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Record W1742017724 · doi:10.1002/cjce.22256

Numerical Study of the Effects of Oxygen Concentration and Fuel Jet Velocity on Thermal Radiation in Methane and Propane Turbulent Diffusion Flames

2015· article· en· W1742017724 on OpenAlexvenueno aff
Long Wu, Noriyuki Kobayashi, Zhanyong Li, Hongyu Huang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsPropaneMethaneSootCombustionJet (fluid)Turbulent diffusionChemistryDiffusionDiffusion flameThermal radiationRadiative transferTurbulenceThermodynamicsMaterials scienceOrganic chemistryPhysicsCombustorOptics

Abstract

fetched live from OpenAlex

Thermal radiation is an important mode in combustion and plays a key role in improving energy efficiency and reducing the formation of pollutants in industrial combustion systems. This paper describes the effects of oxygen concentration and fuel (methane and propane) jet velocity on thermal radiation in turbulent diffusion flames via numerical evaluation, using a detailed gas‐phase reaction mechanism consisting of 36 species and 219 elementary reactions with the discrete ordinates method. The predictions show close agreement with the experimental results in literature. Specifically, the radiative heat transfer flux and radiation fraction of propane flames were larger than those of methane flames at the same input power because of the increased soot yield in propane flames. In both methane and propane flames, thermal radiation generally increased with oxygen concentration, while at higher oxygen concentrations the degree of radiation increase was lower. Because of the promotion of soot oxidation at higher fuel velocities, the radiant fractions were lower than at lower fuel velocities.

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 score0.252
Threshold uncertainty score0.200

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 head0.006
GPT teacher head0.182
Teacher spread0.176 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

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