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Record W2071959202 · doi:10.1260/0309-524x.37.4.381

BEM Simulation and Performance Analysis of a Small Wind Turbine Rotor

2013· article· en· W2071959202 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

VenueWind Engineering · 2013
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversity of Guelph
Fundersnot available
KeywordsWakeAirfoilTurbineStall (fluid mechanics)Marine engineeringRotor (electric)DragChord (peer-to-peer)Aerospace engineeringWind tunnelTurbine bladeEngineeringEnvironmental scienceMechanical engineeringComputer science

Abstract

fetched live from OpenAlex

The blade element momentum (BEM) method is a popular tool for predicting the performance of wind turbine rotors. This study investigated the impact of including factors such as tip loss, hub loss and drag coefficients in BEM simulations of a Bergey XL.1 small wind turbine. The Bergey XL.1 has constant chord, untwisted blades that are challenging to simulate owing to the large variation in angle of attack along the blade during operation. Methods of including post-stall airfoil characteristics, and three wake approaches (Buhl, Glauert and Wilson-Walker) were also examined. BEM simulations were consistent with test data from a Bergey XL.1 collected using a vehicle-based platform. Including tip losses, drag coefficients and wake effects in the BEM simulation had a significant impact on predicted performance, while the effect of including hub loss was negligible. The results illustrate that BEM methods can predict the performance of small wind turbine rotors.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.263
Threshold uncertainty score0.503

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 head0.010
GPT teacher head0.194
Teacher spread0.184 · 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