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Record W4244370073 · doi:10.22215/etd/2015-10968

An Algorithm for Preliminary Aeroelastic Analysis of Composite Wind Turbine Blades

2015· dissertation· en· W4244370073 on OpenAlexaff
Alexander C. McFarlane

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsCarleton University
Fundersnot available
KeywordsAeroelasticityTurbine bladeAerodynamicsFinite element methodStructural engineeringBlade element momentum theoryComputational fluid dynamicsSolverMomentum (technical analysis)EngineeringBlade (archaeology)Beam (structure)Mechanical engineeringTurbineComputer scienceAerospace engineering

Abstract

fetched live from OpenAlex

Determining the structural response of composite horizontal axis wind turbine blades to wind loading is a challenging aeroelastic problem due to the coupling of unsteady aerodynamics and anisotropic structural dynamics. Methods such as Computational Fluid Dynamics (CFD) and Finite Element Analysis (FEA) are readily available through respected commercial software such as ANSYS, but are found to be unsuitable for the preliminary design phase in which many simulations are to be run, due to high computational time. A more efficient algorithm has been developed using a panel method for determining the unsteady aerodynamic blade loading, an anisotropic beam dynamics solver based on the Variational Asymptotic Beam Section (VABS) analysis tool developed by

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.218
Threshold uncertainty score0.828

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.012
GPT teacher head0.277
Teacher spread0.265 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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