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Record W2535897107 · doi:10.5539/mas.v11n2p19

Numerical Simulation of Critical Velocity in Ventilation

2016· article· en· W2535897107 on OpenAlexvenueno aff
Jabar Kesadian, Armen Adamian

Bibliographic record

VenueModern Applied Science · 2016
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsnot available
Fundersnot available
KeywordsCritical ionization velocityVentilation (architecture)Dimensionless quantityFluentMechanicsEnvironmental scienceComputer simulationObstacleFlow (mathematics)AerodynamicsMarine engineeringMeteorologyEngineeringPhysicsLaw

Abstract

fetched live from OpenAlex

Given the importance of the safety and health of passenger’s in underground tunnels, analysis and simulation of fires in tunnels is to design a ventilation system. Longitudinal ventilation systems are widely used in tunnel ventilation and are one of the most important parameters for fire safety in this type of critical velocity system. Critical velocity of ventilation is minimum longitudinal velocity flow of air that prevents the smoke from the fire to the upstream flow.In this study, the effects of the distance between the fire source of the tunnel, obstacle before and after the fire, several sources of fire in the tunnel and ventilation shaft tunnels will be discussed on the critical velocity. Each effect parameter according to a correlation analysis of numerical results obtained using the solver FLUENT software Airpak and then the relationship between dimensionless critical velocity and dimensionless heat release are stated.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.283
Teacher spread0.267 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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
Published2016
Admission routes1
Has abstractyes

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