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Record W2327018697 · doi:10.1115/gt2014-25409

Flame Response Analysis of Syngas

2014· article· en· W2327018697 on OpenAlexafffund
J Górski, Wajid A. Chishty, Matthew R. Johnson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsNational Research Council CanadaCarleton University
FundersNational Research Council Canada
KeywordsSyngasCombustionLaminar flame speedPremixed flameCombustorLaminar flowDiffusion flameSyngas to gasoline plusMethaneFlame speedNatural gasMaterials scienceMechanicsHydrogenChemistryOrganic chemistryHydrogen productionPhysicsSteam reforming

Abstract

fetched live from OpenAlex

The motivation for the work comes from the requirement to assess the feasibility of using alternative fuels in lean-premixed land-based stationary gas turbine combustion systems. Lean-premixed systems are prone to combustion instability issues and the need is to determine how the use of alternative fuels would affect such instabilities. The paper presents the results of an acoustically-forced laminar flat flame study that was conducted to measure the influence of flow velocity oscillations on the response of syngas flames under various operating conditions and in comparison to natural gas flames. The results indicate that syngas concentration in a methane-syngas-air mixture significantly affects the flame response. On the other hand, the ratio of hydrogen-to-carbon monoxide in the syngas was found to have a negligible effect on the flame response for a fuel blend made up of 50% methane and 50% syngas. Flame liftoff distance and flame speed were found to be important parameters that govern the flame transfer function magnitude and resonant frequency for a thermally stabilized flat flame. The paper also presents insights into flame transfer function scaling based on a refined Strouhal definition.

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 score0.162
Threshold uncertainty score0.303

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 head0.003
GPT teacher head0.184
Teacher spread0.181 · 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

Citations0
Published2014
Admission routes2
Has abstractyes

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