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Record W4413768421 · doi:10.1016/j.jsv.2025.119376

Influence of angle of attack on airfoil tip noise

2025· article· en· W4413768421 on OpenAlexafffundabout
Guang C. Deng, Satoshi Baba, Philippe Lavoie, Stéphane Moreau

Bibliographic record

VenueJournal of Sound and Vibration · 2025
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversity of TorontoUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaOntario Research FoundationBombardier
KeywordsAirfoilAngle of attackAcousticsNoise (video)Aerospace engineeringPhysicsEngineeringComputer scienceAerodynamicsArtificial intelligence

Abstract

fetched live from OpenAlex

This study investigates the aerodynamic and aeroacoustic characteristics of airfoil tip noise for a supercritical airfoil profile at α = 5 ° and α = 10 ° using a hybrid noise computation approach. Wall-Resolved Large Eddy Simulations (WR-LES) were performed and validated against experimental data from the University of Toronto. The simulations captured a tri-vortex system (TVS) comprising primary, secondary, and tertiary vortices. At α = 10 ° , upstream shifts in the primary vortex trajectory intensified interactions with the airfoil surface, resulting in amplified surface-pressure fluctuations and increased far-field noise levels. Dynamic Mode Decomposition (DMD) pinpoints dominant acoustic sources and their radiation patterns, distinguishing between duct acoustic modes below 1500 Hz and localized sources above this frequency with clear dipole radiation patterns stemming from three noise sources: the leading-edge, trailing-edge, and side-edge noise. The far-field acoustic predictions, computed using the Ffowcs Williams–Hawkings (FW-H) analogy, showed good agreement with experimental results. Both the solid and porous FW-H formulations accurately captured noise levels, with a 3 dB increase in spectral levels observed at α = 10 ° due to enhanced aerodynamic loading and vortex dynamics. Noise decomposition showed a shift in dominant sources: trailing-edge noise prevailed at α = 5 ° , while tip noise, driven by vortex impingement and crossover, dominated at α = 10 ° , highlighting the complex aerodynamic-acoustic interplay.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.161
Threshold uncertainty score0.147

Codex and Gemma teacher scores by category

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

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 head0.244
Teacher spread0.235 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations1
Published2025
Admission routes3
Has abstractyes

Explore more

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