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Record W4360895815 · doi:10.1299/jsmemecj.2022.j102-04

Measurement of acoustic resistance of resonator orifice influenced by grazing flow, sound pressure and orifice corners' shape

2022· article· en· W4360895815 on OpenAlexaff
淳大 本勝, 崇靖 鳥越, 史人 仲野, 優駿 河野, 優花 岩木, 正治 西村, 唱 中井, 隆 松野, 知伸 後藤

Bibliographic record

VenueThe Proceedings of Mechanical Engineering Congress Japan · 2022
Typearticle
Languageen
FieldEngineering
TopicFlow Measurement and Analysis
Canadian institutionsL'Alliance Boviteq
Fundersnot available
KeywordsBody orificeStrouhal numberAcousticsSound pressureDuct (anatomy)Vortex sheddingResonatorMaterials scienceFlow velocityParticle velocityPhysicsOpticsFlow (mathematics)MechanicsEngineeringAnatomyReynolds number

Abstract

fetched live from OpenAlex

Acoustic resistance of the rectangular orifice of a resonator attached on a duct was measured. Four types of resonator orifices with different corner shapes were used. To express acoustic resistance as a linear sum of bias flow velocity, grazing flow velocity, and particle velocity in the neck of the resonator, we classified the measured resistance data in two regions dominated by sound pressure and grazing flow velocity, respectively. The acoustic resistance is approximated by the larger value of the linear sum in good agreement with experimental results. The dependence of the acoustic resistance on the Strouhal number was also examined. The variation in acoustic resistance due to vortex shedding at similar orifice corner shape shown by Moers was clearly reproduced.

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)0.003
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 score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.009
GPT teacher head0.187
Teacher spread0.178 · 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
Published2022
Admission routes1
Has abstractyes

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