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Record W4249520023 · doi:10.32920/ryerson.14646213.v1

An investigation into a small wind turbine blade design

2021· preprint· en· W4249520023 on OpenAlexaff
Sayem Zafar

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsAirfoilBlade (archaeology)AerodynamicsTurbine bladeTurbineMarine engineeringWind powerWind speedMechanical engineeringStructural engineeringEngineeringAerospace engineeringMeteorologyPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

The objective of the project was to design a small wind turbine blade which is aerodynamically efficient and easy to manufacture. Preliminary aerodynamic analysis concluded NACA 63-425 to be the most efficient airfoil. Blade geometry was determined after calculating baseline geometric values for low drag which was then used to calculate power. Blade's structural integrity was studied using ANSYS® software. Tested results yielded that a single layer of E-fibreglass-epoxy is good enough to sustain the prescribed loads. The results were used to calculate the total weight of the blade which was then used to determine the start-up speed. Overall the project was successful in designing a wind turbine blade that produced 450 [W] of electrical power at 4[m/s] wind speed with the start-up speed of around 2[m/d]. The project fulfilled its objective which was to design a more effective wind turbine blade with manufacturability in mind.

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 categoriesMeta-epidemiology (narrow)
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 score0.285
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.037
GPT teacher head0.239
Teacher spread0.202 · 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.

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

Citations0
Published2021
Admission routes1
Has abstractyes

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