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Record W2016016247 · doi:10.1115/imece2004-59407

A Numerical Study of the Influence of Hydrogen Addition on Soot Formation in a Laminar Counterflow Ethylene/Oxygen/Nitrogen Diffusion Flame

2004· article· en· W2016016247 on OpenAlexaff
Hongsheng Guo, Fengshan Liu, Gregory J. Smallwood

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemical Engineering
TopicAdvanced Combustion Engine Technologies
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsSootHydrogenDiffusion flameEthyleneOxygenChemistryLaminar flowPropaneDiffusionBenzeneHeliumDilutionInorganic chemistryMaterials scienceAnalytical Chemistry (journal)Chemical engineeringCombustionThermodynamicsOrganic chemistryCatalysis

Abstract

fetched live from OpenAlex

The influence of hydrogen addition to the fuel on soot formation in an ethylene/oxygen/nitrogen diffusion flame was numerically studied by simulation of three counterflow laminar diffusion flames at atmosphere pressure. The fuel mixtures for the three flames are pure ethylene, ethylene/hydrogen and ethylene/helium, respectively, while the oxidant is a mixture of oxygen and nitrogen. A detailed gas phase reaction mechanism including species up to benzene and complex thermal and transport properties were used. The soot inception and surface growth rates were, respectively, calculated based on benzene and HACA (H-abstraction and C2H2-addition) mechanisms. The predicted results for the three flames were compared and analyzed. It is indicated that although the addition of either hydrogen or helium to the fuel can reduce the soot volume fraction, the addition of hydrogen is more efficient. While the addition of helium reduces soot formation only through dilution, the addition of hydrogen suppresses soot formation through both dilution and chemical reaction effects. This conclusion is qualitatively consistent with available experiments. The simulations revel that the chemically inhibiting effect is caused by the decrease of hydrogen atom concentration in soot formation region, due to the displacement of the primary reaction zone, when hydrogen is added to the fuel.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.766
Threshold uncertainty score0.412

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 head0.008
GPT teacher head0.225
Teacher spread0.217 · 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 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

Citations4
Published2004
Admission routes1
Has abstractyes

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