Additional thesis with a size of 10 European Credits. The increasing demand for wind energy has led to the development of more efficient wind turbines. A common way to increase the performance of wind turbines is the use of vortex generators. Vortex generators are small devices that are mounted on the turbine blade and they create a stream wise vortex. The vortex transports high momentum fluid towards the surface of the blade, delaying the flow separation. Although vortex generators improve the efficiency of the blade by preventing flow separation, they also create additional drag. This drag can be reduced by using micro vortex generators, which have a height that is smaller than the boundary layer thickness. The positive effect of these micro vortex generators is more local, due to the weaker vortex they create. This means that their effects on the flow should be predicted with more precision to place them in the correct location upstream of the separation. The flow affected by a micro vortex generator can be computed by means of a RANS simulation. A three-dimensional RANS simulation has been performed on a micro vortex pair, using different RANS turbulence models: the standard k-epsilon model, the k-omega-SST model, the Launder Gibson (LG) Reynolds Stress Transport Model and the Speziale Sarkar Gatski (SSG) Reynolds Stress Transport Model. The first two models are eddy viscosity models, which are computationally less expensive than the Reynolds Stress Transport Models (RSTM). The results from the numerical simulations are compared to available experimental data, which include the mean velocity components and the Reynolds stresses. In terms of computational time simulation with the k-omega-SST model was the fastest, whereas the two Reynolds Stress Transport Model simulations were 40% slower. The mean flow field and the decay of the vortex as predicted by the k-omega-SST model and the two Reynolds Stress Transport Models were in agreement with the experimental data. The standard k-epsilon model predicts the wrong shape for the vortex. When the turbulent kinetic energy is compared to the experimental data, all models fail to predict the correct profiles close to the leading edge of the vortex generators. Further downstream the prediction for the turbulent kinetic energy made by the RSTM correspond better with the experimental data than the eddy viscosity models. The degree of Reynolds stress anisotropy is also considered, by looking at the two invariants of the Reynolds stress anisotropy tensor. The eddy viscosity models underpredict the Reynolds stress anisotropy, whereas the RSTM slightly overpredict it, compared to the experimental results. The distribution of Reynolds stress in the flow domain is predicted best by the RSTM. It is concluded that when the mean flow features are of interest the best choice is the k-omega-SST model, because the computation time of this model is less than the RSTM, but the mean flow is predicted with almost the same accuracy. The RSTM predicts the Reynolds stresses the most precise, but there are still some differences with the experimental results that could be improved. The use of the k-epsilon model is not advised.