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Record W3175024409 · doi:10.1177/1475472x211023873

Influence of the liner-type treatment on the trailing-edge noise generated by a flat plate

2021· article· en· W3175024409 on OpenAlexaffabout
Gyuzel Yakhina, Bastien Dignou, Yann Pasco, Stéphane Moreau

Bibliographic record

VenueInternational Journal of Aeroacoustics · 2021
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsTrailing edgeNoise (video)PhysicsAcousticsOpticsDipoleAirfoilWind tunnelAnechoic chamberMaterials scienceScatteringDirectivityGeometryMechanicsAntenna (radio)Mathematics

Abstract

fetched live from OpenAlex

Several liner-type treatments (three different rectangular grooves covered by three different low porosity wire-mesh screens) on the trailing edge of a flat plate have been investigated in the anechoic wind-tunnel of Université de Sherbrooke. Far-field acoustic directivity measurements have been achieved at Reynolds numbers based on the plate length from [Formula: see text] to [Formula: see text], yielding radiation maps of all possible liner combinations that are then compared to the reference solid flat plate and to the plate with inserts alone. Noise from the flat plate corresponds to dipolar trailing-edge scattering with an extra shallow hump attributed to the unsteady flow recirculation behind the thick plate. When grooves are added, the latter contribution is amplified and additional cavity noise is observed with several tones and humps. The tones are shown to be resonance between high order modified Rossiter modes and cavity depthwise modes. The hump is a combination of drag dipoles and cavity monopoles from the groove row. The addition of screens always reduces the amplification of the dipolar edge scattering but exhibits very different non-linear responses for the cavity noise. The combination screen with the smallest cells and the insert with the shallowest cavities (corresponding to the same type of treatment applied previously on the Controlled-Diffusion airfoil) yields the lowest levels overall, while the screen with intermediate cell size almost always triggers noise amplification and the screen with a coarse mesh has an intermediate behavior. At high frequencies, the previously reported roughness noise is also 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 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)0.000
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
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
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)0.0010.000

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 head0.011
GPT teacher head0.230
Teacher spread0.219 · 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

Citations3
Published2021
Admission routes2
Has abstractyes

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