CFD simulations of a semi-transverse ventilation system in a long tunnel
Bibliographic record
Abstract
In the present work, a semi-transverse ventilation system in a long tunnel with a length of 4.9 km, as a complex case study, is numerically studied by performing a set of three-dimensional steady incompressible computational fluid dynamics (CFD) simulations. The ventilation system consisted of a ceiling duct connected to two axial fans at the ending portals, and a series of jet fans in the main tunnel for supporting airflow in the desired direction. To focus on what can and cannot be achieved in commissioning tests, the ventilation system’s performance in various scenarios is numerically evaluated with two different tunnel states; empty tunnel and complete traffic congestion with 1176 stationary vehicles – which is almost impossible to evaluate during a commissioning test. By considering two hypothetical locations for the extraction zone from the main tunnel (in a distance of 450 and 1000 m from one portal), it is shown that the required number of jet fans in a traffic condition drops from 57 for the first extraction location to 43 (25% decrease) when the ventilation system extracts from the second zone. We show that if only the close axial fan to the extraction zone is activated, the required number of jet fans reduces by 56% and 72% for the first and second extraction locations, respectively. This finding can provide a cheaper and easier controlling scenario for emergency ventilation.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".