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Record W2141403524 · doi:10.2514/2.3129

Analysis of Nonlinear Aeroelastic Signals

2003· article· en· W2141403524 on OpenAlexafffund
Hekmat Alighanbari, B. H. K. Lee

Bibliographic record

VenueJournal of Aircraft · 2003
Typearticle
Languageen
FieldPhysics and Astronomy
TopicChaos control and synchronization
Canadian institutionsNational Research Council CanadaToronto Metropolitan University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsAeroelasticityAirfoilNonlinear systemControl theory (sociology)Lyapunov exponentAttractorChaoticPhase spaceMathematicsDescribing functionLimit cycleBilinear interpolationAerodynamicsMathematical analysisLimit (mathematics)Computer sciencePhysicsEngineeringStructural engineeringMechanics

Abstract

fetched live from OpenAlex

The objective of this investigation is to present proper signal processing techniques to analyze nonlinear aeroelastic time series where limit cycle oscillations and chaotic motions may occur. A powerful method to study nonlinear aeroelastic behavior of aircraft structures is the phase-space reconstruction technique. In the reconstruction process, the mutual information function and the percentage of false neighbors methods are used to estimate the time-delay and the dimension of the attractor, respectively. The dynamics of the system is then determined from the Lyapunov exponents. A method of estimating frequency and damping values of the aeroelastic system from the reconstructed phase-space is also presented. Examples are given for a two-dimensiona l airfoil oscillating in pitch and plunge with either a bilinear or a cubic spring nonlinearity in one of the degrees of freedom.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

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 head0.007
GPT teacher head0.236
Teacher spread0.229 · 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

Citations6
Published2003
Admission routes2
Has abstractyes

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