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Record W2083537392 · doi:10.1115/imece2009-12595

Experimental and Numerical Investigation of Spatial and Temporal Dispersion of Forced Fuel Oscillations

2009· article· en· W2083537392 on OpenAlexafffund
Wajid A. Chishty, Joan Boulanger, Sean Yun

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsNational Research Council Canada
FundersNational Research Council Canada
KeywordsCombustorMechanicsDispersion (optics)CombustionMixing (physics)Flow (mathematics)Oscillation (cell signaling)AmplitudeEnvironmental scienceVolumetric flow rateAdvectionTurbineMaterials sciencePhysicsOpticsThermodynamicsChemistry

Abstract

fetched live from OpenAlex

In lean premixed combustors of gas turbine engines, fuel-air mixing is considered vital for controlling pollutant emissions as well as combustion instability. Enhancement in mixing may be obtained by modulating the fuel flow rate. Modulation of fuel flow is also a useful technique to actively control combustion instabilities arising from the pressure oscillations in the combustor and thrust augmenters. Effectiveness of the forced oscillations depends on the level of dispersion present in the system. Knowledge of dispersion levels is also important in determining the degree of mixing and therefore, the effectiveness of a premixer. This paper presents the experimental efforts undertaken to study the spatial and temporal dispersion of fuel flow rate oscillation introduced at the premixer inlet. Effects of oscillation amplitude and frequency are investigated at different bulk flow rates and at various locations in the premixer. Also presented is a review of the in-house numerical work done towards this end, using three computational methods. Results show that the degree of dispersion in fuel flow rate oscillations depends on modulation amplitude and frequency as well as advective velocity of the bulk flow.

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 score0.823
Threshold uncertainty score0.156

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 head0.008
GPT teacher head0.214
Teacher spread0.206 · 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
Published2009
Admission routes2
Has abstractyes

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