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Record W4232132434 · doi:10.22215/etd/2014-10517

Equivalence Ratio Gradient Effects on Local Flame Front Topology and Heat Release Rate in Stratified, Iso-Octane/Air Turbulent V-Flames

2014· dissertation· en· W4232132434 on OpenAlexafffund
Patrizio Vena

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsCarleton University
FundersNational Research Council Canada
KeywordsPlanar laser-induced fluorescenceTurbulencePremixed flameLaminar flame speedMechanicsFlame structureDiffusion flameCurvatureMaterials scienceTransverse planeOctaneCombustorAnalytical Chemistry (journal)CombustionChemistryGeometryLaser-induced fluorescenceMathematicsPhysicsOpticsChromatographyLaserOrganic chemistry

Abstract

fetched live from OpenAlex

This thesis details the development and application of an experimental apparatus and methodology capable of quantifying the effects of partial premixing on the local behaviour of turbulent flames. A novel rectangular slot burner was used to generate controlled, transverse variations in mixture strength along its exit plane, such that turbulent, rod-stabilized V-flames could be anchored in reactant mixtures of varying mean gradients in equivalence ratio. A unique windowing approach was devised in which 3-pentanone tracer planar laser induced fluorescence (PLIF) of the reactants was used to determine an analysis region of interest (ROI) within the flame. Iso-contours of equivalence ratio (e.g. = 0.95-1.05 for nearstoichiometric flame regions) were traced up to the mean position of the flame front to define the width of the ROI, which was specific to each mean gradient flame condition. This analysis methodology enabled fair comparison among gradient settings, and ensured that any observed variation in local flame behaviour could be attributed specifically to mean gradient effects, rather than simple mixture strength effects.

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 score0.206
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.004
GPT teacher head0.214
Teacher spread0.209 · 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

Citations1
Published2014
Admission routes2
Has abstractyes

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