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Record W2063920172 · doi:10.5539/mas.v6n9p11

Flame Spread along a Thin Combustible Solid with Randomly Distributed Square Pores of Two Different Sizes

2012· article· en· W2063920172 on OpenAlexvenueno aff
Abe Syuhei, Hiroyuki Torikai

Bibliographic record

VenueModern Applied Science · 2012
Typearticle
Languageen
FieldEngineering
TopicFire dynamics and safety research
Canadian institutionsnot available
Fundersnot available
KeywordsPorositySquare (algebra)Materials scienceFlame spreadEnvironmental scienceComposite materialCombustionMathematicsGeometryChemistry

Abstract

fetched live from OpenAlex

The objective of our study is to predict the flame spread route by the quantity of combustible materials and their placement. In this paper, we examine non-uniform flame spread in open air along a thin combustible solid with randomly distributed square pores of two different sizes (8 x 8 and 4 x 4 mm respectively). Experimental results show that the flame-spread probability falls with increasing porosity. Despite uniform porosity, the flame-spread probability differs with the rate of large square pores to small square pores. For a combustible area larger than a noncombustible area, the flame-spread probability reaches the local minimum value with a change in R8 (ratio of 8 mm pores) under the same porosity condition. Conversely, for a combustible area smaller than a noncombustible area, the flame-spread probability reaches a local peak with changing R8 under the same porosity condition. In addition, we calculated the ratio of the unburned area (unburned area / total combustible area) by counting the unburned cells after the flame spread test, which might be useful to predict the fire hazard. We found that the ratio of unburned area grows with increasing porosity.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.715
Threshold uncertainty score0.530

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 head0.011
GPT teacher head0.246
Teacher spread0.235 · 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 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

Citations11
Published2012
Admission routes1
Has abstractyes

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