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Record W2765111059 · doi:10.1115/pvp2017-65405

Flow-Induced Noise of Perforated Plates at Oblique Angles of Incidence

2017· article· en· W2765111059 on OpenAlexaff
Paul Vanoostveen, Samir Ziada

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Vibration Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsVortexParticle image velocimetryAcousticsOpticsMicrophoneFlow (mathematics)Noise (video)MechanicsPhysicsLoudspeakerTurbulence

Abstract

fetched live from OpenAlex

This paper investigates experimentally the mechanism of tone generation from flow over a simplified model of a perforated plate. To simplify the geometry to two dimensions, a perforated plate is modeled by a series of rectangular slats with an adjustable gap width between them. This apparatus is tested at various angles of incidence and flow velocities, to identify the conditions favorable to the production of tonal noise. The results of this research are presented in two main parts. First, the acoustic response of the test plates is documented by means of microphone measurements. It is found that for an angle of incidence between 5 and 30 degrees and a flow velocity of 10 to 30 m/s, tonal noise is produced. Outside of this range of angles, the produced sound is broadband. In the second part, phase-locked particle image velocimetry (PIV) is used to study the flow field. It is found that vortices form in the free shear layer of the gaps between the slats. These vortices impinge on the side of the downstream slat and are then ejected through the gap to the backside of the plate. As these vortices leave the edge of the downstream slat, counter rotating vortices are shed in sympathy with the incident vortices. Vortex pairs are therefore periodically shed which are thought to be the cause of tone generation.

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 score0.001
Threshold uncertainty score0.002

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.012
GPT teacher head0.226
Teacher spread0.215 · 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
Published2017
Admission routes1
Has abstractyes

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