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Record W2318400761 · doi:10.1115/power2010-27174

Experimental and Theoretical Study on the Aerodynamic Performance of a Small Horizontal Axis Wind Turbine

2010· article· en· W2318400761 on OpenAlexaff
Maryam Refan, Horia Hangan

Bibliographic record

VenueASME 2010 Power Conference · 2010
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsWestern University
Fundersnot available
KeywordsAerodynamicsTurbineStall (fluid mechanics)Wind powerMechanicsWind tunnelTip-speed ratioTurbine bladeRotor (electric)Boundary layerTorqueControl theory (sociology)Marine engineeringPhysicsEngineeringAerospace engineeringComputer scienceMechanical engineeringElectrical engineering

Abstract

fetched live from OpenAlex

An upwind, three-bladed small horizontal axis wind turbine (HAWT) with a rotor of 2.2 m in diameter is tested in the Boundary Layer Wind Tunnel Laboratory II (BLWTL II). The power output of the turbine is measured for free stream velocities ranging from 1 m/s to 9 m/s. The blade element momentum (BEM) theory is implemented to predict the power curve of the HAWT. The theoretical characteristics of the turbine are discussed in terms of power and torque coefficients and the experimental results are compared to the numerical (BEM) estimation. Moreover, a force balance test is carried out on a single stationary blade for 16 angles of incidence, −6°≤α′≤30°, and three free stream velocities, 5, 7 and 9 m/s, and integral blade aerodynamic coefficients are determined. These experimental characteristics are intended to provide a useful basis for developing an alternative computational method to use integral blade experimental aerodynamic data to predict the power curve of the wind turbine in the transition zone between dynamic stall and fully stalled regimes.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.011
GPT teacher head0.218
Teacher spread0.207 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations5
Published2010
Admission routes1
Has abstractyes

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