2012
DOI: 10.1002/we.1532
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Unstructured overset incompressible computational fluid dynamics for unsteady wind turbine simulations

Abstract: Overset computational fluid dynamics (CFD) methods are the most sophisticated methods currently available to predict the unsteady motion of wind turbine blades without the need for additional simplifications or restrictions on the turbine operational conditions. An unstructured implementation of the governing equations of motion permits rapid modeling of the salient components, such as nacelles, towers and other localized obstructions of interest. A time‐accurate incompressible formulation accelerates the conv… Show more

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Cited by 31 publications
(9 citation statements)
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“… experienced similar behavior in 2002 also with full turbulent flow on only 1.3 Mio points. Moreover, results of Yelmule and Anjuri or Lynch reveal that transition prediction appears to enable more accurate prediction of the pressure distribution.…”
Section: Resultsmentioning
confidence: 99%
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“… experienced similar behavior in 2002 also with full turbulent flow on only 1.3 Mio points. Moreover, results of Yelmule and Anjuri or Lynch reveal that transition prediction appears to enable more accurate prediction of the pressure distribution.…”
Section: Resultsmentioning
confidence: 99%
“…, Rahimi et al. or Lynch and Smith who use EllipSys3D, OpenFoam or FUN3D, respectively. Even though the latter two codes contain compressible and incompressible formulation , the authors do not investigate the validity of the incompressible assumption.…”
Section: Introductionmentioning
confidence: 99%
“…Lynch and Smith [11] experimentally studied the unstructured over set incompressible CFD simulations of wind turbine using a hybrid RANS/LES turbulence model. A correlated model was predicted.…”
Section: Introductionmentioning
confidence: 99%
“…As such, the RANS approach currently falls short in precisely predicting the shaft torque at stall, as laminar/turbulent boundary layer transition and flow separation have both been shown to have first-order effects on blade aerodynamics predictions [5]. Full-scale computational fluid dynamics (CFD) studies have shown that increasing RANS fidelity for separated flows and wake regions by employing detached-eddy simulation (DES) can significantly improve predictions of blade torque near stall compared with RANS [6][7][8], and the application of the hybrid RANS/LES model yielded nearly identical solutions compared with RANS at low wind speed.…”
Section: Introductionmentioning
confidence: 99%