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Record W2996498632

Development and Testing of an Aeroacoustic Wind Tunnel Test Section

2019· article· en· W2996498632 on OpenAlexaffvenue
Basim Al Tlua, Joana Rocha

Bibliographic record

VenueCanadian acoustics · 2019
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsCarleton University
Fundersnot available
KeywordsWind tunnelAnechoic chamberAirfoilTrailing edgeEngineeringAeroacousticsAcousticsNoise (video)Hypersonic wind tunnelStructural engineeringAirplaneSubsonic and transonic wind tunnelMarine engineeringAerodynamicsAerospace engineeringTransonicSound pressurePhysicsComputer science
DOInot available

Abstract

fetched live from OpenAlex

A new aeroacoustic wind tunnel test section is developed and tested at Carleton University. The aeroacoustic test section is fitted with two anechoic chambers on the two side walls. Side walls of the test section are lined with acoustic transparency tensioned cloth screens, which act as an interface between the test section and the anechoic chambers to provide a smooth flow surface while eliminating the need for a jet catcher and reducing interference effects. The test section is tested in the medium speed closed-loop wind tunnel at Carleton University. The design layout of the wind tunnel and design treatments to improve acoustic performance are discussed. Experiments are conducted to verify the acoustic performance of the developed aeroacoustic wind tunnel test section. It is found that background noise is comparable with other existing aeroacoustic wind tunnel facilities. The trailing edge noise of a NACA0012 airfoil model is also measured as a benchmark test. Results reveal that noise radiated from the airfoil trailing edge model are adequately higher than the background noise for a wide frequency range, and also that a sawtooth serrated trailing edge is effective for reducing noise compared to a straight trailing edge.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.292
Threshold uncertainty score0.777

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 head0.009
GPT teacher head0.178
Teacher spread0.168 · 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

Citations6
Published2019
Admission routes2
Has abstractyes

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