MétaCan
Menu
Back to cohort
Record W1984369129 · doi:10.1155/2011/714146

Assessment of Turbulence Models for Flow Simulation around a Wind Turbine Airfoil

2011· article· en· W1984369129 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueModelling and Simulation in Engineering · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicWind and Air Flow Studies
Canadian institutionsUniversité du Québec à RimouskiPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsAirfoilTurbineTurbulenceMarine engineeringAerospace engineeringMechanicsFlow (mathematics)Wind powerEnvironmental sciencePhysicsEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

This investigation focuses on the application of the computational fluid dynamics tool FLUENT to the study of flows over the NACA 63–415 airfoil at various angles of attack. With the aim of selecting the most suitable turbulence model to simulate flow around ice‐accreted airfoils, this work concentrates on assessing the prediction capabilities of various turbulence models on clean airfoils at the large angles of attack that cause highly separated flows to occur. The study was undertaken by conducting simulations with the one‐equation Spalart‐Allmaras (SA) model, the two‐equation RNG k ‐ ϵ and SST k ‐ ω models, and the Reynolds stress model (RSM). Domain discretization was carried out using general quadrilateral grids generated with GAMBIT, the FLUENT preprocessing tool. Comparisons were made with available experimental data.

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.

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: none
Teacher disagreement score0.547
Threshold uncertainty score0.406

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 head0.034
GPT teacher head0.245
Teacher spread0.211 · 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