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Record W1997558015 · doi:10.1121/1.4904489

Subharmonic tonal noise from backflow vortices radiated by a low-speed ring fan in uniform inlet flow

2015· article· en· W1997558015 on OpenAlexafffund
Stéphan Magne, Stéphane Moreau, Alain Berry

Bibliographic record

VenueThe Journal of the Acoustical Society of America · 2015
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversité de Sherbrooke
FundersNatural Sciences and Engineering Research Council of CanadaCompute Canada
KeywordsAcousticsAeroacousticsNoise (video)PhysicsRotor (electric)InletReynolds-averaged Navier–Stokes equationsVortexBackflowTurbofanFrequency domainMechanicsFlow (mathematics)TurbulenceComputer scienceEngineeringMathematicsAerospace engineeringSound pressureMathematical analysisMechanical engineering

Abstract

fetched live from OpenAlex

In order to highlight the mechanisms responsible for subharmonic tonal noise, a complete aeroacoustic study of a ring fan in presence of a uniform inlet flow is conducted. Unsteady RANS simulations with a compressible flow solver are used to compute the flow field and identify the acoustic sources on the rotor. The tip clearance recirculation shows upstream vortices that impinge the rotor blades and create the main source of unsteadiness on the fan. Since these vortices rotate at a lower speed than the rotor, the frequency of the impact is lower than the blade passing frequency. The acoustic signature is computed by propagating the noise sources located on the rotor surfaces using two methods: A Ffowcs-Williams and Hawkings analogy in the time-domain and an analytical model in the frequency-domain based on the compact rotating dipole formulation. A comparison with experimental results confirms that the aeroacoustic phenomena responsible for the subharmonic tonal noise are well captured and properly propagated by the acoustic codes.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

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 studies0.0000.001
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 head0.009
GPT teacher head0.215
Teacher spread0.206 · 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

Citations62
Published2015
Admission routes2
Has abstractyes

Explore more

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