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Record W3164028129 · doi:10.11159/ffhmt21.135

Computational Study on Wingtip Vertical Fluid Injection for InducedDrag Reduction

2021· article· en· W3164028129 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2021
Typearticle
Languageen
FieldEngineering
TopicAerodynamics and Fluid Dynamics Research
Canadian institutionsnot available
Fundersnot available
KeywordsDragReduction (mathematics)Lift-induced dragDrag coefficientComputational fluid dynamicsComputer scienceAerospace engineeringMechanicsPhysicsEngineeringMathematicsGeometry

Abstract

fetched live from OpenAlex

The present study examines the numerical analysis of the effects of wingtip vertical fluid injection on the performance of a 3D wing. A wing configuration was chosen as a baseline configuration and a slot was created at the wingtip to inject the fluid vertically. The investigation was performed at various incidence angles along with a range of fluid injection speeds. The reduction in wingtip vortices, due to the presence of the vertical fluid injection at the wingtip, was observed. In turn, an improvement in pressure distribution around the wingtip was apparent. With an increase in injection velocity, the drag values were seen to decrease. However, for all injection flowrates, the lift-to-drag ratio increases. This alternate control method can be used to improve the aerodynamic efficiency of the wing by reducing the induced drag.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.931
Threshold uncertainty score0.595

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 head0.030
GPT teacher head0.272
Teacher spread0.242 · 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