MétaCan
Menu
Back to cohort
Record W4200484023 · doi:10.1177/0309524x211060548

Experimental investigation of NACA-0012 airfoil instability noise with sawtooth trailing edges

2021· article· en· W4200484023 on OpenAlexaff
Basim Al Tlua, Joana Rocha

Bibliographic record

VenueWind Engineering · 2021
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsCarleton University
Fundersnot available
KeywordsSawtooth waveAirfoilNACA airfoilTrailing edgePhysicsNoise (video)SerrationAcousticsAngle of attackInstabilityReynolds numberMechanicsOpticsMaterials scienceAerodynamicsTurbulenceEngineeringComputer scienceTelecommunications

Abstract

fetched live from OpenAlex

This study presents an experimental study of the effect of sawtooth trailing-edge serrations on airfoil instability noise. The far-field noise measurements are obtained to investigate the noise radiation characteristics of a NACA-0012 airfoil operated at various angles of attack: 0°, 5°, and 10°, and covered Reynolds numbers of 2.87 × 10 5 , 3.71 × 10 5 , and 5 × 10 5 . It is found that as the Reynolds number increases, the instability noise shifts from tonal to broadband, whereas as the angle of attack increases, it shifts from broadband to tonal. Furthermore, sawtooth trailing-edges are used to minimize instability tonal noise, leading to considerable self-noise reduction. Parametric studies of the serration amplitude 2 h and streamwise wavelength λ are performed to understand the effect of sawtooth trailing-edges on noise reduction. It is observed that the sound pressure reduction level is sensitive to both the amplitude and streamwise wavelength. Overall, the sawtooth trailing-edge with larger amplitude and smaller wavelength produce the greatest amount of noise reduction.

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.008
GPT teacher head0.181
Teacher spread0.174 · 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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

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