MétaCan
Menu
Back to cohort
Record W2083301595 · doi:10.1115/gt2014-27335

On the Effect of Altitude on the Performance of a Small Wind Turbine Blade

2014· article· en· W2083301595 on OpenAlexaff
Abolfazl Pourrajabian, Masoud Mirzaei, Reza Ebrahimi, David Wood

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBlade (archaeology)TurbineBlade element momentum theoryWind powerPower (physics)Altitude (triangle)TorqueWind speedTurbine bladeBlade pitchPower densityControl theory (sociology)Marine engineeringEngineeringComputer scienceStructural engineeringMechanical engineeringMathematicsPhysicsGeometryMeteorologyElectrical engineering

Abstract

fetched live from OpenAlex

This study deals with the effect of the altitude on the performance of a Small Wind Turbine (SWT) blade. Four potential regions of wind energy with altitudes up to 3,000 m were selected and a three-bladed, 2 m diameter small HAWT was designed for those regions. Starting time was combined with output power in an objective function to improve the performance of the turbine at low wind speeds. The goals of the objective function, the output power and the starting performance, were addressed by geometry optimization of the blade which was carried out by the genetic algorithm. The modified Blade-Element Momentum (BEM) theory was applied to calculate the output power and starting time. Results show that the performance of an optimal blade which was optimized for operating at sea level degrades for other regions. That degradation is more important for the starting performance in comparison with the reduction of the power coefficient. To improve the performance of the blade in the considered regions, two redesign procedures were carried out. First, the geometry of the blade was optimized respect to the air density of the regions which led to increase of the power coefficient and the starting time. Much more power was achieved using the second approach in which the tip speed ratio was added to the geometry of the blade as an additional design variable. Results also indicate that the generator resistive torque remarkably puts off the starting of the turbine especially at very high altitudes.

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 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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.816
Threshold uncertainty score0.157

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 head0.007
GPT teacher head0.189
Teacher spread0.182 · 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 teacher head, 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

Citations8
Published2014
Admission routes1
Has abstractyes

Explore more

Same topicWind Energy Research and DevelopmentFrench-language works237,207