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Record W4235394574 · doi:10.32920/ryerson.14663562

Supersonic Windtunnel Noise Attenuation

2021· preprint· en· W4235394574 on OpenAlexaboutno aff
William Wai Lim Wong

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsnot available
Fundersnot available
KeywordsWind tunnelAcousticsSilencerAerodynamicsSupersonic wind tunnelNoise (video)Sound pressureHypersonic wind tunnelSupersonic speedAcoustic attenuationSound powerTransonicAeroacousticsShock waveAttenuationNozzlePhysicsEngineeringSound (geography)Computer scienceAerospace engineeringOpticsMechanicsInlet

Abstract

fetched live from OpenAlex

The aerodynamic generated noise in the supersonic wind tunnel during operation at Ryerson University has exceeded the threshold of hearing damage. An acoustic silencer was to be designed and added to the wind tunnel to reduce the noise level. The main sources of noise generated from the wind tunnel with the silencer were identified to be located at the convergent divergent nozzle and the turbulent region downstream of the shock wave at the diffuser with the maximum acoustic power level of the entire wind tunnel at 161.09 dB. The designed silencer provided an overall sound pressure level reduction of 21.41 db which was considered as acceptable. Refinement to the mesh size and changes to the geometry of the mixing chamber was suggested for a more accurate result in noise output as well as flow conditions would match up to the physical flow. Additional acoustic treatment should be applied to the wind tunnel to further reduce sound pressure level since the noise level still exceeded the threshold of hearing loss.

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)0.000
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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

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 science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.012
GPT teacher head0.209
Teacher spread0.198 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

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Same topicAerodynamics and Acoustics in Jet FlowsFrench-language works237,207