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Record W2904858792 · doi:10.2514/1.c035113

Design and Characterization of a High-Speed Subsonic Aeroacoustic Wind Tunnel

2018· article· en· W2904858792 on OpenAlexafffundabout
Frank Giardino, Joana Rocha

Bibliographic record

VenueJournal of Aircraft · 2018
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsCarleton University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWind tunnelAerospace engineeringAerodynamicsSubsonic and transonic wind tunnelCharacterization (materials science)AcousticsMarine engineeringEngineeringStructural engineeringGeologyTransonicPhysics

Abstract

fetched live from OpenAlex

The high-speed aeroacoustic wind tunnel at Carleton University is a new facility commissioned with the purpose of facilitating experimental studies of turbulent boundary-layer (TBL)-induced surface pressure fluctuations. This research is primarily intended for applications related to aircraft cabin noise generation from structures exposed to high-speed flow. This open-jet blowdown wind tunnel is a unique facility in Canada and one of a few aeroacoustic wind tunnels in the world capable of achieving speeds up to Mach 0.8. Flow is delivered from a nozzle with dimensions of 6.1 by 15 cm to a test section enclosed within an anechoic chamber with internal dimensions of 1.9 by 0.88 by 1.95 m. This study details the complete design methodology for all major wind-tunnel components, including the numerical simulations performed in the validation of the designed components. The results of the test section background noise levels and TBL surface pressure fluctuation spectral behavior developed over a flat test section plate are compared with established data and empirical models available in the literature.

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)0.001
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 score0.001
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
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 head0.009
GPT teacher head0.200
Teacher spread0.191 · 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

Citations7
Published2018
Admission routes3
Has abstractyes

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