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Record W4407981366 · doi:10.1016/j.csite.2025.105914

Study on thermoacoustic instabilities in an aircraft engine combustor using 1D network model

2025· article· en· W4407981366 on OpenAlexaff
Juchan Son, Seungchai Jung, Shaun Kim, Daesik Kim

Bibliographic record

VenueCase Studies in Thermal Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsUniversity of Ottawa
FundersKorea Institute of Energy Technology Evaluation and PlanningMinistry of Trade, Industry and Energy
KeywordsCombustorThermoacousticsMechanicsAerospace engineeringAcousticsMaterials scienceEnvironmental sciencePhysicsCombustionEngineeringChemistry

Abstract

fetched live from OpenAlex

The purpose of this study was to evaluate the combustion instability characteristics of target aircraft combustors under development using network thermoacoustic (TA) models. The eigenfrequencies of the aeroengine combustor and their mode shapes were calculated using the TA model, and the growth rates of the acoustic resonances were interpreted from a feedback analysis combined with the combustion process. Two different forms of the flame transfer function (FTF) were considered to reflect the flame responses to the incoming flow fluctuations in feedback coupling. To derive the time delay between the velocity fluctuations from the nozzle to the flame surface, a steady-state computational fluid dynamics (CFD) calculation was performed under actual combustor operating conditions. The acoustic analysis results using the current 1D network model showed that both the eigenfrequency and mode distribution of each resonance were reasonably predicted by comparing it with the 3D Helmholtz calculation results. From the feedback instability analysis, it was found that both the frequency and growth rates of the instabilities were significantly affected by the change in gain and time delay of the FTF.

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 categoriesMeta-epidemiology (narrow)
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 score0.011
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.042
GPT teacher head0.302
Teacher spread0.260 · 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.

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
Published2025
Admission routes1
Has abstractyes

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