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Record W4366090424 · doi:10.3390/aerospace10040376

Structural Dynamic Characterization of a Modular Morphing Wing Exploiting Finite Elements and Taguchi Methodology

2023· article· en· W4366090424 on OpenAlexafffundabout
Faisal Mahmood, Seyed M. Hashemi, Hekmat Alighanbari

Bibliographic record

VenueAerospace · 2023
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMorphingWingModular designAeroelasticityFinite element methodAerodynamicsStructural engineeringComputer scienceEngineeringAerospace engineeringArtificial intelligence

Abstract

fetched live from OpenAlex

Detrimental environmental impacts due to the increasing demands of the aviation industry have gained tremendous global attention. With a potential fuel saving, along with high aerodynamic performance and maneuverability during different phases of a flight, adaptable wing design has become a viable alternative to its fixed-shape counterpart. A morphing wing design embraces, and can respond accordingly to, most of the flight condition variations effectively and efficiently. Despite these prospects, morphing wing design comes with some challenges due to its inherent complexity caused by an increased number of degrees of freedom. With the availability of various morphing parameters, the vibration signature of a morphing wing design plays a vital role in terms of its structural as well as aeroelastic characteristics. In the present paper, the dynamic characteristics of a re-configurable modular morphing wing developed in-house by a research team at Toronto Metropolitan University are investigated. This modular morphing wing, developed based on the idea of a parallel robot, consists of a number of structural elements connected to each other and to the wing ribs through eyebolt joints. Timoshenko bending beam theories, in conjunction with finite element methodology, are exploited. The free vibration of un-morphed (original) and morphed configurations undergoing multiple levels of sweep and spanwise morphing is presented through a design of experiment methodology.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.935
Threshold uncertainty score0.429

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 head0.031
GPT teacher head0.255
Teacher spread0.224 · 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 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

Citations4
Published2023
Admission routes3
Has abstractyes

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