Large-Eddy Simulation of Wind Turbine Wakes in Forest Terrain

Abstract

In this study, large-eddy simulation was employed to investigate the influence of the forest canopy on wind turbine wakes. Nine forest case studies were carried out with different vertical distributions of leaf area density (LAD) and values of leaf area index (LAI). It was found that the wake in forest canopies recovers at a faster rate when compared with the flat terrain. An interesting observation was the significant reduction in turbulence kinetic energy (TKE) in the lower part of the wake above the forest in comparison with the inflow TKE, which occurred for a wide range of turbine downstream positions. The increase of TKE, on the other hand, was mainly located in the region around the top tip. Analyses of the power spectral density showed that the increase in TKE happened at a certain range of frequencies for the forest canopy cases and at all the examined frequencies for the flat case. Wake meandering was also examined and was found to be of a higher amplitude and a lower dominant frequency for the forest cases compared with the flat case. In terms of the influence of forest canopy parameters, the LAI was found to have an impact greater than the vertical distribution of LAD. Specifically, the wake-added TKE and wake-added Reynolds shear stress were found to be approximately the same for cases with the same LAI, regardless of the vertical distribution of LAD.

Impact of Forest Density on Atmospheric Boundary Layer Flow and Turbulence for Wind Energy Applications

“…The influence of forests on the local Atmospheric Boundary Layer (ABL) has been investigated by many researchers both numerically as well as experimentally. For example, the numerical studies, using either the Reynolds-Averaged Navier Stokes (RANS) or Large-Eddy Simulation (LES) modelling approaches, include but are not limited to Finnigan et al (2009); Sogachev (2009); Sogachev et al (2009Sogachev et al ( , 2012; Nebenführ and Davidson (2014); Boudreault et al (2014); Nebenführ and Davidson (2015); Patton et al (2016); Agafonova et al (2016a,b); Chaudhari et al (2016a); Sogachev and Kelly (2016a); Sogachev et al (2017); Boudreault et al (2017); Nebenführ and Davidson (2017); Desmond et al (2018); Ivanell et al (2018); Adedipe et al (2020); Rodrigo et al (2020); Abedi et al (2021); Sogachev (2021); Cheng et al (2021); Adedipe et al (2022); Chaudhari et al (2023), and Li et al (2023). Additionally, on this topic, several experimental studies have been also carried out (Rannik et al 2003;Pedersen and Langreder 2007;Odemark and Segalini 2014;Desmond and Watson 2014;Dellwik et al 2014;Conan et al 2015;Arnqvist et al 2015;Giometto et al 2017;Kazda et al 2017;Cheng et al 2021;Mo et al 2022).…”

“…More recently, a few studies have investigated the impact of different forest densities on wind energy harvesting (Nebenführ and Davidson 2017;Adedipe et al 2020;Cheng et al 2021;Adedipe et al 2022;Li et al 2023;Su and Paw U 2023). Among this, Nebenführ and Davidson (2017) numerically studied the influence of forests, having two separate LAI (of 4.3 and 2.9) on windturbine fatigue loads and compared that with the case of flat terrain.…”