices, which do not affect the pressure distribution on the leeward side of the train.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".