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Record W2618724714 · doi:10.11159/htff17.146

CFD-Based Investigation of the Effectiveness of a Novel LongitudinalVentilation Concept in Tunnels

2017· article· en· W2618724714 on OpenAlexvenueno aff
Merve Altay, Ali Sürmen

Bibliographic record

VenueProceedings of the World Congress on Mechanical, Chemical, and Material Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicUnderground infrastructure and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsComputational fluid dynamicsComputer scienceVentilation (architecture)Marine engineeringAerospace engineeringEnvironmental scienceMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

The construction and complexity of road tunnels are substantially changing throughout the world to meet the need of changing traffic conditions. As a result of catastrophic multiple-death fires occurred in the past years in highway tunnels, the issue of fire safety in road tunnels has gained high importance and visibility throughout the world. However, it is really hard to conduct experimental or numerical studies to ensure fire safety in tunnels because of such reasons as being under the effect of so many dynamic parameters, large tunnel sizes, the heat feedback from the surrounding environment and the effect of natural ventilation on fire, etc. Despite all these restrictions, there are effective fire safety solutions providing high-level protection for people and reducing the risk of structural damage. However, that proposed solutions, most of which need extra space apart from the operational tunnel area, are too different systems from the current and aging tunnels in terms of construction. On the other hand, conventional longitudinal ventilation systems (CLVS) are still being commonly used, in spite of being insufficient to keep the fire, with high heat release rate (HRR), under control.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.208
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2017
Admission routes1
Has abstractyes

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