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Record W6981832402

Flame speeds and stretch effects in counterflow aluminum flames

2015· dissertation· en· W6981832402 on OpenAlexaff

Bibliographic record

VenueeScholarship@McGill (McGill) · 2015
Typedissertation
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsMcGill University
Fundersnot available
KeywordsBunsen burnerCombustionDiffusion flameParticle image velocimetryCombustorPremixed flameAluminiumDragLaminar flame speed
DOInot available

Abstract

fetched live from OpenAlex

The characterization of flame propagation of reactive particles in gas suspension has been done either indirectly, such as from pressure traces in constant-volume combustion bombs, or with more traditional techniques such as flame tubes and Bunsen burners. The present thesis introduces a new counterflow burner for micron-scale particles resulting in a flat flame, which allows for more detailed measurements of the flame structure. Particle image velocimetry using the aluminum fuel particles as tracers determined the flow field. The resulting flow field was then corrected using Stokes drag analysis. Reference flame speeds of the corrected flow were found as a function of aluminum loading concentration and stretch rate. The reference flame speeds were found to be consistently higher than burning velocities in both the Bunsen and spherical flames. Furthermore, the reference flame speeds were dependent on hydrodynamic stretch. The reference flame speeds were highest for the low-stretch flames and lowest for high-stretch flames. The discrepancies between the Bunsen and flat flames are thought to be due to the competing diffusion of heat and species that changes between the two geometries. The dependence on stretch is thought to be caused by a higher thermal load in the preheat region for highly stretched flames, which in turn causes the flame to slow down.

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)0.001
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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.887
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
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 head0.008
GPT teacher head0.217
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 designOther design
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
Published2015
Admission routes1
Has abstractyes

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