# Effects of Turbulence Modeling on the Simulation of Wind Flow over Typical Complex Terrains
## 5 Conclusions  
In this paper, the effects of different RANS turbulence models on the accuracy of the wind flow prediction over complex terrain were investigated, and two modified RANS turbulence models were proposed. In order to find the most suitable turbulence model for simulating the wind flow over complex terrain, the conventional standard SST \(k\)-\(\omega\) model, the modified SST \(k\)-\(\omega\) model, and the standard RSM model and the modified RSM model have been investigated. Four experimental cases were used to validate the numerical results, and several statistical error indices, such as the MAE and RRMSE, were used to evaluate the numerical results. This paper focused on the following two main aspects: one was the effect of different turbulence models on the maintenance of the inflow turbulent kinetic energy and wind speed, and the other was the predictive ability of the wind speed and turbulent kinetic energy or turbulence intensity along the stream direction and vertical direction for the different turbulence models. The main conclusions of this paper are summarized as follows:  
1. The differences in the maintenance of the incoming flow by the four turbulence models are mainly present near the ground. For the inflow wind velocity, the mean velocity profiles with the different turbulence models are almost the same and are consistent with the inlet condition; for the inflow TKE or TI, the standard SST \(k\)-\(\omega\) model, the modified SST \(k\)-\(\omega\) model, and the standard RSM model all underestimate near the ground, and the maximum relative error with the experiment can reach 44.3%. Turbulence is well maintained in both of the modified RSM models, i.e., the TKE decays more slowly when the \(C_{\mu}\) is reduced from 0.09 to 0.05, which is also consistent with the literature [27]. 2. For hilly terrain (e.g., Cases 1-2), the differences in the wind speed predictions by the RANS turbulence models are mainly on the leeward slope, where the maximum differences in the RRMSE values among the four models can reach 11%, with smaller differences on the windward slope and hilltop. For cliff terrain (e.g., Cases 3-4), the larger differences in the wind speed prediction by each RANS model are also near the separation zone. For the prediction of the turbulent kinetic energy, the differences among the turbulence models mainly come from the maintenance of the inflow and the predictive ability of the RANS model itself for the separation flow, and the maximum difference in the RRMSE can reach 47%. After comparing the error indices, it is found that the RSM models, which are based on the anisotropy assumption, are superior to the two-equation models, among which the Mod RSM model is better than the standard RSM model. Therefore, the Mod RSM model is recommended to be used for wind flow prediction in complex terrain, through which a better prediction accuracy can be obtained. 3. Through Case 1, it was also found that the separation zone of the Mod RSM model is more consistent with the experimental results; the standard SST \(k\)-\(\omega\) model and the standard RSM model usually have earlier separation points, resulting in a larger separation zone, and the Mod SST \(k\)-\(\omega\) model has a slight larger separation zone. In this study, through the validation of different types of experimental cases, from the wind tunnel to field environment, from small scale to large scale, and the LES results, it is concluded that the Mod RSM model should be preferentially used for the numerical simulation of the wind flow over complex terrain. This is because an adverse pressure gradient is encountered on the leeward slope of the hill, where the nonequilibrium characteristic of the turbulence is prominent and the turbulence is anisotropic. In addition, if the wind energy project focuses only on the wind flow at the hilltop, the Mod SST \(k\)-\(\omega\) model can be used, which has a reduced computer cost and can achieve a precise prediction.