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Record W4411572179 · doi:10.1017/aer.2025.10028

Aeroelastic stability of imperfectly supported high-aspect-ratio wings

2025· article· en· W4411572179 on OpenAlexafffund
Mojtaba Kheiri, Mahdi Riazat

Bibliographic record

VenueThe Aeronautical Journal · 2025
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of CanadaConcordia University
KeywordsAeroelasticityWingAspect ratio (aeronautics)Stability (learning theory)Structural engineeringAerospace engineeringEngineeringComputer scienceMaterials scienceAerodynamicsComposite material

Abstract

fetched live from OpenAlex

Abstract This paper examines the aeroelastic stability of uniform flexible wings imperfectly supported at one end and free at the other. Real-world aircraft wings inevitably exhibit imperfections, including non-ideal end supports. This work is motivated by the critical need to fundamentally understand how end-support imperfections influence the aeroelastic behaviour of fixed wings. The equations of motion are obtained via the extended Hamilton’s principle. The bending-torsional dynamics of the wing is approximated using the Euler-Bernoulli beam theory. The aerodynamic lift and pitching moment are modelled using the unsteady aerodynamics for the arbitrary motion of thin aerofoils in the time domain, extended by the strip flow theory. The imperfect support is modelled via rotational springs (with linear stiffness) for both bending and torsional degrees of freedom. The Galerkin method is used for the spatial discretisation. The stability analysis is performed by solving the resulting eigenvalue problem, and the numerical results are presented in Argand diagrams. The numerical results presented in this study are novel and offer great insights. It is demonstrated that support imperfections can substantially influence the critical flow velocity for both flutter and divergence, as well as alter the sequence of instabilities and the unstable mode. The extent of these effects directly depends on the magnitude of the imperfections. Interestingly–and counterintuitively–in certain cases, a reduction in the flutter speed is observed as the imperfections decrease.

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 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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.408
Threshold uncertainty score0.876

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 integrity0.0000.001
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 head0.008
GPT teacher head0.219
Teacher spread0.211 · 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 teacher head, 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
Published2025
Admission routes2
Has abstractyes

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