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Record W2014238940 · doi:10.1063/1.4879276

Ideal wind turbine performance at very low tip speed ratio

2014· article· en· W2014238940 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

VenueJournal of Renewable and Sustainable Energy · 2014
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsTip-speed ratioPropellerConformal mapTurbineRADIUSPhysicsWind speedMechanicsAspect ratio (aeronautics)Rotor (electric)Line (geometry)Advance ratioThrustMathematicsMathematical analysisGeometryControl theory (sociology)Blade pitchAerodynamicsEngineeringThermodynamicsMeteorologyComputer science

Abstract

fetched live from OpenAlex

At very low tip speed ratios, wind turbine rotors behave similarly to stationary wings for which the well-known lifting line analysis gives the optimal loading. Lifting line analysis is applied to a stationary rotor of N blades for N = 1, 2, and 4. Analytic (for N = 1 and 2) or semi-analytic solutions (for N = 4) agree with the classical results obtained by conformal mapping. The present solutions compare well with numerical solutions for the Goldstein function for optimally loaded propeller or wind turbine rotors at low tip speed ratio. In all cases, the induced velocity is linear with radius. Assuming this result applies for all N, lifting line analysis is recast as a singular integral equation whose solution agrees with Goldstein's obtained using the same conformal mappings as in his general analysis for any tip speed ratio. The implication is that the assumed induced velocity distribution is correct, and is, therefore, fundamentally different from that at high tip speed ratios when the induced velocity is inversely proportional to radius. For any N, the power and thrust coefficients become proportional to the square of the tip speed ratio and the tip loss factor alters significantly from the common form used at higher tip speed ratios. Extending the analysis to finite pitch was not achieved but several important results were obtained, including the behaviour of the tip loss as N varies at low tip speed ratio. The behaviour is complex and the resulting tip loss factor exceeds unity for a significant part of the blade.

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)0.001
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 score0.817
Threshold uncertainty score0.574

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.004
GPT teacher head0.181
Teacher spread0.177 · 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