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Record W2006243149 · doi:10.1115/1.2906222

A Numerical Study on the Effect of CO Addition on Flame Temperature and NO Formation in Counterflow CH4/Air Diffusion Flames

2008· article· en· W2006243149 on OpenAlexaff
Hongsheng Guo, W. Stuart Neill

Bibliographic record

VenueJournal of Engineering for Gas Turbines and Power · 2008
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsCarbon monoxideAdiabatic flame temperatureDiffusion flameDiffusionChemistryAnalytical Chemistry (journal)Fraction (chemistry)MonoxideMole fractionPremixed flameCarbon fibersMaterials scienceThermodynamicsInorganic chemistryCombustionOrganic chemistryComposite materialPhysical chemistryCombustorCatalysis

Abstract

fetched live from OpenAlex

A numerical study was carried out to understand the effect of CO enrichment on flame temperature and NO formation in counterflow CH4/air diffusion flames. The results indicate that when CO is added to the fuel, both flame temperature and NO formation rate are changed due to the variations in adiabatic flame temperature, fuel Lewis number, and chemical reaction. At a low strain rate, the addition of carbon monoxide causes a monotonic decrease in flame temperature and peak NO concentration. However, NO emission index first slightly increases, and then decreases. At a moderate strain rate, the addition of CO has negligible effect on flame temperature and leads to a slight increase in both peak NO concentration and NO emission index, until the fraction of carbon monoxide reaches about 0.7. Then, with a further increase in the fraction of added carbon monoxide, all three quantities quickly decrease. At a high strain rate, the addition of carbon monoxide causes increase in flame temperature and NO formation rate, until a critical carbon monoxide fraction is reached. After the critical fraction, the further addition of carbon monoxide leads to decrease in both flame temperature and NO formation rate.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.212
Threshold uncertainty score0.397

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 head0.005
GPT teacher head0.220
Teacher spread0.214 · 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

Citations2
Published2008
Admission routes1
Has abstractyes

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