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Record W2806308454 · doi:10.1139/tcsme-2017-0093

Computational fluid dynamic analysis of roof-mounted vertical-axis wind turbine with diffuser shroud, flange, and vanes

2018· article· en· W2806308454 on OpenAlexaffvenue
Hamza Abu-Thuraia, Cevdet Aygün, Marius Paraschivoiu, Marc Alexandre Allard

Bibliographic record

VenueTransactions of the Canadian Society for Mechanical Engineering · 2018
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsConcordia University
Fundersnot available
KeywordsShroudVertical axis wind turbineTurbineMarine engineeringWind powerComputational fluid dynamicsDiffuser (optics)Wind speedTip-speed ratioTidal powerWind directionHypersonic wind tunnelEnvironmental scienceEngineeringMeteorologyMechanical engineeringAerospace engineeringWind tunnelPhysicsElectrical engineeringOptics

Abstract

fetched live from OpenAlex

Advances in wind power and tidal power have matured considerably to offer clean and sustainable energy alternatives. Nevertheless, distributed small-scale energy production from wind in urban areas has been disappointing because of very low efficiencies of the turbines. A novel wind turbine design — a seven-bladed Savonius vertical-axis wind turbine (VAWT) that is horizontally oriented inside a diffuser shroud and mounted on top of a building — has been shown to overcome the drawback of low efficiency. The objective this study was to analyze the performance of this novel wind turbine design for different wind directions and for different guide vanes placed at the entrance of the diffuser shroud. The flow field over the turbine and guide vanes was analyzed using computational fluid dynamics (CFD) on a 3D grid for multiple tip-speed ratios (TSRs). Four wind directions and three guide-vane angles were analyzed. The wind-direction analysis indicates that the power coefficient decreases to about half when the wind is oriented at 45° to the main axis of the turbine. The analysis of the guide vanes indicates a maximum power coefficient of 0.33 at a vane angle of 55°.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.498
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.006
GPT teacher head0.206
Teacher spread0.201 · 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

Citations2
Published2018
Admission routes2
Has abstractyes

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