MétaCan
Menu
Back to cohort
Record W4389541103 · doi:10.17118/11143/20855

Investigating the noise sources of the transonic RAE 2822 airfoil

2023· article· en· W4389541103 on OpenAlexafffund
Antonio Alguacil Cabrerizo, Lorenzo Becherucci, Marlène Sanjosé, Stéphane Moreau

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsÉcole de Technologie SupérieureUniversité de Sherbrooke
FundersAlliance de recherche numérique du Canada
KeywordsAirfoilTransonicNoise (video)Computer scienceAcousticsAerospace engineeringPhysicsArtificial intelligenceEngineeringAerodynamics

Abstract

fetched live from OpenAlex

A compressible Large Eddy Simulation is performed on the transonic RAE 2822 airfoil, and compared to the baseline simulation of Koch et al. (28th AIAA/CEAS Aeroacoustics Conference, paper AIAA 2022-2816) in order to highlight the main noise source mechanisms.The new simulation employs a new mesh which eliminates a jump in the airfoil surface mesh, located in the supersonic laminar boundary layer region of the suction side.This jump induced some hydrodynamic instability in the suction side boundary layer of the baseline simulation, potentially emitting noise at high-frequencies.The new results show that these instabilities are significantly damped when employing the new refined mesh and are consequently very sensitive to the grid quality.Nonetheless, the acoustic response of the airfoil, calculated using the Ffowcs Williams and Hawkings analogy in its solid formulation, remains similar to the baseline, with a highfrequency hump appearing between 30 and 40 kHz.This shows that this hump is not caused by the hydrodynamic instabilities, therefore confirming the grid independence of the acoustic results.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.010
GPT teacher head0.196
Teacher spread0.186 · 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 designObservational
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

Citations0
Published2023
Admission routes2
Has abstractyes

Explore more

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