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Record W1973839068 · doi:10.1115/gt2008-51402

Harmonic Analysis of Jet Mixing

2008· article· en· W1973839068 on OpenAlexaff
Joan Boulanger, Sean Yun, Lei‐Yong Jiang

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMechanicsCombustorCombustion chamberCombustionTurbulenceMixing (physics)Jet (fluid)PhysicsLaminar flowInstabilityControl theory (sociology)AcousticsComputer scienceChemistry

Abstract

fetched live from OpenAlex

Development of low-pollution gas turbine engines has been calling for the development on new technologies. Lean premixed combustion is one of them but tends to be accompanied by combustion instabilities. Some instabilities are caused by coupling between the combustion zone and upstream fuel/air mixing chamber. Acoustic oscillations in the mixing chamber lead to variation of mixture distribution in the combustion zone. On the other hand, the instability itself may improve the turbulent mixing that mitigates these equivalence ratio fluctuations. The goal of this study is to gain knowledge of the fundamental mechanisms of harmonically perturbed jet mixing in air. A jet is considered as a system on which a harmonic analysis is performed. The input parameter is a modulated velocity to induce perturbation. The output parameter is the whole flow field, particularly the statistics of mixture fraction distribution. The tool is a high-order compressible direct numerical simulation code. It is demonstrated that the system can be qualified as a band-pass filter. The efficiency of mixing reaches a maximum for a modulation frequency comparable to the natural mode of a laminar jet. This study suggests that the characteristic frequency of the system to improve mixing can be inferred from the investigation of the natural mode of this system and vice versa.

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 score0.066
Threshold uncertainty score0.376

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 head0.013
GPT teacher head0.204
Teacher spread0.191 · 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
Published2008
Admission routes1
Has abstractyes

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