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Flutter Analysis of a Morphing Wing Technology Demonstrator: Numerical Simulation and Wind Tunnel Testinga

2016· article· en· W2602827044 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueINCAS BULLETIN · 2016
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsAileronFlutterMorphingAeroelasticityWingStructural engineeringWind tunnelFinite element methodEngineeringAerospace engineeringAerodynamicsComputer science

Abstract

fetched live from OpenAlex

As part of a morphing wing technology project, the flutter analysis of two finite element models and the experimental results of a morphing wing demonstrator equipped with aileron are presented. The finite element models are representing a wing section situated at the tip of the wing; the first model corresponds to a traditional aluminium upper surface skin of constant thickness and the second model corresponds to a composite optimized upper surface skin for morphing capabilities. The two models were analyzed for flutter occurrence and effects on the aeroelastic behaviour of the wing were studied by replacing the aluminium upper surface skin of the wing with a specially developed composite version. The morphing wing model with composite upper surface was manufactured and fitted with three accelerometers to record the amplitudes and frequencies during tests at the subsonic wind tunnel facility at the National Research Council. The results presented showed that no aeroelastic phenomenon occurred at the speeds, angles of attack and aileron deflections studied in the wind tunnel and confirmed the prediction of the flutter analysis on the frequencies and modal displacements.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.347
Threshold uncertainty score0.318

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.010
GPT teacher head0.219
Teacher spread0.209 · 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