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Turbulence characteristics in a free wake of an actuator disk: comparisons between a rotating and a non-rotating actuator disk in uniform inflow

2014· article· en· W2010419836 on OpenAlexafffund
Hugo Olivares-Espinosa, S-P Breton, C. Masson, Louis Dufresne

Bibliographic record

VenueJournal of Physics Conference Series · 2014
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsÉcole de Technologie Supérieure
FundersConsejo Nacional de Ciencia y TecnologíaCanada Research Chairs
KeywordsWakeInflowTurbulenceTurbineMechanicsRotor (electric)PhysicsAirfoilDetached eddy simulationWind tunnelActuatorAerospace engineeringComputational fluid dynamicsMarine engineeringEngineeringReynolds-averaged Navier–Stokes equationsElectrical engineering

Abstract

fetched live from OpenAlex

An Actuator Disk (AD) model is implemented in the CFD platform OpenFOAM® with the purpose of studying the characteristics of the turbulent flow in the wake of the rotor of a horizontal-axis wind turbine. This AD model is based on the blade-element theory and it employs airfoil data to calculate the distribution of forces over the disk of a conceptual 5 MW offshore wind turbine. A uniform, non-turbulent flow is used as inflow so the turbulence is only produced in the wake of the AD. Computations are performed using Large-Eddy Simulations (LES) to capture the unsteady fluctuations in the flow. Additionally, a classic Smagorinsky Sub-Grid Scale (SGS) technique is employed to model the unfiltered motions. This new AD implementation makes use of a control system to adjust the rotational velocity of the rotor (below rated power) to the local conditions of the wind flow. The preliminary results show that the wake characteristics are influenced by the force distribution on the disk when compared to the wake produced by a uniformly loaded AD. Also, we observe that the simulated rotor reacts correctly to the introduction of the control system, although operating below the optimal power.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.689
Threshold uncertainty score0.662

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.238
Teacher spread0.222 · 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 designObservational
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

Citations8
Published2014
Admission routes2
Has abstractyes

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