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Record W2217522191 · doi:10.1115/1.4032301

Impact of Gurney Flaplike Strips on the Aerodynamic and Vortex Flow Characteristic of a Reverse Delta Wing

2015· article· en· W2217522191 on OpenAlexaff
T. Lee

Bibliographic record

VenueJournal of Fluids Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsMcGill University
Fundersnot available
KeywordsWingtip vorticesVortex liftLift coefficientWingStall (fluid mechanics)Lift-to-drag ratioLeading edgeTrailing edgeVortexDragMechanicsWing twistLift (data mining)AirfoilAerodynamicsAngle of attackPhysicsSTRIPSMaterials scienceStructural engineeringEngineeringHorseshoe vortexComputer scienceVorticityComposite materialReynolds number

Abstract

fetched live from OpenAlex

The impact of Gurney flaplike strips, of different geometric configurations and heights, on the aerodynamic characteristics and the tip vortices generated by a reverse delta wing (RDW) was investigated via force-balance measurement and particle image velocimetry (PIV). The addition of side-edge strips (SESs) caused a leftward shift of the lift curve, resembling a conventional trailing-edge flap. The large lift increment overwhelmed the corresponding drag increase, thereby leading to an improved lift-to-drag ratio compared to the baseline wing. The lift and drag coefficients were also found to increase with the strip height. The SES-equipped wing also produced a strengthened vortex compared to its baseline wing counterpart. The leading-edge strips (LESs) were, however, found to persistently produce a greatly diffused vortex flow as well as a small-than-baseline-wing lift in the prestall α regime. The downward LES delivered a delayed stall and an increased maximum lift coefficient compared to the baseline wing. The LESs provide a potential wingtip vortex control alternative, while the SESs can enhance the aerodynamic performance of the RDW.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.012
GPT teacher head0.210
Teacher spread0.199 · 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

Citations12
Published2015
Admission routes1
Has abstractyes

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