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Record W2754484179 · doi:10.3390/ijtpp2030016

Effect of Distortion on Turbofan Tonal Noise at Cutback with Hybrid Methods

2017· article· en· W2754484179 on OpenAlexaff
Majd Daroukh, Stéphane Moreau, Nicolas Gourdain, Jean‐François Boussuge, Claude Sensiau

Bibliographic record

VenueInternational Journal of Turbomachinery Propulsion and Power · 2017
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversité de Sherbrooke
FundersOffice National d'études et de Recherches AérospatialesSafran Aircraft EnginesGrand Équipement National De Calcul Intensif
KeywordsTurbofanNacelleInletDuct (anatomy)AcousticsDistortion (music)AeroacousticsAnnulus (botany)Noise (video)Mechanical fanMechanicsPhysicsEngineeringAerospace engineeringSound pressureComputer scienceMechanical engineeringMaterials scienceTelecommunications

Abstract

fetched live from OpenAlex

New ultra high bypass ratio architectures may significantly affect the fan tonal noise of future aircraft engines. Indeed, such a noise source is supposed to be dominated by the interaction of fan-blade wakes with outlet guide vanes. However, shorter nacelles in these engines are expected to trigger an important air-inlet distortion that can be responsible for new acoustic sources on the fan blades. Full annulus simulations based on the unsteady Reynolds-averaged Navier–Stokes equations are presently used to study this effect. Simulation results show that the air-inlet distortion has a main effect in the fan-tip region, leading to a strong variation of the fan-blade unsteady loading. It also significantly modifies the shape of the fan-blade wakes and, consequently, the unsteady loading of the outlet guide vanes. Acoustic predictions based on the extension of Goldstein’s analogy to an annular duct in a uniform axial flow are presented and show that the fan sources notably contribute to the fan tonal noise. The air-inlet distortion is responsible for an increase of the noise radiated by both the fan and the outlet guide vane sources, leading to a global noise penalty of up to three decibels.

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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.612
Threshold uncertainty score0.405

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 head0.004
GPT teacher head0.293
Teacher spread0.288 · 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 designOther design
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

Citations12
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Turbomachinery Propulsion and PowerSame topicAerodynamics and Acoustics in Jet FlowsFrench-language works237,207