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Record W3120198202 · doi:10.54966/jreen.v18i4.539

CFD study of a horizontal axis wind turbine NREL Phase II

2023· article· en· W3120198202 on OpenAlexaff
Riyadh Belamadi, Ramzi Mdouki, Adrian Ilinca, Abdelouaheb Djemili

Bibliographic record

VenueJournal of Renewable Energies · 2023
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversité du Québec à Rimouski
Fundersnot available
KeywordsTurbineAerodynamicsTurbulenceComputational fluid dynamicsAirfoilMechanicsRotor (electric)Marine engineeringWind speedAerospace engineeringEngineeringMeteorologyMechanical engineeringPhysics

Abstract

fetched live from OpenAlex

The present work aims to study the aerodynamic characteristics of the NREL phase II (generated only with S809 profile along the span for an untwisted case) rotor that is a horizontal axis downwind wind turbine rotor and which is assumed to stand isolated in the space. The two dimensional steady-incompressible flow Reynolds average NavierStokes equations, are solved by using the commercial CFD package Ansys Fluent. The 2D computations are first performed on S809 airfoil in order to define the most suitable model to be used; the turbulence closure model has been chosen among four possible candidates (standard , Spalart-Allmaras, based on comparison of pressure coefficient for the different configurations with experimental results. Secondly, through a three dimensional study we tried to simulate the experiment for wind speed velocities of 7.2, 10.56, 12.85, 16.3, and 9.18m/s. Results of pressure and torque for considered wind turbine rotor have been directly compared to the available experimental data. The comparisons show that CFD results along with the turbulence model used can predict the span-wise loading of the wind turbine rotor with reasonable agreement. The work presented here is the first stage of project that aims at giving a better understanding of the main influence of the rotational effect on boundary layer separation, and identify the stalled configuration in order to control this latter in future work.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.857
Threshold uncertainty score0.515

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 head0.017
GPT teacher head0.261
Teacher spread0.245 · 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

Citations2
Published2023
Admission routes1
Has abstractyes

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