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Record W2909338288 · doi:10.2514/6.2019-1857

Stretchable Structure for a Benchtop-Scale Morphed Leading Edge Demonstration

2019· article· en· W2909338288 on OpenAlexaff
Michael B. Jakubinek, Steven Roy, Marc Palardy-Sim, Behnam Ashrafi, Farjad Shadmehri, Michael Barnes, Yadienka Martinez‐Rubi, Meysam Rahmat, Benoît Simard, Ali Yousefpour, F. Fortin

Bibliographic record

VenueAIAA Scitech 2019 Forum · 2019
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsEnhanced Data Rates for GSM EvolutionScale (ratio)Computer scienceMaterials scienceArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Adaptive structures and morphing aircraft technologies have generated much interest in the aerospace community, including regular sessions at AIAA conferences. However, due to the lack of suitable materials to make stretchable structures with loadbearing ability, relatively few works address the development of morphing wings that undergo substantial area change despite the advantage of doing so for the aerodynamic performance. At SciTech 2018 we first presented an approach to development of a stretchable skin based on carbon nanotube-polyurethane sheets with approximately 25 wt.% carbon nanotubes. In this paper we describe the production and properties of the nanocomposite skin, a complimentary support structure produced via structural optimization and 3D printing, and integration of these components along with a custom-designed actuation mechanism within a 25 cm wide, 200 cm long benchtop-scale morphing leading edge demonstration.

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 score0.003
Threshold uncertainty score0.009

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 integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.004
GPT teacher head0.199
Teacher spread0.195 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

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Same venueAIAA Scitech 2019 ForumSame topicAeroelasticity and Vibration ControlFrench-language works237,207