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Comparative CFD study of the effect of the presence of downstream turbines on upstream ones using a rotational speed control system

2014· article· en· W2056318422 on OpenAlexaff
S-P Breton, K. Nilsson, Stefan Ivanell, Hugo Olivares-Espinosa, 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
Fundersnot available
KeywordsTurbineComputational fluid dynamicsMarine engineeringInflowUpstream (networking)MechanicsWind powerOffshore wind powerTurbulenceTorqueRotor (electric)Rotational speedEnvironmental sciencePhysicsAerospace engineeringMechanical engineeringEngineering

Abstract

fetched live from OpenAlex

The effect of a downstream turbine on the production of a turbine located upstream of the latter is studied in this work. This is done through the use of two CFD simulation codes, namely OpenFOAM and EllipSys3D, which solve the Navier-Stokes equations in their incompressible form using a finite volume approach. In both EllipSys3D and Open Foam, the LES (Large Eddy Simulation) technique is used for modelling turbulence. The wind turbine rotors are modelled as actuator disks whose loading is determined through the use of tabulated airfoil data by applying the blade-element method. A generator torque controller is used in both simulation methods to ensure that the simulated turbines adapt, in terms of rotational velocity, to the inflow conditions they are submited to. Results from both simulation codes, although they differ slightly, show that the downstream turbine affects the upstream one when the spacing between the turbines is small. This is also suggested to be the case looking at measurements performed at the Lillgrund offshore wind farm, whose turbines are located unusually close to each other. However, for distances used in today's typical wind farms, this effect is shown by our calculations not to be significant.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.808
Threshold uncertainty score0.264

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 head0.018
GPT teacher head0.246
Teacher spread0.228 · 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 designBench or experimental
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 routes1
Has abstractyes

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