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Record W2759564447 · doi:10.1115/1.4038046

Application of the Blade Element Momentum Theory to Design Horizontal Axis Wind Turbine Blades

2017· article· en· W2759564447 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of Solar Energy Engineering · 2017
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversité de Sherbrooke
FundersUniversité de Sherbrooke
KeywordsChord (peer-to-peer)Blade element momentum theoryAerodynamicsBlade element theoryTurbine bladeOffset (computer science)Blade (archaeology)MechanicsHorizontal axisTurbinePhysicsRADIUSSpan (engineering)Momentum (technical analysis)CurvatureStall (fluid mechanics)Blade pitchWind speedStructural engineeringGeometryMathematicsEngineeringMeteorologyComputer science

Abstract

fetched live from OpenAlex

Small horizontal axis wind turbines (HAWTs) are increasingly used as source of energy production. Based on this observation, the blade element momentum theory (BEMT) is applied all along the blade span to calculate the optimal turbine aerodynamic performances. The main objective is to optimize the HAWT blade profile for specific initial conditions. The effects of three geometric parameters (the blade tip radius, the number of blades, and curvature) and one dynamic parameter (the tip speed ratio (TSR)) are determined for an upstream air speed of 7 m/s. A new empirical relation for the chord distribution over the blade span is presented here; c(r)/R=c0+A[1+r/R]exp(−Br/R), where c0 = 0.04 is the chord offset, A = 1/Z is an amplitude, and B = [(Z/5) + 2] is the decay constant. It takes into account both the effect of blade tip radius and the number of the blades.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.850
Threshold uncertainty score0.497

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 science0.0010.000
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 head0.008
GPT teacher head0.204
Teacher spread0.196 · 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