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Record W3034589746 · doi:10.2514/6.2020-2539

Aerodynamic and acoustic investigation of the liner-type porous treatment for the trailing-edge of the flat plate.

2020· article· en· W3034589746 on OpenAlexaff
Gyuzel Yakhina, Bastien Dignou, Stéphane Moreau

Bibliographic record

VenueAIAA AVIATION 2020 FORUM · 2020
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsTrailing edgeAirfoilAcousticsMaterials scienceParticle image velocimetryBoundary layerDirectivityNoise (video)AerodynamicsMechanicsPhysicsComposite materialEngineeringTurbulenceComputer science

Abstract

fetched live from OpenAlex

An investigation of a flat plate with the liner-type (cavities covered by mesh) trailing-edge treatment is addressed in the present study to understand the influence of the geometrical parameters such as the cavity depth and the wire-mesh cell size on the noise generation mechanism. Previously, this noise control method showed high effectiveness during the test with a Controlled-Diffusion airfoil. The directivity measurements distinguish one configuration with shallow cavity and coarse wire-mesh which have the best noise performance. However, this configuration produces more noise at high frequencies than the configuration without mesh, due to the amplification of the cavity noise. The analysis of a Particle Image Velocimetry results along the treated area shows an increase of the boundary layer thickness and root-mean-square velocity fluctuations due to the liner-type treatment. The penetration of the flow into the treatment has been evidenced as well.

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)0.000
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
Metaresearch0.0000.000
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)0.0010.000

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 head0.013
GPT teacher head0.209
Teacher spread0.195 · 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

Citations1
Published2020
Admission routes1
Has abstractyes

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Same venueAIAA AVIATION 2020 FORUMSame topicAerodynamics and Acoustics in Jet FlowsFrench-language works237,207