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Record W2523450473 · doi:10.1017/aer.2016.94

Non-linear aeroelastic analysis in the time domain of high-aspect-ratio wings: Effect of chord and taper-ratio variation

2016· article· en· W2523450473 on OpenAlexaff
Afzal Suleman, Frederico Afonso, José Vale, Éder Oliveira, Fernando Lau

Bibliographic record

VenueThe Aeronautical Journal · 2016
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsAeroelasticityAspect ratio (aeronautics)WingFlutterChord (peer-to-peer)Structural engineeringStall (fluid mechanics)Lift-to-drag ratioDragAerodynamicsMathematicsMechanicsEngineeringMaterials scienceComputer sciencePhysicsComposite material

Abstract

fetched live from OpenAlex

ABSTRACT Commercial jets usually have relatively low-aspect-ratio wings, in spite of the associated benefits of increasing the wing aspect-ratio, such as higher lift-to-drag ratios and ranges. This is partially explained by the fact that the wing becomes more flexible by increasing the aspect-ratio that results in higher deflections which can cause aeroelastic instability problems such as flutter. An aeroelastic computational framework capable of evaluating the effects of geometric non-linearities on the aeroelastic performance of high-aspect-ratio wings has been developed and validated using numerical and experimental data. In this work, the aeroelastic performance of a base wing model with 20 m span and 1 m chord is analysed and the effect of changing the wing chord or the taper-ratio is determined. The non-linear static aeroelastic equilibrium solutions are compared in terms of drag polar, root bending moment and natural frequencies, and the change in the flutter speed boundary is assessed as a function of aspect-ratio using a time-marching approach.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.007

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 science0.0000.000
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 head0.004
GPT teacher head0.207
Teacher spread0.203 · 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 designSimulation or modeling
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

Citations10
Published2016
Admission routes1
Has abstractyes

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