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Record W4406802305 · doi:10.1063/5.0248565

The effects of wingtip modifications on the wake of horizontal axis wind turbines

2025· article· en· W4406802305 on OpenAlexafffund
Khashayar RahnamayBahambary, Alexandra Komrakova, Brian A. Fleck

Bibliographic record

VenuePhysics of Fluids · 2025
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversity of Alberta
FundersCanada First Research Excellence Fund
KeywordsPhysicsWakeMechanicsAerospace engineeringClassical mechanics

Abstract

fetched live from OpenAlex

We study the impact of a novel wingtip modification on the wake dynamics of a 10-MW horizontal axis wind turbine using delayed detached eddy simulation. We considered a baseline turbine without wingtip modifications and a turbine equipped with winglets. The results reveal that the winglet significantly alters the near and mid wake regions, increasing the velocity deficit and reducing turbulence intensity in the near wake while minimally affecting the far wake beyond the distance of nine rotor diameters. The vortex rings from the blade tips decay faster in the wake of the modified turbine, reducing the wake energy and vorticity. The budget of mean kinetic energy transport shows that the winglet reduces the turbulence production in the near wake while increasing the turbulence convection and production in the far wake region. To study the meandering in the far wake of two turbine configurations, the dominant Strouhal number and the standard deviation of the wake center were studied. The results indicated that the winglet does not notably affect the amplitude of the wake meandering. Furthermore, the winglet increased turbine power production by 4.5% and thrust by 1.5% while reducing power and torque fluctuations by 10%. Although the winglet affected near wake dynamics, its influence on the far wake is minimal, suggesting potential benefits for wind farm design where turbines are not closely spaced.

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 score0.207
Threshold uncertainty score0.173

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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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