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Record W2896121531 · doi:10.1109/irsec.2017.8477247

Comparison of BEM and Full Navier-Stokes CFD Methods for Prediction of Aerodynamics Performance of HAWT Rotors

2017· article· en· W2896121531 on OpenAlexaff
Abdelhamid Bouhelal, Arezki Smaïli, Ouahiba Guerri, Christian Masson

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsÉcole de Technologie Supérieure
FundersMinistère de l'Education Nationale, de l'Enseignement Superieur et de la Recherche
KeywordsReynolds-averaged Navier–Stokes equationsComputational fluid dynamicsAerodynamicsRotor (electric)Wind tunnelMechanicsFlow (mathematics)Reynolds numberComputer scienceAerospace engineeringMarine engineeringEngineeringMechanical engineeringPhysicsTurbulence

Abstract

fetched live from OpenAlex

The essential contribution of this study consists of comparing between two radically different aerodynamic methods which were applied to predict the aerodynamic performance of horizontal axis wind turbines (HAWTs). The classical blade element momentum theory (BEM) and full rotor geometry computational fluid dynamics (CFD) based on the Reynolds Averaged Navier-Stokes (RANS) approach were used in order to discover their strengths and weaknesses for a range of wind speeds where the flow over the rotor varied from fully attached flow to massively separated flow (i.e. Tip Speed Ratio, TSR = 4-10). New MEXICO measurements (Model rotor EXperiments In COntrolled conditions) from German-Dutch wind tunnel (DNW) which were recently carried out between June and July 2014 are used for validating and testing the both BEM and CFD codes. In general, it was founded that, RANS-CFD simulations give good agreements with uniform accuracy level in all studied cases, whereas BEM calculations give reasonable results only at low wind speeds and it fails at higher wind speeds due to the separated flow conditions.

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 score0.566
Threshold uncertainty score0.239

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 head0.041
GPT teacher head0.366
Teacher spread0.325 · 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

Citations19
Published2017
Admission routes1
Has abstractyes

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