MétaCan
Menu
Back to cohort
Record W2018346415 · doi:10.1115/imece2006-14458

A Numerical Investigation of NOx Formation in Counterflow CH4/H2/Air Diffusion Flames

2006· article· en· W2018346415 on OpenAlexaff
Hongsheng Guo, Stuart W. Neill, Gregory J. Smallwood

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsDiffusionNOxHydrogenFraction (chemistry)Diffusion flameMass fractionFlame structureChemistryMole fractionAnalytical Chemistry (journal)Materials scienceThermodynamicsCombustionEnvironmental chemistryPhysical chemistryChromatographyOrganic chemistry

Abstract

fetched live from OpenAlex

A detailed numerical study was carried out for the effect of hydrogen enrichment on flame structure and NOx formation in counterflow CH4/air diffusion flames. Detailed chemistry and complex thermal and transport properties were employed. The enrichment fraction was changed from 0 (pure CH4) to 1.0 (pure H2). The result indicates that for flames with low to moderate stretch rates, with the increase of the enrichment fraction from 0 to 0.5~0.6, NO emission index keeps almost constant or only slightly increases. When the enrichment fraction is increased from 0.5~0.6 to about 0.9, NO emission index quickly increases, and finally NO formation decreases again when pure hydrogen flame condition is approached. However, for flames with higher stretch rates, with the increase of hydrogen enrichment fraction from 0 to 1.0, the formation of NO first quickly increases, then slightly decreases and finally increases again. Detailed analysis suggests that the variation of the characteristics in NO formation in stretched CH4/air diffusion flames is caused by the change of flame structure and NO formation mechanism, when the enrichment fraction and stretch rate are changed.

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 score0.375
Threshold uncertainty score0.386

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 head0.007
GPT teacher head0.204
Teacher spread0.197 · 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
Published2006
Admission routes1
Has abstractyes

Explore more

Same topicAdvanced Combustion Engine TechnologiesFrench-language works237,207