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Record W2333464096 · doi:10.2514/6.2011-3900

Wake model effects on the prediction of turbulence-interaction broadband noise in a realistic compressor stage

2011· article· en· W2333464096 on OpenAlexafffund
Laurent Soulat, Stéphane Moreau, H. Posson

Bibliographic record

Venue41st AIAA Fluid Dynamics Conference and Exhibit · 2011
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversité de Sherbrooke
FundersCompute Canada
KeywordsWakeGas compressorStage (stratigraphy)TurbulenceNoise (video)BroadbandAcousticsPhysicsWake turbulenceComputer scienceAerospace engineeringMechanicsEngineeringGeologyOptics

Abstract

fetched live from OpenAlex

The effects of the wake modelling on the prediction of the broadband noise generated by the impingement of the turbulent wakes on a stationary blade row are studied. The analysis focuses on the description of the wake shape that is usually approximated by a Gaussian curve for acoustical purposes. The prediction of the broadband noise is achieved using an analytical acoustic model dedicated to turbomachinery configurations. The characteristics of the model regarding the wake treatment are described. In order to provide a realistic reference for the wake shape, a low-speed axial compressor stage is analyzed using numerical RANS simulation. The unsteady flow-field structure is presented. The simulation is validated using a comparison with experimental data, with special attention paid for the wake numerical prediction. The shape of the rotor wakes is thoroughly described. The comparison between the simulated wake and the corresponding Gaussian approximation is detailed, showing differences between the two approaches. The broadband predictions yielded by the two wake models are finally compared. The use of the Gaussian approximation of the wakes shape for broadband noise prediction purposes is validated in the studied realistic 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 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 score0.191
Threshold uncertainty score0.716

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 head0.034
GPT teacher head0.215
Teacher spread0.181 · 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

Citations10
Published2011
Admission routes2
Has abstractyes

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