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Record W2070947300 · doi:10.1115/1.2432890

A Numerical Study on the Effect of Water Addition on NO Formation in Counterflow CH4/Air Premixed Flames

2008· article· en· W2070947300 on OpenAlexaff
Hongsheng Guo, W. Stuart Neill, Gregory J. Smallwood

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
KeywordsPremixed flameStoichiometryDiffusion flameAdiabatic flame temperatureChemistryDrop (telecommunication)Flame speedFlame structureLaminar flame speedThermodynamicsChemical engineeringMechanicsMaterials scienceAnalytical Chemistry (journal)CombustionOrganic chemistryCombustorMechanical engineering

Abstract

fetched live from OpenAlex

The effect of water addition on NO formation in counterflow CH4/air premixed flames was investigated by numerical simulation. Detailed chemistry and complex thermal and transport properties were employed. The results show that the addition of water to a flame suppresses the formation of NO primarily due to flame temperature drop. Among a lean, a stoichiometric, and a rich premixed flame, the effectiveness of water addition is most significant for the stoichiometric flame and least for the rich flame. The addition of water also reduces the formation of NO in a flame because of the chemical effect. Compared to the stoichiometric flame, the chemical effect is intensified in the lean and rich flames.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.007
GPT teacher head0.215
Teacher spread0.208 · 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 source (direct Gemma or distilled Codex), 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

Citations11
Published2008
Admission routes1
Has abstractyes

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