Study on the influence of asymmetric mountain structures at tunnel portals on the aerodynamics of intersecting trains
Bibliographic record
Abstract
Abstract When two trains going through a tunnel pass each other, the difference of the surrounding space between a train's both sides rises, which induces abrupt aerodynamic force variations on the trains, resulting in the phenomenon of sudden swaying. This study employs the unsteady Reynolds-averaged Navier-Stokes (URANS) method of numerical simulation to analyse the effects of two terrain conditions, that is, a tunnel with and without asymmetric mountain structures at its portals, on the aerodynamic characteristics of two trains during their intersections. The results indicate that during two trains’ intersections at tunnel portals, the rear car suffers the highest risk of swaying. The presence of asymmetric mountain structures at tunnel portals reduces the risk of swaying the train adjacent to the mountains but increases the risk for trains farther away from the mountains. When trains intersect at the exit (for the train adjacent to the mountain) of the tunnel, the presence of mountain structures at the portal reduces the peak-to-peak lateral force values by 12.7% for the front car of the train adjacent to the mountain and increases by 16.5% for the rear car of the train away from the mountain. The impact of the mountain structures on the peak-to-peak values of a train's lateral force is minimal when two trains intersect at the midpoint of the tunnel. Therefore, it is suggested to consider the placement of symmetrical buffer structures or the modification of existing mountain structures at appropriate locations near tunnel portals to mitigate the abrupt lateral force variations experienced by passing trains.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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".