URANS simulations of a horizontal axis wind turbine under stall condition using Reynolds stress turbulence models. (15th December 2020)

• Record Type: Journal Article
• Authors: Amiri, Mojtaba Maali; Shadman, Milad; Estefen, Segen F.
• Is Part Of: Energy. Volume 213(2020)
• Publication Date: 2020-12-15
• Subjects: Wind turbine aerodynamics -- URANS equations -- Reynolds stress turbulence models -- Modeling accuracy -- NREL phase VI; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2020.118766
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: Over the last decade, a dramatic increase in the size of commercial wind turbines is noticeable. The optimal structural design and reliable fatigue-life prediction of these large-scale structures depend on the accurate modeling of the turbulent flow around the rotors. The present paper aims at extending the knowledge of the turbulent flow characteristics around horizontal axis wind turbines and assessing the performance of several URANS turbulence models, principally Reynolds stress turbulence (RST) models, in predicting the wind turbine aerodynamics under stall condition. The simulations are performed using the NREL phase VI wind turbine over a wide range of wind speeds. Three RST models and the linear and quadratic variants of the k − ω SST turbulence model are examined. The results demonstrate that the RST models generally provide more reliable predictions. An evaluation of the Boussinesq hypothesis questions the applicability of the turbulence models employing this hypothesis to the wind turbine aerodynamic problems. Finally, the elliptic blending RST model, which is one of the latest formulations of the RST models, appears to provide the best trade-off between accuracy and computational cost. Highlights: The RST models generally provide more reliable predictions compared to the SST models. The models employing the Boussinesq hypothesis are inadequate. Almost all the models resolve favorably the frequency spectrum of a local variable. Nearly all the models fail to predict adequately the frequency spectrum of a global variable. The ERST model appears to provide the best trade-off between accuracy and cost.