MétaCan
Menu
Back to cohort
Record W4409622363 · doi:10.1155/je/8779428

Wind Turbine Optimization by Blade Element Momentum Method and Particle Swarm Optimization Technique

2025· article· en· W4409622363 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 Engineering · 2025
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsBlade (archaeology)Particle swarm optimizationBlade element momentum theoryTurbine bladeMomentum (technical analysis)Multi-swarm optimizationTurbineElement (criminal law)Particle (ecology)Computer scienceMechanical engineeringMarine engineeringEngineeringMathematical optimizationMathematicsGeologyEconomics

Abstract

fetched live from OpenAlex

The aerodynamic efficiency of wind turbines is greatly influenced by the shape of their airfoils. In this study, four airfoils were optimized to enhance the performance of a small horizontal axis wind turbine. The optimization process involved adjusting the thickness and camber of the airfoils using the blade element momentum method and particle swarm optimization technique. The goal was to find the most aerodynamically efficient airfoil based on the thickness‐to‐camber ratio. The optimized airfoils were compared to select the best one for a three‐blade, 6‐m diameter turbine configuration. The results showed that the optimized microturbine achieved better efficiency than the baseline turbines and those optimized by other researchers. Notably, the study also rigorously validated the blade element momentum–particle swarm optimization methodology through experimental methods, providing robust support for our findings.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.451
Threshold uncertainty score0.438

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 head0.004
GPT teacher head0.229
Teacher spread0.224 · 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