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Record W3034671848 · doi:10.2514/6.2020-2607

Large Eddy Simulation of an Outflow butterfly valve

2020· article· en· W3034671848 on OpenAlexaff
Safouane Tebib, Marlène Sanjosé, Chaofan Zhang, Stéphane Moreau, Martin Brouillette

Bibliographic record

VenueAIAA AVIATION 2020 FORUM · 2020
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversité du QuébecUniversité de Sherbrooke
Fundersnot available
KeywordsButterfly valveAerodynamicsAcousticsNoise (video)SchlierenTransonicLarge eddy simulationWind tunnelAerospace engineeringComputer scienceMechanicsPhysicsEngineeringMechanical engineeringTurbulence

Abstract

fetched live from OpenAlex

The present study focuses on the aeroacoustic aspects of an outflow butterfly valve that is used to maintain the pressure inside an airplane cabin. When flying at 17,000 ft, an annoying tonal noise has been reported and the flowwithin the valve is transonic. Large Eddy Simulations are performed on different setups in order to understand the noise mechanisms and results are compared with a 2D-rectangular channel butterfly valve representative of the actual geometry. The experimental valve allows performingwall-pressure measurements, Schlieren visualization and far-field noise investigation. Initial LES simulations with second order precision on a relatively coarse mesh have shown difficulties to reproduce the experimental results. Therefore, improvements are made in terms of configuration and numerical scheme leading to the two numerical setups investigated in this paper. Different aerodynamic behaviors, mostly in the shock/boundary layer interaction region, are observed between the two configurations with 0.1D and 0.3D span width. As for the acoustics, far-field computations with FW-H analogy will be compared with experiments for both cases.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.008
GPT teacher head0.226
Teacher spread0.219 · 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 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

Citations1
Published2020
Admission routes1
Has abstractyes

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Same venueAIAA AVIATION 2020 FORUMSame topicAerodynamics and Acoustics in Jet FlowsFrench-language works237,207