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Record W6979730412

Acoustic Characterization of a Low-Speed Closed Loop Wind Tunnel

2024· other· en· W6979730412 on OpenAlexvenueno aff

Bibliographic record

VenueCanadian acoustics · 2024
Typeother
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsnot available
Fundersnot available
KeywordsPlenum spaceMicrophoneWind tunnelClosed loopVolume (thermodynamics)FlutterCharacterization (materials science)
DOInot available

Abstract

fetched live from OpenAlex

A low-speed closed loop wind tunnel with an open-jet test section having a maximum windspeed of 60 m/s is being modified for future aero-acoustic testing. The wind tunnel is new to Carleton University as of 2021, and no acoustic characterization tests have been completed until recently. The objective of this research is to acoustically characterize the wind tunnel before and stepwise through its acoustic modifications. Multiple tests have been made using ¼-inch microphones at different locations inside the plenum to record the existing out-of-flow background noise. The first planned wind tunnel modification is a height extension to the current plenum with dimensions 0.74×1.8×1.3 m (H×L×W) to increase the observer distance for the microphones. Once the plenum volume is increased, acoustic treatment will be applied to the interior of the plenum to create a hemi-anechoic measurement environment. An in-flow microphone mount was designed and manufactured with computer controlled vertical traversing; it will be installed after the plenum modifications are complete.

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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.006
GPT teacher head0.187
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 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

Citations0
Published2024
Admission routes1
Has abstractyes

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