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Record W2796469581 · doi:10.1155/2018/9358132

Unsteady Simulation of a Synthetic Jet Actuator with Cylindrical Cavity Using a 3-D Lattice Boltzmann Method

2018· article· en· W2796469581 on OpenAlexafffund
Hongbin Mu, Qingdong Yan, Wei Wei, Pierre E. Sullivan

Bibliographic record

VenueInternational Journal of Aerospace Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilUniversity of TorontoCompute CanadaCanada Foundation for InnovationGovernment of Ontario
KeywordsLattice Boltzmann methodsSynthetic jetMechanicsMass fluxVortexActuatorInflowPhysicsJet (fluid)Conservation of massFlow control (data)Momentum (technical analysis)InletEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

A synthetic jet actuator is a zero-net mass-flux device that imparts momentum to its surroundings and has proved to be a useful active flow control device. Using the lattice Boltzmann method (LBM) with the Bhatnagar-Gross-Krook (BGK) collision models, a 3-D simulation of a synthetic jet with cylindrical cavity employing a sinusoidal velocity inlet boundary condition was conducted. The velocity distributions are illustrated and discussed, and the numerical results are validated against previous experimental data. The computed results show the ingestion and expulsion flow over one working cycle as well as the evolution of vortices important to the control of the separated shear layer. Zero-net mass-flux behavior is confirmed.

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 score0.016
Threshold uncertainty score0.032

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.305
Teacher spread0.284 · 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

Citations5
Published2018
Admission routes2
Has abstractyes

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