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Record W1999960330 · doi:10.2514/6.2012-2060

On the generation of airfoil tonal noise at zero angle of attack and low to moderate Reynolds number

2012· article· en· W1999960330 on OpenAlexfundno aff
Elias Arcondoulis, Con J. Doolan, Laura A. Brooks, Anthony C. Zander

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsnot available
FundersIndependent Electricity System Operator
KeywordsAirfoilReynolds numberAngle of attackZero (linguistics)Noise (video)AcousticsPhysicsComputer scienceMechanicsAerodynamicsTurbulenceArtificial intelligence

Abstract

fetched live from OpenAlex

The generation of tones from a NACA 0012 airfoil at zero angle of attack and Reynolds numbers of 50,000 to 150,000 was investigated using acoustic beamforming and surface flow visualization techniques in an anechoic wind tunnel. Boundary layer separation lengths were obtained using surface flow visualization techniques and XFOIL software and were input into a feedback loop equation to determine if the measured tones were generated by a feedback mechanism. Using the maximum point of velocity on the airfoil surface as a feedback length provided the best agreement with the measured tones. Boundary layer tripping on one airfoil surface resulted in a decrease in the primary tone amplitude and some of the secondary tone amplitudes. Applying a trip to both surfaces resulted in a significant noise decrease and no tones were observed.

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 score0.173
Threshold uncertainty score0.219

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 head0.021
GPT teacher head0.233
Teacher spread0.213 · 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

Citations11
Published2012
Admission routes1
Has abstractyes

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