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Record W2328550806 · doi:10.2514/6.2013-2233

On the sources of jet noise: a numerical study using band-pass Filtering

2013· article· en· W2328550806 on OpenAlexafffund
Alireza Najafi­-Yazdi, Ali Uzun, Luc Mongeau

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsMcGill University
FundersCompute CanadaPratt and Whitney Canada
KeywordsJet noiseNoise (video)AcousticsJet (fluid)Computer scienceBand-pass filterPhysicsElectronic engineeringEngineeringMechanicsOpticsArtificial intelligence

Abstract

fetched live from OpenAlex

Bandpass filtering of the flow and sound was performed in order to gain further insight into the role of coherent structures in subsonic jet noise generation. The bandpass filtering procedure was conducted on the data obtained from a well-resolved large eddy simulation of sound radiation from an unheated, Mach 0.9 jet at ReD = 400,000. Results from the bandpass filtering of the pressure field suggest two dominant mechanisms of sound radiation in unheated subsonic jets that can occur in all scales of turbulence. The first mechanism is the stretching and the distortion of coherent vortical structures. For large scale structures, i.e. low frequency radiation, this mechanism is dominant close to the termination of the potential core. This mechanism appears quadrupolar in nature, and is responsible for strong sound radiation at aft angles. The second sound generation mechanism appears to be the transverse vibration of the shear-layer interface within the ambient quiescent flow. This mechanism is believed to be responsible for sound radiation along the sideline directions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.024
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.221
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), 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
Published2013
Admission routes2
Has abstractyes

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Same topicAerodynamics and Acoustics in Jet FlowsFrench-language works237,207