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Record W4405107045 · doi:10.3390/fluids9120290

Synthetic Jet Actuators for Active Flow Control: A Review

2024· review· en· W4405107045 on OpenAlexafffund
Howard H-C Ho, Ali Shirinzad, Ebenezer E. Essel, Pierre E. Sullivan

Bibliographic record

VenueFluids · 2024
Typereview
Languageen
FieldEngineering
TopicPlasma and Flow Control in Aerodynamics
Canadian institutionsConcordia UniversityUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du Canada
KeywordsSynthetic jetBody orificeFlow control (data)ActuatorFluidicsJet (fluid)MechanicsVortexComputer scienceFlow (mathematics)Fluid dynamicsControl theory (sociology)PhysicsMechanical engineeringAerospace engineeringEngineeringControl (management)Telecommunications

Abstract

fetched live from OpenAlex

A synthetic jet actuator (SJA) is a fluidic device often consisting of a vibrating diaphragm that alters the volume of a cavity to produce a synthesized jet through an orifice. The cyclic ingestion and expulsion of the working fluid leads to a zero-net mass-flux and the transfer of linear momentum to the working fluid over an actuation cycle, leaving a train of vortex structures propagating away from the orifice. SJAs are a promising technology for flow control applications due to their unique features, such as no external fluid supply or ducting requirements, short response time, low weight, and compactness. Hence, they have been the focus of many research studies over the past few decades. Despite these advantages, implementing an effective control scheme using SJAs is quite challenging due to the large parameter space involving several geometrical and operational variables. This article aims to explain the working mechanism of SJAs and provide a comprehensive review of the effects of SJA design parameters in quiescent conditions and cross-flow.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.004
Threshold uncertainty score0.014

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0040.002

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 head0.018
GPT teacher head0.279
Teacher spread0.261 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations20
Published2024
Admission routes2
Has abstractyes

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