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Record W3201882004 · doi:10.1115/fedsm2021-65225

Frequency Response of Synthetic Jets Emanating From an Array of Circular Orifices

2021· article· en· W3201882004 on OpenAlexafffund
Nadim Arafa, Pierre E. Sullivan, Alis Ekmekci

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Acoustics in Jet Flows
Canadian institutionsUniversity of Toronto
FundersUniversity of Toronto
KeywordsBody orificeSynthetic jetAcousticsExcitationJet (fluid)Helmholtz resonatorPhysicsOpticsMechanicsResonatorActuatorEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

Abstract The effect of the excitation frequency of synthetic jet actuators on the mean jet velocity of synthetic jets issuing from an array of circular orifices is investigated experimentally. Herein, the focus is placed on an array of circular orifices, rather than a single orifice, as it brings the advantage of covering long-span airfoils. The array consists of 16 circular orifices, each having a diameter of 3.42 mm, distributed over a span of 300 mm. The jets are generated by the excitation of a single cavity via 16 piezoelectric elements. Localized velocity measurements at the exit of the orifices show that the mean jet velocity varies with the excitation frequency. Several distinct resonant peaks were observed in the frequency response. Acoustic simulations of the cavity showed that these peaks correspond to acoustic mode shapes of the cavity. Due to the high-aspect ratio of the cavity, several acoustic mode shapes exist in the excitation frequency range aside from the Helmholtz resonance frequency. Moreover, the mean jet velocity emanating from the array shows a variation from orifice to orifice, depending on the excited acoustic mode.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0000.000
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 head0.009
GPT teacher head0.224
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
Published2021
Admission routes2
Has abstractyes

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