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Record W2893123558 · doi:10.11648/j.ep.20180201.15

Natural Ventilation of Smoke Subject to Wind Pressure Within Fire Safety Engineering Approach

2018· article· en· W2893123558 on OpenAlexaff
Rachid Fakir

Bibliographic record

VenueEngineering Physics · 2018
Typearticle
Languageen
FieldEngineering
TopicEvacuation and Crowd Dynamics
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsBuoyancySmokeNatural ventilationComputer simulationMechanicsEnvironmental scienceWind tunnelVentilation (architecture)EngineeringMarine engineeringSimulationMeteorologyComputer scienceAerospace engineeringMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

This paper presents an analytical model able to explore the design method of natural smoke evacuation through an exhaust vent subject to wind pressure. The proposed model can be used to determine the flow rate of smoke and the optimal section of the exhaust vent according to the geometric parameters of the room, the heat release rate and the pressure exerted by the wind forces at the exhaust vent. The numerical resolutions of the mathematical equations of the model were performed using the dichotomy method. The proposed analytical approach is built progressively by, (i) the examination of interaction forces between buoyancy forces and wind pressure forces, (ii) a numerical validation of the analytical model using the numerical simulation software Fire Dynamics Simulator, and (iii) proposal of a method of approximation of the optimal section of the exhaust vent for a maximum smoke evacuation rate. The results show very satisfactory and provide adequate prediction of the optimal size of the exhaust vent between the theoretical approximation and various configurations of numerical simulation. The feasibility and effectiveness of the proposed approach lead to an accurate and reliable analytical model able to analyze the influence of the size of natural smoke evacuation vents subjected to the pressure of wind forces.

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 categoriesMeta-epidemiology (narrow)
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 score0.370
Threshold uncertainty score1.000

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 head0.200
Teacher spread0.193 · 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.

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
Published2018
Admission routes1
Has abstractyes

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