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Record W2171373440 · doi:10.1115/ht2007-32466

Effects of Hydrogen and Helium Addition to Fuel on Soot Formation in Axisymmetric Coflow Laminar Methane-Air Diffusion Flame

2007· article· en· W2171373440 on OpenAlexaff
Fengshan Liu, Francesca Migliorini, F. Cignoli, Silvana De Iuliis, G. Zizak

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsSootLaminar flowHydrogenDiffusion flameMethaneHeliumChemistryRadiative transferAcetyleneThermodynamicsMaterials scienceCombustionCombustorPhysical chemistryPhysicsOrganic chemistryOptics

Abstract

fetched live from OpenAlex

Numerical and experimental studies were conducted to investigate the effects of hydrogen and helium addition to fuel on soot formation in atmospheric axisymmetric coflow laminar methane-air diffusion flame. Soot temperature and volume fraction distributions were measured using a two-dimensional two-color technique. Numerically the conservation equations of mass, momentum, energy, and species in the limit of low-Mach number were solved. Detailed gas-phase chemistry and thermal and transport properties were accounted for. Radiative heat transfer by CO, CO2, H2O, and soot was calculated using the discrete-ordinates method with the radiative properties of the mixture obtained from a wide-band model. Soot was modeled using a two-equation semi-empirical model in which the mechanisms for inception and surface growth are assumed to be PAH coagulation and H-abstraction acetylene addition. Both experimental and numerical results show that helium addition is more efficient than hydrogen addition in reducing soot formation in the methane flame. These results are different from the previous investigations in ethylene flames where the hydrogen addition was found to be more effective in reducing soot formation than helium addition due to the additional chemical suppression of hydrogen on soot. It is suggested here that hydrogen chemically enhances soot formation when added to methane.

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 score0.445
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.004
GPT teacher head0.196
Teacher spread0.192 · 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
Published2007
Admission routes1
Has abstractyes

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