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Record W4282542916 · doi:10.2514/6.2022-2816

Acoustic Investigation of the Transonic RAE 2822 Airfoil with Large-Eddy Simulation

2022· article· en· W4282542916 on OpenAlexaff
Régis Koch, Marlène Sanjosé, Stéphane Moreau

Bibliographic record

Venue28th AIAA/CEAS Aeroacoustics 2022 Conference · 2022
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsÉcole de Technologie SupérieureUniversité de Sherbrooke
Fundersnot available
KeywordsAirfoilAcousticsMach numberPhysicsTrailing edgeTransonicChord (peer-to-peer)Leading edgeRelative windNoise (video)MechanicsAngle of attackAerospace engineeringAerodynamicsComputer scienceEngineering

Abstract

fetched live from OpenAlex

An acoustic investigation is achieved on the transonic RAE 2822 airfoil in order to highlight the main noise sources and to compare the porous surface and the solid surface formulations of the Ffowcs Williams and Hawkings analogy. A 50 mm slice of the airfoil is computed using compressible large-eddy simulation. The airfoil chord is 0.3048 m, the inflow Reynolds number based on chord is 6.5e6, the inflow Mach number is 0.725, and the angle of attack is 2.91°. Simulations are compared with experimental data and show very good agreement. A strong shock is located on the airfoil suction side and is responsible for acoustic radiation in the far-field in addition to the airfoil self noise (trailing-edge noise). Yet, the solid surface formulation is shown to be sufficient to capture most of the acoustic signature in the far-field, which indicates that quadrupole noise sources related to the shock have a smaller contribution compared to the dipole noise source mainly located at the trailing edge. The latter is confirmed by additional predictions based on Amiet’s moel. This work can be used as an acoustic benchmark for future numerical investigations on the RAE 2822 airfoil since it is the first compressible large-eddy simulation for acoustic investigation achieved on this configuration.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.519
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.009
GPT teacher head0.192
Teacher spread0.182 · 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.

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

Citations4
Published2022
Admission routes1
Has abstractyes

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