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Record W4406999635 · doi:10.1016/j.jsv.2025.118978

Installation effects on airfoil self-noise estimated by direct numerical simulations

2025· article· en· W4406999635 on OpenAlexafffund
Ziyang Zhou, Stéphane Moreau, Marlène Sanjosé

Bibliographic record

VenueJournal of Sound and Vibration · 2025
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsÉcole de Technologie SupérieureUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAirfoilNoise (video)AcousticsDirect numerical simulationMechanicsPhysicsEnvironmental scienceAerospace engineeringMarine engineeringEngineeringComputer scienceReynolds numberTurbulence

Abstract

fetched live from OpenAlex

Several Direct Numerical Simulations (DNS) have been achieved using the Lattice-Boltzmann Method (LBM) on a Controlled-Diffusion (CD) airfoil at R e c = 1 . 5 × 1 0 5 and α = 8 ° to evaluate installation effects on airfoil self-noise. 2D DNS provides unrealistic airfoil noise sources and a significant overprediction of the far-field noise with possibly a change in the ranking of the noise sources. Conversely, all 3D simulations of the airfoil immersed in the wind tunnel jet show excellent agreement with the flow around the airfoil and the far-field noise. Two new 3D DNS with some shear-layer refinement and possibly an additional zigzag trip to trigger turbulence have better resolved the jet development. Even though all 3D DNS identify two main noise sources, the laminar separation bubble (LSB) at the leading edge and the turbulent eddies at the trailing edge , their intensity varies because of a strong coupling between the jet and the airfoil that yields different LSB sizes, increased inlet turbulent intensity, oscillations of the angle-of-attack, and variations in the noise sources. The state of the jet shear layer at the nozzle exit of a given open-jet anechoic wind tunnel is seen to influence the flow field around the airfoil and its noise radiation, with potentially a larger jet contribution.

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 score0.244
Threshold uncertainty score0.276

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 head0.005
GPT teacher head0.233
Teacher spread0.228 · 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

Citations8
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Sound and VibrationSame topicAerodynamics and Acoustics in Jet FlowsFrench-language works237,207