Wake meandering statistics of a model wind turbine: Insights gained by large eddy simulations

Daniel Foti, Xiaolei Yang, Michele Guala, Fotis Sotiropoulos

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31 Scopus citations

Abstract

Wind tunnel measurements in the wake of an axial flow miniature wind turbine provide evidence of large-scale motions characteristic of wake meandering [Howard, Phys. Fluids 27, 075103 (2015)PHFLE61070-663110.1063/1.4923334]. A numerical investigation of the wake, using immersed boundary large eddy simulations able to account for all geometrical details of the model wind turbine, is presented here to elucidate the three-dimensional structure of the wake and the mechanisms controlling near and far wake instabilities. Similar to the findings of Kang et al. [Kang, J. Fluid Mech. 744, 376 (2014)JFLSA70022-112010.1017/jfm.2014.82], an energetic coherent helical hub vortex is found to form behind the turbine nacelle, which expands radially outward downstream of the turbine and ultimately interacts with the turbine tip shear layer. Starting from the wake meandering filtering used by Howard et al., a three-dimensional spatiotemporal filtering process is developed to reconstruct a three-dimensional meandering profile in the wake of the turbine. The counterwinding hub vortex undergoes a spiral vortex breakdown and the rotational component of the hub vortex persists downstream, contributing to the rotational direction of the wake meandering. Statistical characteristics of the wake meandering profile, along with triple decomposition of the flow field separating the coherent and incoherent turbulent fluctuations, are used to delineate the near and far wake flow structures and their interactions. In the near wake, the nacelle leads to mostly incoherent turbulence, while in the far wake, turbulent coherent structures, especially the azimuthal velocity component, dominate the flow field.

Original languageEnglish (US)
Article number044407
JournalPhysical Review Fluids
Volume1
Issue number4
DOIs
StatePublished - Aug 16 2016

Bibliographical note

Funding Information:
This work was supported by the US Department of Energy (Grants No. DE-EE0002980, No. DE-EE0005482, and No. DE-AC04-94AL85000) and Sandia National Laboratories. Computational resources were provided by Sandia National Laboratories and the University of Minnesota Supercomputing Institute.

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