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Record W2188871051 · doi:10.12762/2014.al07-05

Combustion Noise in Modern Aero-Engines

2014· preprint· en· W2188871051 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueHAL (Le Centre pour la Communication Scientifique Directe) · 2014
Typepreprint
Languageen
FieldEngineering
TopicCombustion and flame dynamics
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsCombustionNoise (video)Environmental scienceAerospace engineeringAeronauticsAcousticsComputer scienceEngineeringPhysicsChemistryArtificial intelligence

Abstract

fetched live from OpenAlex

Combustion noise has recently been the subject of attention of both the aeroacoustic and the combustion research communities. Over the last decades, engine manufacturers have made important efforts to significantly reduce fan and jet noise, which increased the relative importance of combustion noise. Two main mechanisms of combustion-noise generation have been identified: direct combustion noise, generated by acoustic waves propagating to the outlet, and indirect combustion noise, caused by the acceleration of entropy waves (or hot spots) and vorticity waves through turbine blades. The purpose of this paper is to describe some of the predicting tools used in combustion noise, as well as to present an overview on some recent experimental studies.

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.

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)0.002
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: none
Teacher disagreement score0.647
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
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 science0.0010.001
Research integrity0.0000.001
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 head0.009
GPT teacher head0.201
Teacher spread0.192 · 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