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Record W2317801388 · doi:10.2514/6.2010-775

Structure of Laminar Methane-Oxygen Diffusion Flames at High Pressures

2010· article· en· W2317801388 on OpenAlexaff
Joo Seok Hyun, Ömer L. Gülder

Bibliographic record

Venue48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition · 2010
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsLaminar flowMethaneOxygenDiffusionMaterials scienceThermodynamicsMechanicsChemical engineeringChemistryPhysicsEngineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Characteristics of laminar methane-oxygen diffusion flames were studied at sub-critical and supercritical pressures up to 100 atmospheres. The influence of pressure on soot formation and on the structure of the temperature field was investigated over the pressure range of 10 to 90 atmospheres in a high pressure combustion chamber using a non-intrusive, line-of-sight spectral soot emission diagnostic technique. Two distinct zones characterized the appearance of a methane and oxygen diffusion flame: an inner luminous zone similar to the methane-air diffusion flames and an outer blue flame zone. The flame height, marked by the visible soot radiation emission, was reduced by over 50% over the pressure range of 10 to 100 atmospheres at the higher methane flow rate. At the lower methane flow rate, above 10 atmospheres, the luminous flame zone started collapsing and solid carbonaceous structures appeared on the fuel nozzle rim. Further increase in pressure resulted in shrinking of the luminous zone into mostly solid material and only the blue flame zone was visible. For the higher methane flow rate, between 10 and 40 atmospheres, the soot levels increased with increasing pressure; however, above 40 atmospheres the soot concentrations reached a plateau and then began decreasing with increasing pressure.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.001
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 head0.009
GPT teacher head0.247
Teacher spread0.237 · 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

Citations0
Published2010
Admission routes1
Has abstractyes

Explore more

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