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Record W2019952258 · doi:10.2514/2.1798

Acoustic Noise-Source Identification in Aircraft-Based Atmospheric Temperature Measurements

2002· article· en· W2019952258 on OpenAlexaff
Ronald J. Hugo, Scott R. Nowlin, Ila L. Hahn, Frank D. Eaton, Kim McCrae

Bibliographic record

VenueAIAA Journal · 2002
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversity of Calgary
FundersAir Force Research Laboratory
KeywordsAcousticsEnvironmental scienceNoise (video)Aircraft noiseAerospace engineeringAeroacousticsIdentification (biology)MeteorologyRemote sensingAtmospheric sciencesPhysicsSound pressureComputer scienceGeologyNoise reductionEngineering

Abstract

fetched live from OpenAlex

Atmospheric temperature fluctuation data collected from a Grumman Gulfstream II aircraft show features in temperature power spectral density functions that do not follow the expected - 5/3 slope for homogeneous isotropic turbulence. Spectral analysis techniques show that these features result from the upstream propagation of a nondispersive acoustical wave. The source of the acoustical wave, which appears only at flight altitudes greater than 28,000 ft (8530 m), is attributed to engine acoustics in the form of jet screech where vortical structures interact with a quasi-periodic shock cell structure, both in the jet exhaust. The nature of the acoustical disturbance is shown to be dependent on velocity, with increased velocity resulting in a decrease in jet-screech peak frequency. These results are found to be consistent with those of other researchers investigating jet screech in jet flows.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.013
GPT teacher head0.200
Teacher spread0.187 · 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 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

Citations7
Published2002
Admission routes1
Has abstractyes

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