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Record W2335997940 · doi:10.5539/mas.v10n5p132

The Performance & Flow Visualization Studies of Three dimensional (3-D) Wind Turbine Blade Models

2016· article· en· W2335997940 on OpenAlexvenueno aff
Sutrisno Sutrisno, Prajitno, Purnomo Purnomo, B. W. Setyawan

Bibliographic record

VenueModern Applied Science · 2016
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsnot available
Fundersnot available
KeywordsTurbineTurbine bladeAirfoilFlow visualizationLaminar flowChord (peer-to-peer)Stall (fluid mechanics)Flow (mathematics)Wind tunnelBlade (archaeology)Marine engineeringStructural engineeringComputer scienceMechanicsEngineeringAerospace engineeringPhysics

Abstract

fetched live from OpenAlex

<p>The researches on the design of 3-D wind turbine blades have been received less attention so far even though 3-D blade products are widely sold. In the opposite, advanced researches in 3-D helicopter blade have been studied rigorously. Researches in wind turbine blade modeling are mostly assumed that blade span wise sections behaves as independent two dimensional (2-D) airfoils, implying that there is no exchange of momentum in the span wise direction. Further more flow visualization experiments are infrequently conducted.</p><p>The purpose of this study is to investigate the performance of 3-D wind turbine blade models with backward-forward swept and verify the flow patterns using flow visualization. In this research, the blade models are constructed based on the twist and chord distributions following Schmitz’s formula. Forward and backward swept are added to the wind turbine blades. It is hoped that the additional swept would enhance or diminish outward flow disturbance or stall development propagation on the span wise blade surfaces to give better blade design.</p><p>The performance of the 3-D wind turbine system models are measured by a torque meter, employing Prony’s braking system, and the 3-D flow patterns around the rotating blade models are investigated applying “tuft-visualization technique”, to study the appearance of laminar, separated and boundary layer flow patterns surrounding the 3-dimentional blade system.</p>For low speed wind turbines, Dumitrescu and Cardos (2011) have identified that stall spreads from the root of the rotating blade. In this study, it is found that for blades with (i) forward swept tip and backward swept root, the initial stall at the blade bottom would be amplified by concurrent strengthening flow due to the backward swept root to create strong stall spreading outward, and therefore the blades gives lower performance. For blades with (ii) backward swept tip and forward swept root, the initial stall at the blade bottom would be weakened by opposite weakening flow due to the forward swept root, generate weak stall that tend to deteriorate. These blades have better performance.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.281
Threshold uncertainty score0.222

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 head0.038
GPT teacher head0.262
Teacher spread0.224 · 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

Citations7
Published2016
Admission routes1
Has abstractyes

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