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Record W4391542433 · doi:10.3390/act13020060

Multiphysics Modeling of a Synthetic Jet Actuator in Operation

2024· article· en· W4391542433 on OpenAlexafffund
Matthew G. Butler, Alis Ekmekci, Pierre E. Sullivan

Bibliographic record

VenueActuators · 2024
Typearticle
Languageen
FieldEngineering
TopicPlasma and Flow Control in Aerodynamics
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaAlliance de recherche numérique du CanadaCMC Microsystems
KeywordsMultiphysicsSynthetic jetActuatorComputational fluid dynamicsJet (fluid)Mechanical engineeringAcousticsBiomimeticsNoise (video)Computer scienceEngineeringAerospace engineeringFinite element methodStructural engineeringPhysicsArtificial intelligence

Abstract

fetched live from OpenAlex

Active flow control is a promising technology for reducing noise, emissions, and power consumption in various applications. To better understand the performance of synthetic jet actuators, a computational model that couples structural mechanics with electrostatics, pressure acoustics, and fluid dynamics is needed. The model presented here was validated against experimental data and then used to investigate the fluid behavior inside and outside the synthetic jet actuator cavity, the impacts of thermoviscous losses on capturing the acoustic response of the actuator, and the viability of different modeling methods of diaphragms in computational simulations. The results capture the feedback from the fluid onto the diaphragm and highlight the need for careful acoustic modeling.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.007
GPT teacher head0.202
Teacher spread0.196 · 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 designSimulation or modeling
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

Citations2
Published2024
Admission routes2
Has abstractyes

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