Preprints
https://doi.org/10.5194/wes-2022-72
https://doi.org/10.5194/wes-2022-72
 
26 Aug 2022
26 Aug 2022
Status: this preprint is currently under review for the journal WES.

Evolution of Eddy Viscosity in the Wake of a Wind Turbine

Ryan Scott1, Luis Martínez-Tossas2, Nicholas Hamilton2, and Raúl B. Cal1 Ryan Scott et al.
  • 1Portland State University Department of Mechanical Engineering, Portland, Oregon, USA
  • 2National Renewable Energy Lab, Golden, Colorado, USA

Abstract. The eddy viscosity hypothesis is a popular method in wind turbine wake modeling for estimating turbulent stresses. We document the downstream evolution of eddy viscosity in the wake of a wind turbine from experimental and large eddy simulation data. Wake eddy viscosity is isolated from its surroundings by subtracting the inflow profile and the driving forces are identified in each wake region. Eddy viscosity varies in response to changes in turbine geometry and nacelle misalignment with larger turbines generating stronger velocity gradients and shear stresses. We propose a model for eddy viscosity based on a Rayleigh distribution curve. Model parameters are obtained from scaling the eddy viscosity hypothesis and demonstrate satisfactory agreement with the reference data. The model is implemented in the curled wake formulation in the FLOw Redirection and Induction in Steady State (FLORIS) framework and assessed through comparisons to the previous formulation. Our approach produced more accurate flow field estimates with lower total error for the majority of cases.

Ryan Scott et al.

Status: open (until 25 Oct 2022)

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Ryan Scott et al.

Ryan Scott et al.

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Short summary
In this work we examine the relationship between wind speed and turbulent stresses within a wind turbine wake. This relationship changes further from the turbine as the driving physical phenomena vary throughout the wake. We propose a model for this process and test the effectiveness of our model against existing formulations. Our approach increases the accuracy of wind speed predictions which will lead to better estimates of wind plant performance and promote more efficient wind plant design.