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Record W2035364434 · doi:10.1021/ef800854e

Characteristics of a Multi-jet Burner in Oxy-Liquefied Petroleum Gas (LPG) Flames

2009· article· en· W2035364434 on OpenAlexfundno aff
Hyeon-Jun Kim, Wonyoung Choi, Soo Ho Bae, Hyun Dong Shin

Bibliographic record

VenueEnergy & Fuels · 2009
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsnot available
FundersKorea Science and Engineering FoundationCanada Excellence Research Chairs, Government of Canada
KeywordsCombustorCombustionKeroseneMechanicsAdiabatic flame temperatureChemistryMole fractionOxygenGas burnerFlame structurePremixed flameMixing (physics)Jet (fluid)DissipationScalar (mathematics)TurbulenceVolumetric flow rateAnalytical Chemistry (journal)ThermodynamicsPhysicsChromatographyOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

In this study, a multi-jet burner with an extremely intense flame was designed for oxy-fuel combustion. The flame characteristics were experimentally and numerically investigated at a fixed overall flow rate of fuel and oxygen and at oxygen feeding ratios (OFRs) of 0.25, 0.5, and 0.75, which gives an overall equivalence ratio of 0.909. The measured temperature profiles were compared to values predicted by numerical simulations, and good agreement was observed. To determine the cause of differing flame height at various OFRs, the iso-surfaces of the fuel, oxygen mole fraction, and the mixture fraction in the physical space were investigated using the numerical data. These results can be understood through an analysis of the scalar dissipation rate, which signifies the mixing characteristics of the fuel with the oxygen and the destruction of scalar fluctuations by turbulent mixing. The flame height seen at an OFR of 0.25 was the lowest because the peak scalar dissipation rate was higher than at other flow conditions. This information is important to reduce the flame height for the control of an intense flame.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.435
Threshold uncertainty score0.702

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 head0.206
Teacher spread0.199 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

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