Effect of different types of vortex generators on delaying flow separation
Bibliographic record
Abstract
Under crosswind conditions, ensuring the safe operation of high-speed trains is a top priority. The crosswind-induced separated flow on the train roof may form large-scale vortical structures on the leeward side, increasing the risk of overturning. Because of their scalability and ability to energize the flow, vortex generators (VGs) are regarded as a promising control measure when installed on high-speed trains. Therefore, it is essential to evaluate the effectiveness of vortex generators in delaying flow separation on the train roof. In this study, three-dimensional structured grids were constructed for the Baseline case, as well as for rectangular, triangular, and wedge-shaped VGs. Large eddy simulation was employed for numerical simulations. The comparative analysis leads to the following conclusions: the vortices generated by the VGs are located at the interface between the boundary layer and the free stream. The induced downwash flow effectively transports momentum from the free stream into the boundary layer, thereby reducing its thickness, enhancing attachment, and delaying separation. The delayed flow, shifted downward, weakens the shear layer characterized by υ′w′¯ on the train's leeward side. This suppresses the unsteady growth of large-scale vortical structures by reducing energy uptake from the free stream, ultimately contributing to improved crosswind stability of the train.
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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 | 0.001 |
| 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".