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Aeroelastic modelling of tail fins for small wind turbines

2022· article· en· W4281684770 on OpenAlexaff
Mohamed M. Hammam, David Wood

Bibliographic record

VenueJournal of Physics Conference Series · 2022
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsAeroelasticityAngle of attackYawLift (data mining)Nonlinear systemWind tunnelFinMechanicsVortexEuler anglesAllowance (engineering)Rotor (electric)Control theory (sociology)AerodynamicsStructural engineeringPhysicsEngineeringMathematicsGeometryAerospace engineeringComputer scienceMechanical engineering

Abstract

fetched live from OpenAlex

Abstract Many small upwind turbines use a tail fin to align the rotor with the wind. Despite the importance of a well-designed fin for efficient operation and in generating ultimate and fatigue loads, the aeroelastic modelling of tail fins is not well developed. This work extends the previous linearized analyses by including nonlinear effects and the difference between the yaw angle and the angle of attack. The analysis is based on unsteady slender body theory, but includes the vortex lift generated at high angles of attack and the effects of vortex bursting. The model predicts with reasonable accuracy the yaw behaviour of delta-shaped tail fins (without a rotor) released at 45° in a wind tunnel, provided allowance is made for the viscous friction in the yaw bearings. The difference in the response frequency between the linear and nonlinear model increases at the higher yaw angle of 80° for which no wind tunnel measurements are available. As a first step towards simplifying the nonlinear model, the difference in yaw angle predictions from the linear model is estimated. The maximum difference is a function of initial yaw angle and is large for yaw angles of magnitude greater than 45°.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.654
Threshold uncertainty score0.350

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 head0.054
GPT teacher head0.218
Teacher spread0.164 · 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

Citations3
Published2022
Admission routes1
Has abstractyes

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