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Record W4237257228 · doi:10.29252/jafm.11.06.28925

Effect of Grid Topology on Numerical Simulations of Flow Fields around Wind Turbine Nacelle Anemometer

2018· article· en· W4237257228 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.

Bibliographic record

VenueJournal of Applied Fluid Mechanics · 2018
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsÉcole de Technologie Supérieure
Fundersnot available
KeywordsNacelleAnemometerTurbineReynolds-averaged Navier–Stokes equationsTurbulenceWakeUnstructured gridMechanicsGridComputational fluid dynamicsAerospace engineeringComputer scienceMeteorologyPhysicsEngineeringGeometryMathematics

Abstract

fetched live from OpenAlex

In this paper, the effect of mesh topology on the numerical predictions of the immediate near wake region of a horizontal axis wind turbine is investigated. The present work focuses on the nacelle anemometry measurements. Steady Reynolds Averaged Navier-Stokes (RANS) equations are applied to describe the airflow around the wind turbine nacelle. The k-ε turbulence model is used. To model the turbine rotor, the approach based on the actuator disc concept is considered. The computational domain has been meshed with five different configurations of grid; namely, quasi-structured, unstructured and three different hybrid grids constituted of blending of quasi-structured and unstructured grids. The obtained results are compared to the available experimental data. The hybrid mesh with quasi-structured grid in the boundary layer region and unstructured grid in the vicinity of the nacelle is found to be more promising to simulate the near wake generated downstream of the wind turbine nacelle and to predict accurately the nacelle anemometry measurements.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.395

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 head0.007
GPT teacher head0.237
Teacher spread0.229 · 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