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Record W4396213977 · doi:10.2316/j.2023.203-0450

NUMERICAL AND EXPERIMENTAL STUDY ON THE EFFECT OF OVERLAP ON SAVONIUS WIND TURBINE, 1-9.

2023· article· en· W4396213977 on OpenAlexvenueno aff
Zaki Alomar, Gerard Khoury, Jihad Rishmany, Michel Daaboul

Bibliographic record

VenueInternational Journal of Power and Energy Systems · 2023
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsnot available
FundersConseil National de la Recherche ScientifiqueCentre National de la Recherche Scientifique
KeywordsTurbineMarine engineeringEnvironmental scienceMathematicsEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

Wind as a renewable energy source is not yet fully exploited despite the permanent availability of this source.Moreover, in countries where renewable energy regulations are still absent, largescale applications are still not available.The only wind turbines implemented are small-scaled applications owed to individual contributions.In this context, the Savonius wind turbine seems to be the most suitable choice at this scale because of its relatively low noise level, ease of manufacturing and maintenance, and self-starting aspect even at low wind speeds.However, the major drawback of such a turbine is its relatively low efficiency.In this framework, this study aims at assessing the various design parameters (number of blades, height-to-diameter ratio, and overlap ratio) of a Savonius wind turbine in an attempt to increase its efficiency.This is realised via computational fluid dynamics simulations followed by experimental validation through wind tunnel testing.Results show that the optimum configuration consists of a two-blade S-rotor with around 18% overlap.Future improvements could include shielding the returning blade to increase the torque.

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 score0.667
Threshold uncertainty score0.237

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 head0.009
GPT teacher head0.256
Teacher spread0.247 · 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
Published2023
Admission routes1
Has abstractyes

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