Preprints
https://doi.org/10.5194/wes-2022-50
https://doi.org/10.5194/wes-2022-50
 
07 Jun 2022
07 Jun 2022
Status: this preprint is currently under review for the journal WES.

Wind turbine wake simulation with explicit algebraic Reynolds stress modeling

Mads Baungaard1, Stefan Wallin2, Maarten Paul van der Laan1, and Mark Kelly1 Mads Baungaard et al.
  • 1DTU Wind Energy, Technical University of Denmark, Risø Campus, Frederiksborgvej 399, 4000 Roskilde, Denmark
  • 2Department of Mechanics, Linné FLOW centre, KTH Royal Institute of Technology, Osquars Backe 18, 10044 Stockholm, Sweden

Abstract. Reynolds-averaged Navier-Stokes (RANS) simulations of wind turbine wakes are usually conducted with two-equation turbulence models based on the Boussinesq hypothesis, which are simple and robust but lack the capability of predicting various turbulence phenomena. Using the explicit algebraic Reynolds stress model (EARSM) of Wallin and Johansson (2000) can aid some of these deficiencies, while still being numerical robust and only slightly more computationally expensive than the traditional two-equation models. The model implementation is verified with the homogeneous shear flow, half-channel flow and square duct flow cases, and subsequently full 3D wake simulations are run and analyzed. The results are compared with reference large eddy simulation (LES) data, which shows that the EARSM especially improves the prediction of turbulence anisotropy and turbulence intensity but that it also predicts less Gaussian shaped wake profiles with the standard settings of the model.

Mads Baungaard et al.

Status: open (until 06 Aug 2022)

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  • RC1: 'Comment on wes-2022-50', Anonymous Referee #1, 28 Jun 2022 reply

Mads Baungaard et al.

Mads Baungaard et al.

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Short summary
Wind turbine wakes in the neutral atmospheric surface layer are simulated with RANS using an explicit algebraic Reynolds stress model. Contrary to standard two-equation turbulence models, it can predict turbulence anisotropy and complex physical phenomena like secondary motions. For the cases considered, it improves Reynolds stress, turbulence intensity and velocity deficit predictions, although a more top-hat shaped profile is observed for the latter.