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Record W2053563376 · doi:10.1177/0954410014538244

Active control of aeroelastic oscillations of a cantilever structure using piezoelectric strips

2014· article· en· W2053563376 on OpenAlexaff
Tahereh Mirmohammadi, Arun K. Misra, Dan Mateescu

Bibliographic record

VenueProceedings of the Institution of Mechanical Engineers Part G Journal of Aerospace Engineering · 2014
Typearticle
Languageen
FieldEngineering
TopicAeroelasticity and Vibration Control
Canadian institutionsMcGill University
Fundersnot available
KeywordsAeroelasticityCantileverAerodynamicsVibration controlController (irrigation)Finite element methodVibrationActuatorControl theory (sociology)Aerodynamic forcePiezoelectricityPiezoelectric sensorStructural engineeringEngineeringAcousticsComputer sciencePhysicsControl (management)Aerospace engineering

Abstract

fetched live from OpenAlex

Recently, active control of aeroelastic oscillations and the use of piezoelectric materials in vibration analysis and control of structures have been the subjects of many researches in air vehicle design. The present paper deals with the use of bonded piezoelectric sensors and actuators in control of aeroelastic oscillations of a cantilever plate under the effects of unsteady subsonic aerodynamic loading. The aerodynamic loading is calculated using a numerical panel method. The structural model of the plate undergoing small transverse oscillations is modeled using finite element formulation. The aerodynamic and structural models are coupled through an interactive computer model to transfer the data simultaneously and an active feedback control is applied to suppress the oscillations. A systematic method is also presented to obtain the gains of the feedback controller. The numerical results show that the controller can effectively reduce the amplitude of the oscillations in small amount of time and with small gains, hence lower cost.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.500
Threshold uncertainty score0.812

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.183
Teacher spread0.176 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

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