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Record W2797698811 · doi:10.1017/aer.2018.15

Numerical and experimental transition results evaluation for a morphing wing and aileron system

2018· article· en· W2797698811 on OpenAlexafffund
Ruxandra Mihaela Botez, Andreea Koreanschi, Oliviu Şugar Gabor, Yvan Tondji, M. Guezguez, J.T. Kammegne, Teodor Lucian Grigorie, Dragos George Sandu, Y. Mébarki, Mahmoud Mamou, Francesco Amoroso, Rosario Pecora, Leonardo Lecce, Gianluca Amendola, Ignazio Dimino, Antonio Concilio

Bibliographic record

VenueThe Aeronautical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of CanadaConsortium de Recherche et d’innovation en Aérospatiale au Québec
KeywordsAileronMorphingWingWind tunnelAngle of attackLift coefficientStructural engineeringAerodynamicsAerospace engineeringComputer scienceEngineeringMechanicsPhysicsTurbulenceArtificial intelligence

Abstract

fetched live from OpenAlex

ABSTRACT A new wing-tip concept with morphing upper surface and interchangeable conventional and morphing ailerons was designed, manufactured, bench and wind-tunnel tested. The development of this wing-tip model was performed in the frame of an international CRIAQ project, and the purpose was to demonstrate the wing upper surface and aileron morphing capabilities in improving the wing-tip aerodynamic performances. During numerical optimisation with ‘in-house’ genetic algorithm software, and during wind-tunnel experimental tests, it was demonstrated that the air-flow laminarity over the wing skin was promoted, and the laminar flow was extended with up to 9% of the chord. Drag coefficient reduction of up to 9% was obtained when the morphing aileron was introduced.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0050.001

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 head0.021
GPT teacher head0.260
Teacher spread0.239 · 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 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

Citations45
Published2018
Admission routes2
Has abstractyes

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