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Record W2201891279 · doi:10.1139/juvs-2014-0018

Design and testing of foam-inflated wings for small unmanned aerial vehicles

2015· article· en· W2201891279 on OpenAlexaffvenue
Goetz Bramesfeld, Ryan Prinster

Bibliographic record

VenueJournal of Unmanned Vehicle Systems · 2015
Typearticle
Languageen
FieldEngineering
TopicAerospace Engineering and Energy Systems
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsInflatableWingStructural engineeringAileronAerodynamicsStiffnessChord (peer-to-peer)Materials scienceMechanical engineeringAerospace engineeringEngineeringComputer science

Abstract

fetched live from OpenAlex

Inflatable wings can greatly improve the portability of small UAVs. The foam-inflated wing concept consists of a hybrid structure of rigid and flexible elements. This approach potentially reduces the packaging and logistical needs before deployment of a small UAV. The small packaging volume of the wing is achieved using spars and wing skin that are made out of flexible polyester-film material. For deployment, the cylindrical spars are rigidized using expanding foam that increases the buckling stiffness of the spars. Solid ribs that were previously attached to those spars provide high shape compliance for the external skin. Due to the solid rib structure, the chord length of the wing defines the maximum packing dimension before inflation. As demonstrated in tests, the inflation process of the hybrid wing is uncomplicated and swift. The subsequent foam-inflated structure is lightweight and can be tailored to the expected loads. In addition, the underlying ribs ensure a high aerodynamic quality, despite the inflatable nature of the hybrid wing structure. The feasibility of the concept of the hybrid wing structure has been demonstrated in flight tests.

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)0.001
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.045
GPT teacher head0.216
Teacher spread0.171 · 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
Published2015
Admission routes2
Has abstractyes

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