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Record W2252795224 · doi:10.4271/2000-01-0201

A Semi-Empirical Model of Spark-Ignited Turbulent Flame Growth

2000· article· en· W2252795224 on OpenAlexafffund
KENNETH R. C. MANN, David S.‐K. Ting, Paul Henshaw

Bibliographic record

VenueSAE technical papers on CD-ROM/SAE technical paper series · 2000
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSPARK (programming language)TurbulenceMaterials scienceMechanicsEnvironmental scienceComputer sciencePhysics

Abstract

fetched live from OpenAlex

<div class="htmlview paragraph">A semi-empirical turbulent flame growth model has been developed based on thermodynamic equilibrium calculations and experiments in a 125-mm cubical combustion chamber. It covers the main flame growth period from spark kernel formation until flame wall contact, including the effects of laminar flame speed, root mean square turbulence intensity, turbulent eddy size, and flame size. As expected, the combustion rate increases with increasing laminar flame speed and/or turbulence intensity. The effect of turbulent eddy scale is less obvious. For a given turbulence intensity, smaller scales produce higher instantaneous flame speed. However, turbulence of a smaller scale also decays more rapidly. Thus, for a given laminar flame speed and turbulence intensity at the time of ignition, there is an optimum turbulent eddy size which leads to the fastest combustion rate over the period considered. This optimum eddy size falls in between the more effective, but faster decaying small scale turbulence, and the longer lasting, less effective large scale turbulence which maintains the highest overall turbulence intensity over the combustion period considered.</div>

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), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.960
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.002
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 head0.013
GPT teacher head0.231
Teacher spread0.217 · 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 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

Citations8
Published2000
Admission routes2
Has abstractyes

Explore more

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