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CFD Analysis of a Finite Linear Array of Savonius Wind Turbines

2016· article· en· W2527092497 on OpenAlexaff
Belkacem Belabes, Marius Paraschivoiu

Bibliographic record

VenueJournal of Physics Conference Series · 2016
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsConcordia University
Fundersnot available
KeywordsComputational fluid dynamicsWind powerTurbineRotor (electric)Marine engineeringWind speedInletEnvironmental scienceMechanicsEngineeringMeteorologyPhysicsAerospace engineeringMechanical engineeringElectrical engineering

Abstract

fetched live from OpenAlex

Vertical axis wind turbines such as Savonius rotors have been shown to be suitable for low wind speeds normally associated with wind resources in all corners of the world. However, the efficiency of the rotor is low. This paper presents results of Computational Fluid Dynamics (CFD) simulations for an array of Savonius rotors that show a significant increase in efficiency. It looks at identifying the effect on the energy yield of a number of turbines placed in a linear array. Results from this investigation suggest that an increase in the energy yield could be achieved which can reach almost two times than the conventional Savonius wind turbine in the case of an array of 11turbines with a distance of 1.4R in between them. The effect of different TSR values and different wind inlet speeds on the farm has been studied for both a synchronous and asynchronous wind farm.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.153
Threshold uncertainty score0.298

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 head0.021
GPT teacher head0.234
Teacher spread0.213 · 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 designBench or experimental
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

Citations13
Published2016
Admission routes1
Has abstractyes

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