MétaCan
Menu
Back to cohort
Record W2774088646 · doi:10.5539/mas.v11n12p118

The Rolled-up and Tip Vortices Studies in the CFD Model of the 3-D Swept-Backward Wind Turbine Blades

2017· article· en· W2774088646 on OpenAlexvenueno aff
Sutrisno Sutrisno, Deendarlianto Deendarlianto, Tri Agung Rochmat, Indarto Indarto, Setyawan Bekti Wibowo, Sigit Iswahyudi, Caesar Wiratama, Djatmiko Bagus Maulana Erlambang

Bibliographic record

VenueModern Applied Science · 2017
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsnot available
Fundersnot available
KeywordsVortexStarting vortexHorseshoe vortexTurbineMechanicsPhysicsVortex stretchingTurbine bladeLift (data mining)Vortex ringComputational fluid dynamicsFlow visualizationFlow (mathematics)Computer scienceThermodynamics

Abstract

fetched live from OpenAlex

In this paper, the method to analyze of vortex dynamics simulation of 3-D (three dimensional) backward wind turbine blades is introduced, consisted of flow visualization part and detailed measurement part. With this method, one could explain visually and by calculation the role of 3-D flow vortex mechanism patterns on 3-D backward wind turbine blade, the interchange between kinetic and potential energies, the utilization of very strong vortex, which could lose energy, generate lift, and produce tangential mechanical power. The method could be elucidated by analyzing the appearance of rolled-up vortex effect on the 3-D backward wind turbine blades. A sharp pointed backward blade, generally has a weak tip vortex, may generate a second weak vortex center, and appears due to the rolled-up vortex effect, which is quite difficult to identify. The weakness of tip vortex makes the sharp pointed blade more efficient to exchange energy. Blunt backward turbine blades generally have a strong vortex center, a tip vortex; which in the form of a vortex core. Due to the rolled-up vortex effect, it could generate a second weak vortex center that is clearly visible.

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)0.001
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: Empirical
Teacher disagreement score0.264
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.062
GPT teacher head0.299
Teacher spread0.238 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

Explore more

Same venueModern Applied ScienceSame topicWind Energy Research and DevelopmentFrench-language works237,207