Abstract
Simulating wind-turbines in Reynolds-averaged Navier-Stokes (RANS) codes is highly challenging, at least partly due to the importance of turbulence anisotropy in the evolution of the wake. We present a preliminary investigation into the role of anisotropy in RANS simulations of vertical-axis turbines, by comparison with LES. Firstly an LES data-set serving as our ground-truth is generated, and verified against previously published works. This data-set provides raw turbulence anisotropy fields for several turbine configurations. This anisotropy is injected into RANS simulations of identical configurations to determine the extent to which it influences (i) the production of turbulence kinetic energy, (ii) the turbulence momentum forcing, and finally (iii) the mean-flow. In all these quantities we observe the anisotropy has a surprisingly limited effect, and is certainly not the leading-order error in Boussinesq RANS for these cases. Nevertheless we go on to show that it is feasible to predict anisotropy fields for unseen configurations based only on the mean-flow, by using a tensorized version of random-forest regression.
Highlights
In studies of wind farm design, control and maintainance, turbine wake evolution is of great interest for turbine loading as well as power extraction
First we verify our Large Eddy Simulations (LES) reference simulations against previous work; secondly we show the effect of directly injecting LES anisotropy bLijES into Reynolds-averaged Navier-Stokes (RANS) simulations of the same flow case; we demonstrate the ability of the TensorBasis Random-Forest (TB-RF) regression model to predict bij for an unseen flow
Conclusions & Recommendations We have quantified the effect of turbulence anisotropy in isolation on the accuracy of RANS models for wind-turbines
Summary
In studies of wind farm design, control and maintainance, turbine wake evolution is of great interest for turbine loading as well as power extraction. Several modelling tools have been developed to simulate turbine wakes under the Atmospheric Boundary Layer (ABL). LES is much too expensive to be used in an engineering context of real-time control, or farm layout optimization. In these areas, simple empirical models such as Jensen [3] and Larsen [4] models dominate. Simple empirical models such as Jensen [3] and Larsen [4] models dominate These are extremely cheap, but include very little physics, relying on sufficient data for fitting, and generally consistent wake behaviour. One consistent deficit is RANS well-known significant overprediction of turbulence eddy-viscosity in the near-wake, and the consequent much-too-rapid wake recovery [6]
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