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Record W1987943215 · doi:10.1115/gt2008-50454

Optimizing Mixing for Maximum Damping of Fuel-Air Ratio Oscillations in Gas Turbine Premixers

2008· article· en· W1987943215 on OpenAlexaff
Wajid A. Chishty, Ibrahim Yimer

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMechanicsStrouhal numberCombustorAmplitudePhysicsDamping ratioRange (aeronautics)Pressure dropCoaxialMaterials scienceControl theory (sociology)ThermodynamicsAcousticsVibrationTurbulenceCombustionOpticsReynolds numberEngineeringChemistry

Abstract

fetched live from OpenAlex

An analytical model based on advection-diffusion volume is analyzed with the objective to investigate the limits of achievable damping in fuel-air ratio oscillations over the range of frequencies at which combustor thermoacoustic instabilities are normally encountered. Results show that there exists an optimum degree of diffusion that will allow maximum damping in fuel-air ratio fluctuations. The upper bound on damping is found to be dependent on a constant value of a new non-dimensional number, defined as a ratio between Peclet Number and Strouhal Number. The analysis presented here is considered useful to evaluate the extent of damping that is inherently obtainable in practical premixers. Also presented are the results of experimental investigations, which were conducted to verify the analytically predicted behavior of premixers. The measurements were performed over a selected range of frequencies using a premixer configuration with square cross section and with fuel jet issuing in a co-flowing bulk air stream. The effects of modulation amplitude and frequency, and pressure drop across the premixer on the damping effectiveness of the premixer were examined. Results show that for the test conditions considered, the modulation amplitude has a profound effect on the premixer performance, whereas pressure drop effects are insignificant.

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: none
Teacher disagreement score0.680
Threshold uncertainty score0.397

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 head0.017
GPT teacher head0.219
Teacher spread0.203 · 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

Citations1
Published2008
Admission routes1
Has abstractyes

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