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Record W4403058476 · doi:10.33737/gpps24-tc-194

Broadband interaction noise predictions for an axial compressor stator

2024· article· en· W4403058476 on OpenAlexafffund
Antonio Alguacil, Stefanie Lohse, Niklas Maroldt, Joerg R. Seume, Stéphane Moreau

Bibliographic record

VenueProceedings · 2024
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversité de Sherbrooke
FundersAlliance de recherche numérique du CanadaNatural Sciences and Engineering Research Council of CanadaMitacsLeibniz-GemeinschaftDeutsche Forschungsgemeinschaft
KeywordsBroadbandGas compressorStatorAcousticsNoise (video)Computer sciencePhysicsElectrical engineeringTelecommunicationsEngineeringArtificial intelligence

Abstract

fetched live from OpenAlex

The broadband interaction noise of the axial compressor stator-only configuration in the Aeroacoustic Wind Tunnel (AWT) at the Leibniz University Hannover is investigated in this work. Posson’s analytical model, which uses a threedimensional rectilinear cascade response model coupled with an in-duct acoustic analogy for the subsequent acoustic propagation, is employed to estimate the noise from impinging gusts. First, a model validation with experimental results of NASA’s Source Diagnostic Test (SDT) AIAA benchmark is presented. For the subsequent compressor stator investigations, the model inputs are derived from numerical pre-tests using steady RANS simulations. Assuming homogeneous isotropic turbulence, two different models by Liepmann and Von Kármán are applied to calculate the velocity turbulence spectrum and radial correlation length. Finally, the acoustic results are evaluated by comparing the sound power spectra upstream and downstream of the compressor stator vane. The resulting differences can be related to variations in axial velocities and integral turbulent length scale for all four operating conditions. The obtained broadband interaction noise estimations of the test rig serve as a baseline for following experimental noise measurements.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.902
Threshold uncertainty score0.455

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 communication0.0000.001
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 head0.031
GPT teacher head0.299
Teacher spread0.268 · 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

Citations0
Published2024
Admission routes2
Has abstractyes

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