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Record W2793735286 · doi:10.2514/1.j056921

Synthetic Jet Performance for Different Axisymmetric Cavities Analyzed with Three-Dimensional Lattice-Boltzmann Method

2018· article· en· W2793735286 on OpenAlexafffund
Hongbin Mu, Qingdong Yan, Wei Wei, Pierre E. Sullivan

Bibliographic record

VenueAIAA Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicLattice Boltzmann Simulation Studies
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaChina Scholarship CouncilUniversity of TorontoGovernment of OntarioCompute Canada
KeywordsBeijingChinaEngineeringMechanical engineeringPhysicsGeographyArchaeology

Abstract

fetched live from OpenAlex

Asynthetic jet actuator is a zero-net mass-flux device that imparts momentum and is useful for active flow control [1,2]. To study the influence of cavity shape on synthetic jet performance, this Note presents model results to validate previous experimental investigations that the synthetic jet is sensitive to cavity shape. Three axisymmetric synthetic jets with different cavity shapes (cylindrical, conical, and contraction), as shown in Figs. 1 and 2, are examined for jet performance, keeping other parameters constant, such as cavity volume, nozzle length, and nozzle diameter [3–5]. Using a three-dimensional lattice-Boltzmann method with the Bhatnagar–Gross–Krook collision models [6–8], the comparison of the three shaped cavities is conducted using the same sinusoidal velocity inlet boundary condition. Time-dependent synthetic jet simulations are carried out, the velocity and momentum profiles are illustrated and discussed, and the numerical simulations are validated against previous experimental data. The computed results confirm that synthetic jet performance depend on cavity shape. The jet flow rate and momentum, relevant in flow control applications, decrease sequentially from the cylindrical to the conical to the contraction cavity.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.356
Threshold uncertainty score0.881

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.024
GPT teacher head0.270
Teacher spread0.246 · 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 teacher head, 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

Citations7
Published2018
Admission routes2
Has abstractyes

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