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Record W2091778354 · doi:10.1142/s0219455414500849

Analytical and Experimental Aeroelastic Wing Flutter Analysis and Suppression

2015· article· en· W2091778354 on OpenAlexaff
Khalid A. Alsaif, Mosaad A. Foda, Hachimi Fellouah

Bibliographic record

VenueInternational Journal of Structural Stability and Dynamics · 2015
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsUniversité de Sherbrooke
Fundersnot available
KeywordsAeroelasticityFlutterAirfoilControl theory (sociology)AerodynamicsWingAerodynamic forceEngineeringStructural engineeringNonlinear systemWind tunnelController (irrigation)Computer scienceAerospace engineeringPhysicsControl (management)

Abstract

fetched live from OpenAlex

Aeroelastic response and control of airfoil-flap wing exposed to unsteady aerodynamic loads is addressed. The aim is to suppress flutter and to maintain stability of the system. The analytical aerodynamic model is featuring plunging–pitching–flapping coupled motion. Both linear and nonlinear models are developed. Linear quadratic regulator theory is used to design a full state feedback controller in state-space. The control law is implemented through the flap torque to suppress flutter instability and enhance the aeroelastic response. The system response is investigated when it is flying beyond the flutter speed and the control is delayed by a few seconds. The effects of aircraft propeller excitation and the variation of the aspect ratio on the intitiation of flutter are investigated. Numerical simulations are complemented by experimental measurements in a wind tunnel for NACA0012 airfoil.

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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.008

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.012
GPT teacher head0.253
Teacher spread0.241 · 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

Citations7
Published2015
Admission routes1
Has abstractyes

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