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Record W4312468280 · doi:10.1115/fedsm2022-87621

Separation Control on an NACA 0025 Airfoil Using an Array of MEMS-Based Synthetic Jets

2022· article· en· W4312468280 on OpenAlexaff
Karen Xu, Philippe Lavoie, Pierre E. Sullivan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicPlasma and Flow Control in Aerodynamics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAirfoilSynthetic jetParticle image velocimetryFlow separationFlow visualizationReynolds numberDragFlow control (data)AcousticsWakeChord (peer-to-peer)ActuatorMaterials scienceVortexMechanicsAngle of attackAerodynamicsAerospace engineeringFlow (mathematics)PhysicsEngineeringComputer scienceTurbulenceElectrical engineering

Abstract

fetched live from OpenAlex

Abstract An novel array of micro-electromechanical systems (MEMS) synthetic jets was designed to control flow separation by periodic blowing at two pre-determined frequencies on a NACA 0025 airfoil for a chord Reynolds number Rec = 105 and angle-of-attach α = 10°. The synthetic jets were generated by commercially available microblowers, providing ease of maintenance compared to other customized synthetic jet actuators. The velocity output of this jet array was characterized with hot-wire anemometry (HWA), and reattached flow was identified using smoke-wire visualization. Pressure measurements show that the array can suppress flow separation on the airfoil, resulting in 2.5 times lift recovery. From wake measurements, up to 50% drag reduction was achieved with the actuation of the jet array compared to the baseline (uncontrolled) case. Particle image velocimetry (PIV) was used to visualize the flow fields of the baseline case and two controlled cases. There was a significant difference in the scale of the vortices produced by the jet array and features of the reattached flow between the two actuation frequencies used.

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 score0.229
Threshold uncertainty score0.627

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 head0.013
GPT teacher head0.241
Teacher spread0.228 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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