Aerodynamic Performance of Different Metro Train Geometries through a Straight Tunnel Based on Steady and Unsteady Reynolds-Averaged Navier-Stokes Modeling
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Numerical RANS modeling has been carried out to assess the aerodynamics of different metro train geometries through a straight tunnel. A steady-state approach was first used to choose the best geometry out of seven alternatives in terms of drag reduction when compared with a typical blunt face train design representative of European metro networks. The proposed models have different edge-rounding characteristics at the front and rear faces. Afterward, the baseline and optimized geometries are compared at different train velocities, and the flow structure surrounding the models is discussed using unsteady RANS results. The study focuses on skin and pressure drag coefficients for trains traveling at 40 km h<sup>−1</sup> in a straight tunnel with a blockage ratio of 0.69. All the considered alternatives show a drag reduction between 5% and 20% relative to the baseline case.</div></div>
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".