## 3 Microscale CFD Numerical Simulation
### Computational Domain and Meshing  
To assure the full development of the wake flow of the wind turbine [40; 41], the computational domain was determined as 12D \(\times\) 5D \(\times\) 5D (flow direction \(X\)\(\times\) extension direction \(Y\)\(\times\) vertical direction \(Z\), and D is the diameter of the wind wheel). The wind turbine was placed 3D to the entrance of the computational domain. To achieve satisfying computational efficiency and accuracy [42; 43], due to the complicated blade appearance, the hybrid grid discrete form was applied to divide the whole computational domain into the internal and external parts. The local encrypted region covered the wind turbine model, which used the non-structured meshing. Non-structured meshing produces a tetra/mixed tetrahedral mesh in ICEM CFD automatically by Robust (Octree). The peripheral region had a regular shape that used the high-quality structured meshing. The grid number, grid quality, and windward pressure coefficient of the tower body under different meshing schemes are shown in Table 5. Grid quality includes the minimum orthogonal quality of the grids and grid skewness. The minimum orthogonal quality of the grids shall be larger than 0.1, and it is best to be higher than 0.2. No negative volume is allowed. The grid skewness shall be lower than 0.95, and it is suggested to be lower than 0.9 [44]. It can be seen from Table 5 that with the increase of the total number of grids, the grid quality and windward pressure coefficient converged gradually. No significant differences were observed in grid quality and computational results under a 9.3 million meshing program and a 28.4 million meshing program. Considering computational accuracy and efficiency, this paper chose the 9.3 million meshing program. When processing flow in the near-wall region, the standard wall function was applied to connect the physical variables on the wall and the unknown variables in the turbulence core region directly. In this paper, the cooperation of the standard wall function and standard \(k\)-\(\epsilon\) model (which will be introduced in Section 3.3) can increase the simulation accuracy more significantly. Essentially, the flowing conditions in the turbulence core region were solved by the \(k\)-\(\epsilon\) model. The flow in the wall surface was not solved. Instead, the physical parameters (e.g., speed) of this region were solved by a semi-empirical formula directly. Since the near-wall flow was processed by the standard wall function in this paper, no encryption of the wall surface was needed during meshing, and it only has to place the first internal node in the logarithmic law region. The calculated results demonstrated that the \(y\)+ of the model wall could assure bottom grids in the logarithmic law, which met common engineering requirements. The meshing schemes of the overall computational domain and model are shown in Figure 8. Both the number of grids and grid quality met the requirements of calculation.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Position/\%** & **Blade** & **Chord** & **Installation** & **Blade Pitch** & **Position/\%** & **Blade** & **Chord** & **Installation** & **Blade Pitch** \\  & **Span/m** & **Length/m** & **Angle/\({}^{o}\)** & **Angle/\({}^{o}\)** & **Position/\%** & **Span/m** & **Length/m** & **Angle/\({}^{o}\)** & **Angle/\({}^{o}\)** \\ \hline
5 & 3 & 2.9 & 0.823 & 37.14 & 55 & 33 & 1.95 & 0.169 & \(-\)0.293 \\
10 & 6 &