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Record W2997025997 · doi:10.22215/etd/2013-07167

Effect of fuel composition on the response of an acoustically forced flat flame

2013· dissertation· en· W2997025997 on OpenAlexaff
J Górski

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsCarleton University
Fundersnot available
KeywordsCombustorCombustionSyngasMethanePremixed flameLaminar flame speedDiffusion flameStrouhal numberFlame speedFuel gasNuclear engineeringMechanicsEngineeringChemistryHydrogenPhysics

Abstract

fetched live from OpenAlex

Interest in alternative fuels for power generation is growing, yet these fuels bring new challenges to gas turbine design and operation. Among these challenges are combustor operability issues, highlighted by problems with combustion instabilities. For this thesis, a fundamental study of the effects of fuel composition on combustion dynamics was undertaken. An acoustically forced flat flame burner was constructed, allowing measurement of the flame transfer function (FTF) relating acoustic perturbations to heat release rate fluctuations in the flame. Tests were done using methane, along with simulated syngas and biogas fuel mixtures over a variety of operating conditions. Large variations in methane concentration had a significant impact on the FTF, while variations in the hydrogen to carbon monoxide ratio did not impact the FTF in fuel mixtures of equal parts methane and syngas. The Strouhal number was found to be an important parameter in predicting phase response independent of the fuel type. Flame liftoff distance and fuel composition were the key parameters determining the peak FTF magnitude. A hypothesis on the role of the non-adiabatic nature of the flat flame and thermal-diffusive effects on the trends in peak FTF magnitude is presented and discussed.

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 score0.754
Threshold uncertainty score0.548

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 head0.004
GPT teacher head0.228
Teacher spread0.225 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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