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Record W2955231105 · doi:10.1177/0309524x19849861

On the wind turbine blade loads from an aeroelastic simulation and their transfer to a three-dimensional finite element model of the blade

2019· article· en· W2955231105 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueWind Engineering · 2019
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsAeroelasticityBlade element momentum theoryAerodynamicsBlade element theoryFinite element methodTurbine bladeBlade (archaeology)Structural engineeringTurbineNacelleRotor (electric)EngineeringHelicopter rotorMechanical engineeringAerospace engineering

Abstract

fetched live from OpenAlex

This article first presents a description of the different load types to which a wind turbine blade is subjected. Analytical equations are derived to express blade loads from operation parameters of the wind turbine (rotor and nacelle velocities and accelerations; pitch, coning, tilt, and azimuth angles; blade mass properties; turbine geometry). This allows a better understanding of the contribution of each of these parameters to the total load on a blade. A difficulty arises for transferring the loads computed by an aeroelastic model (a one-dimensional model of the blade) to a three-dimensional finite element model of the blade. A method is proposed for that purpose. It consists in applying the aerodynamic loads using RBE3 elements and applying gravitational and inertial loads as volume forces. Finally, an example of this method used for the design of a 10 kW wind turbine blade is presented.

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.

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 score0.039
Threshold uncertainty score0.511

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.197
Teacher spread0.185 · 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