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Record W2518451748 · doi:10.1115/1.4034524

Two-Dimensional Experimental Investigation on the Effects of a Fowler Flap Gap and Overlap Size on the Wake Flow Field

2016· article· en· W2518451748 on OpenAlexaff
David Demel, Mohsen Ferchichi, W. Allan, Marouen Dghim

Bibliographic record

VenueJournal of Fluids Engineering · 2016
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Turbulent Flows
Canadian institutionsRoyal Military College of Canada
Fundersnot available
KeywordsTrailing edgeStall (fluid mechanics)Angle of attackWingWakeMechanicsAirfoilMaterials scienceLeading edgeParticle image velocimetryOpticsPhysicsAerodynamicsStructural engineeringEngineeringTurbulence

Abstract

fetched live from OpenAlex

This work details an experimental investigation on the effects of the variation of flap gap and overlap sizes on the flow field in the wake of a wing-section equipped with a trailing edge Fowler flap. The airfoil was based on the NACA 0014-1.10 40/1.051 profile, and the flap was deployed with 40 deg deflection angle. Two-dimensional (2D) particle image velocimetry (PIV) measurements of the flow field in the vicinity of the main wing trailing edge and the flap region were performed for the optimal flap gap and overlap, as well as for flap gap and overlap increases of 2% and 4% chord beyond optimal, at angles of attack of 0 deg, 10 deg, and 12 deg. For all the configurations investigated, the flow over the flap was found to be fully stalled. At zero angle of attack, increasing the flap gap size was found to have minor effects on the flow field but increased flap overlap resulted in misalignment between the main wing boundary layer (BL) flow and the slot flow that forced the flow in the trailing edge region of the main wing to separate. When the angle of attack was increased to near stall conditions (at angle of attack of 12 deg), increasing the flap gap was found to energize and improve the flow in the trailing edge region of the main wing, whereas increased flap overlap further promoted flow separation on the main wing suction surface possibly steering the wing into stall.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.304
Threshold uncertainty score0.329

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 head0.007
GPT teacher head0.189
Teacher spread0.181 · 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 designBench or experimental
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

Citations4
Published2016
Admission routes1
Has abstractyes

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