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Record W2749450456

Innovative wing tip equipped with morphing upper surface and morphing aileron for greener aviation

2016· article· en· W2749450456 on OpenAlexafffundvenueabout
Ruxandra Mihaela Botez, Andreea Koreanschi, Oliviu Şugar Gabor, Youssef Mébarki, Mahmoud Mamou, Yvan Tondji, Guillaume Brianchon, Mohamed Sadock Guezguez, Manuel Flores Salinas, Francesco Amoroso, Rosario Pecora, Leonardo Lecce, Gianluca Amendola, Ignazio Dimino, Antonio Concilio

Bibliographic record

VenueNPARC · 2016
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsNational Research Council CanadaÉcole de Technologie Supérieure
FundersNatural Sciences and Engineering Research Council of CanadaConsortium de Recherche et d’innovation en Aérospatiale au Québec
KeywordsMorphingAileronWingAeronauticsAviationAerospace engineeringEngineeringComputer scienceComputer graphics (images)
DOInot available

Abstract

fetched live from OpenAlex

A wing tip demonstrator equipped with a conventional aileron and with a morphing aileron was designed, manufactured and tested during three wind tunnel sessions at the NRC subsonic wind tunnel facilities in Ottawa. Experimental data during wind tunnel tests was recorded from 32 Kulite sensors installed on the morphing skin, Infrared readings and Loads balance. During the 1st and 2nd set of tests, the morphing wing equipped with its rigid aileron was studied. During the 3rd set of tests, the morphing wing was equipped with the morphing aileron. The morphing aileron and the transition flow optimization results, for the 3rd set of tests, are discussed in this paper. Despite the many challenges, the experimental results have shown that delays of the flow transition of up to 8% of the chord were achieved.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.015

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 communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.035
GPT teacher head0.242
Teacher spread0.206 · 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

Citations0
Published2016
Admission routes4
Has abstractyes

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