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Record W2326880541 · doi:10.2514/6.2010-463

Comparison of Potential Flow Wake Models for Horizontal-Axis Wind Turbine Rotors

2010· article· en· W2326880541 on OpenAlexafffund
Shane Cline, Curran Crawford

Bibliographic record

Venue48th AIAA Aerospace Sciences Meeting Including the New Horizons Forum and Aerospace Exposition · 2010
Typearticle
Languageen
FieldEngineering
TopicWind Energy Research and Development
Canadian institutionsUniversity of Victoria
FundersUniversity of Victoria
KeywordsWakeHorizontal axisTurbineFlow (mathematics)Marine engineeringAerospace engineeringWind powerPotential flowComputer scienceMechanicsEnvironmental scienceGeologyPhysicsEngineeringElectrical engineeringStructural engineering

Abstract

fetched live from OpenAlex

Potential ow (PF) methods are a promising alternative to mainstream wind turbine aerodynamics tools such as blade element momentum (BEM) methods and mesh-based computational uid dynamics (CFD) approaches. PF is relatively easy to setup and robust with respect to geometry. The advent of the fast multipole method (FMM) brings computational speed to PF methods. These attributes make PF suitable for integration with multidisciplinary design optimization (MDO) tools. A C++ library employing a Weissinger lifting-line model and several PF wake models has been developed. The library utilizes FMM to accelerate the N-body computation of PF element interactions. This paper compares the numerical accuracy and computational speed of PF wake models according to their compositions of vortex particles, laments, and sheets. A standard three-bladed horizontal-axis wind turbine (HAWT) wind rotor under steady tower-free operation and prescribed elliptic circulation is presented. The e ects of numerical artefacts such as wake discretization, truncation, and FMM tuning parameters are explored. Finally, computational speed enhancement is con rmed.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
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 score0.341
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.023
GPT teacher head0.283
Teacher spread0.260 · 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.

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

Citations16
Published2010
Admission routes2
Has abstractyes

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