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Record W4312127458 · doi:10.3390/en15249365

Self-Starting Characteristics and Flow-Induced Rotation of Single- and Dual-Stage Vertical-Axis Wind Turbines

2022· article· en· W4312127458 on OpenAlexafffund
Muhammad Saif Ullah Khalid, David Wood, Arman Hemmati

Bibliographic record

VenueEnergies · 2022
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversity of CalgaryUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTurbineWind powerWakeRotor (electric)MechanicsRotation (mathematics)Flow (mathematics)Vertical axis wind turbineVortexAngular velocityWind shearVertical axisTurbine bladeAerospace engineeringWind speedEnvironmental sciencePhysicsMeteorologyEngineeringMechanical engineeringGeometryClassical mechanicsElectrical engineeringMathematics

Abstract

fetched live from OpenAlex

Despite offering promising opportunities for wind energy harvesting in urban environments, vertical axis wind turbines face limitations in terms of poor starting characteristics. In this study, we focus on analyzing improvements offered by dual-stage turbines for a range of wind velocities. Numerical simulations are performed for different phase angles between the rotors (a measure of relative angular positions of the blades in the two rotors) to quantify the response time for their starting behavior. These simulations rely on a through sliding mesh technique coupled with flow-induced rotations. We find that for U∞=4m/s, the phase angles of 30∘ and 90∘ substantially reduce starting time in comparison to a single-stage turbine. Dual-stage turbines with a phase angle of 90∘ exhibit similar or better starting behavior for other wind speeds. The phase angle of 0∘ in double-rotor turbines shows the poorest starting response. Moreover, it is revealed that stabilization of shear layers generated by the blades passing through the windward side of the turbine, vortex-entrapment by these rotating blades, and suppressing of flow structures in the middle of the wake enhance the capacity of VAWTs to achieve faster steady angular speed.

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 score0.384
Threshold uncertainty score0.398

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 head0.014
GPT teacher head0.210
Teacher spread0.196 · 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

Citations10
Published2022
Admission routes2
Has abstractyes

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