MétaCan
Menu
Back to cohort

EFFECTS OF WATER VAPOUR ADDITION TO THE AIR STREAM ON SOOT VOLUME FRACTION AND FLAME TEMPERATURE IN A LAMINAR COFLOW ETHYLENE FLAME

2013· article· en· W2055485714 on OpenAlexaff
Fengshan Liu, Jean-Louis Consalvi, Andrés Fuentes, Gregory J. Smallwood

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicCatalytic Processes in Materials Science
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsSootDiffusion flameLaminar flowVolume fractionDilutionAdiabatic flame temperatureVolume (thermodynamics)Materials scienceWater vaporDiffusionAbsorption (acoustics)Premixed flameAnalytical Chemistry (journal)ChemistryThermodynamicsCombustionEnvironmental chemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

The effects of adding water vapour to the air stream on flame temperature and soot volume fraction were investigated numerically in a laminar coflow ethylene diffusion flame at atmospheric pressure using a detailed C2 reaction mechanism including PAH. Thermal radiation was calculated using the discrete-ordinates method and a statistical narrowband correlated-k based wide band model for the absorption coefficients of CO2 and H2O. Soot formation was modeled using a PAH based inception model and the HACA mechanism for surface growth and oxidation. The added water vapour affects soot formation and flame properties through not only dilution and thermal effects, but also through chemical and radiation effects. Addition of water vapour significantly reduces radiation heat loss.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.003
GPT teacher head0.204
Teacher spread0.201 · 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 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

Citations0
Published2013
Admission routes1
Has abstractyes

Explore more

Same topicCatalytic Processes in Materials ScienceFrench-language works237,207