Aerodynamic Study of Two Opposing Moving Trains in a Tunnel Based on Different Nose Contours
Bibliographic record
Abstract
It is well known that the train nose shape has significant influence on the aerodynamic characteristics. This study explores the influence of four kinds of nose shapes (fusiform, flat-broad, bulge-broad, ellipsoidal) on the aerodynamic performance of two opposing high-speed trains passing by each other through a tunnel at 250 km/h. The method of three dimensional, compressible, unsteady Reynolds-averaged Navier-Stokes equations and RNG k-ε double equation turbulence model was carried out to simulate the whole process of two trains passing by each other inside a tunnel. Then the pressure variations on tunnel wall and train surface are compared with previous full-scale test to validate the numerical method adopted in this paper. The assessment characteristics, such as transient pressure and aerodynamic loading, are analyzed to investigate the influence of nose shape on these assessment parameters. It is revealed that aerodynamic performance of trains which have longitudinal nose profile line B (fusiform, flat-broad shape) is relatively better when passing by each other in a tunnel. The results can be used as a guideline for high-speed train nose shape design.
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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".