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Record W2912875365 · doi:10.5539/mas.v13n2p240

Numerical Analysis on Aerodynamic Performance of Counter-rotating Wind Turbine through Rear Rotor Configuration

2019· article· en· W2912875365 on OpenAlexvenueno aff
Verdy A. Koehuan, Sugiyono Sugiyono, Samsul Kamal

Bibliographic record

VenueModern Applied Science · 2019
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsnot available
Fundersnot available
KeywordsRotor (electric)TurbineAerodynamicsMechanicsVortexComputational fluid dynamicsTip-speed ratioPhysicsMaterials scienceThermodynamics

Abstract

fetched live from OpenAlex

Numerical analysis was conducted on the aerodynamic performance and the flow characteristics around the counter-rotating wind turbine or CRWT blade through rear rotor configuration using various rotor diameter ratios and distance ratios to the turbine blade through a CFD (Computational Fluid Dynamics) simulation. CFD simulation showed the normalized power coefficients of the front rotor, rear rotor, and combined rotor (CRWT) to the single rotor with a strong influence of the rear rotor configuration with the addition of tip speed ratio (TSR). A larger average normalized power coefficient takes place at D1/D2=1.0 with L/D1=0.75 by 1.221. It is about 22.1% increased to the SRWT for the given TSR range. Axial velocity contours and resultant velocity vectors around the CRWT blade with a diameter ratio of D1/D2 > 1.0 and a closer rotor distance provide a stronger bound vortex and strong separation around the rear hub blade with a tendency to increase from the hub to the tip blade at low TSR. The higher the TSR, the movement of tip vortex moves closer to the rear tip blade which has the effect of increasing the leakage flow in the area of D1/D2 < 1.0.

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 score0.280
Threshold uncertainty score0.399

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.225
Teacher spread0.217 · 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
Published2019
Admission routes1
Has abstractyes

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