A smooth-guiding method for aerodynamic drag reduction on key regions of a high-speed train
Bibliographic record
Abstract
Bogie and pantograph areas are the important sources of the aerodynamic drag for high-speed trains. In this study, a smooth and fully encapsulated structure in bogie areas and a teardrop deflector in pantograph areas were designed to improve the train aerodynamic performance. Herein, two different three-car grouping high-speed train models were employed, i.e., Models I and II. The numerical simulations were carried out using an improved delayed detached eddy simulation (IDDES) method. The results show that after installing the smooth and fully encapsulated structure in bogie areas, the aerodynamic drag coefficients of bogie regions of the head and tail cars are reduced by 59.4% and 57.0%, respectively. In addition, the wake flow of the high-speed train is improved as well. The similar reduction is observed in the pantograph area. After installing the teardrop type deflector, the airflow over the pantograph area becomes smoother, which contributes to 45.6% aerodynamic drag reduction in the pantograph area. Therefore, combining with the smooth and fully encapsulated structure and teardrop type deflector, the Model II achieves an aerodynamic drag reduction of 17.3%.
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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 | 0.000 |
| 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".