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Record W2329024902 · doi:10.1115/ht-fed2004-56892

Numerical Simulation of a Pool Fire and Large Object in a Cross-Wind

2004· article· en· W2329024902 on OpenAlexaff
Cécile Devaud, Elizabeth J. Weckman

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsEnclosureFuselageComputational fluid dynamicsCulvertEnvironmental scienceWind speedLarge eddy simulationCrosswindMeteorologyAerospace engineeringCombustionAerodynamicsMarine engineeringEngineeringStructural engineeringPhysicsTurbulence

Abstract

fetched live from OpenAlex

The present investigation is focused on assessing the capabilities of Large Eddy Simulations (LES) using simplified sub-models for combustion and soot in a specific fire scenario. Fire development resulting from an aviation fuel spill close to a plane fuselage is considered. The computational domain and boundary conditions are defined according to the experimental configuration used in tests run by the Fire Research Group at the University of Waterloo. The present setup consists of a 2-m-diameter pool fueled with kerosene and located 1-m-upstream of a 2.7-m-diameter culvert in a large enclosure. A cross-wind with a velocity of 13 m/s is imposed on the fire and culvert. The calculations are time-dependent and three-dimensional. Sensitivity to the grid refinement, size of the enclosure and wind profiles is first investigated. Comparison between measured temperatures and numerical results across the computational domain is made. Velocity profiles are also examined. Reasonable agreement with the experiments is found. In the light of the present results, directions for future work are also discussed.

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)0.001
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 score0.016
Threshold uncertainty score0.031

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.280
Teacher spread0.272 · 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

Citations1
Published2004
Admission routes1
Has abstractyes

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