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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 OpenAlexafffund
Vincent Dehouck, Mohamed Lateb, Jonathan Sacheau, Hachimi Fellouah

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.

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)0.000
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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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

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

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 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

Citations30
Published2017
Admission routes2
Has abstractyes

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