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Record W2130710085 · doi:10.1002/we.472

Computational fluid dynamics simulation of the aerodynamics of a high solidity, small‐scale vertical axis wind turbine

2011· article· en· W2130710085 on OpenAlexafffund
Kevin W. McLaren, Stephen Tullis, Samir Ziada

Bibliographic record

VenueWind Energy · 2011
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsMcMaster University
FundersSandia National LaboratoriesUniversity of WaterlooOntario Centres of Excellence
KeywordsSolidityStall (fluid mechanics)AirfoilAerodynamicsTurbineMechanicsComputational fluid dynamicsVertical axis wind turbineTip-speed ratioVortexBlade element momentum theoryTurbine bladeVortex sheddingReynolds numberRotational speedPhysicsAerospace engineeringEngineeringComputer scienceClassical mechanicsTurbulence

Abstract

fetched live from OpenAlex

ABSTRACT A computational fluid dynamics simulation was performed for a small‐scale, high solidity (σ = 0.48) H‐type Darrieus vertical axis wind turbine. Two‐dimensional unsteady Reynolds‐averaged Navier–Stokes equations were solved for the turbine numerical model, which has a large stationary domain and smaller rotating subdomain connected by a sliding mesh interface. The simulation results were first validated against steady‐state airfoil data. The model was then used to solve for three rotating blades with constant ambient flow velocity (Re = 360,000) over numerous blade speed ratios. The high solidity and the associated low blade speed ratio and rotational speed of the turbine result in complex flow–blade interaction mechanisms. These include dynamic stall resulting in vortex shedding, vortex impingement on the source blade and significant flow momentum extraction causing reduced power production from the downstream blade pass. Copyright © 2011 John Wiley & Sons, Ltd.

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: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

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 studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0010.000
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 head0.015
GPT teacher head0.202
Teacher spread0.187 · 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

Citations117
Published2011
Admission routes2
Has abstractyes

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