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Record W4293078354 · doi:10.21203/rs.3.rs-1738821/v1

Flutter Analysis of Floating Horizontal-axis Wind Turbine Blades

2022· preprint· en· W4293078354 on OpenAlexaff
Saeid Fadaei, Abbas Mazidi, Fred F. Afagh, Robert Langlois

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsCarleton University
Fundersnot available
KeywordsAeroelasticityFlutterTurbine bladeBlade element momentum theoryTorsion (gastropod)Structural engineeringBlade element theoryTimoshenko beam theoryAerodynamicsTurbineRotor (electric)Beam (structure)Helicopter rotorEngineeringMechanicsPhysicsMechanical engineering

Abstract

fetched live from OpenAlex

Abstract In this study, flutter of blades in floating horizontal-axis wind turbines is investigated. The blade is modeled as a non-uniform Euler-Bernoulli beam in bending and torsion, which can experience large deflections. The discretized form of the aeroelastic governing equations of the blade is obtained by combining blade element momentum theory (BEM) and geometrically exact beam theory (GEBT). To emulate the true physical and geometrical properties of the blade, for each property, a mathematical function that has been fit to the series of data points corresponding to the NREL 5 MW turbine blade is constructed and used in the aeroelastic governing equations. Numerical results are compared with the results obtained from the ABAQUS software and good agreement is observed. Results are presented for both parked and operational wind turbine rotors. Results show the significant effect of the turbine tower rotation, due to wave action, on the aeroelastic stability of the blades. Furthermore, it is shown that coupled motion of the platform as a rigid body with rotor angular velocity can lead to flutter instability at low wind speeds.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity, Insufficient payload (model declined to judge)
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 score0.534
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0030.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0050.000

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 head0.040
GPT teacher head0.337
Teacher spread0.298 · 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.

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
Published2022
Admission routes1
Has abstractyes

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