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Record W1978080733 · doi:10.1088/0964-1726/24/3/037006

Analysis and design of lattice materials for large cord and curvature variations in skin panels of morphing wings

2015· article· en· W1978080733 on OpenAlexaff
Andrea Vigliotti, Damiano Pasini

Bibliographic record

VenueSmart Materials and Structures · 2015
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsMcGill University
FundersRoyal Society
KeywordsMorphingCurvatureCamber (aerodynamics)WingAerodynamicsPlanarStructural engineeringHomogenization (climate)Nonlinear systemComputer scienceMechanical engineeringMaterials scienceEngineeringAerospace engineeringGeometryPhysicsMathematicsArtificial intelligence

Abstract

fetched live from OpenAlex

In the past few decades, several concepts for morphing wings have been proposed with the aim of improving the structural and aerodynamic performance of conventional aircraft wings. One of the most interesting challenges in the design of a morphing wing is represented by the skin, which needs to meet specific deformation requirements. In particular when morphing involves changes of cord or curvature, the skin is required to undergo large recoverable deformation in the actuation direction, while maintaining the desired shape and strength in the others. One promising material concept that can meet these specifications is represented by lattice materials. This paper examines the use of alternative planar lattices in the embodiment of a skin panel for cord and camber morphing of an aircraft wing. We use a structural homogenization scheme capable of capturing large geometric nonlinearity, to examine the structural performance of lattice skin concepts, as well as to tune their mechanical properties in desired directions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

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 integrity0.0000.000
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 head0.019
GPT teacher head0.238
Teacher spread0.219 · 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

Citations27
Published2015
Admission routes1
Has abstractyes

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