## 4 Results and Discussion
One may argue that it should not be expected that similar conclusions would be found when investigating different columns/rows (with or without a gap in between turbines in this case) of wind turbines, wind velocities and slightly different wake models. In that sense similar studies such as the ones performed by [11, 13, 14, 44, 45, 46, 43] may complement each other. In addition, they highlight the strengths and limitations of each model/method and offer to researchers better possibilities to gain a deeper and more integrated insight into the particular usefulness of every model/method under different circumstances and cases. Results similar to those of the ACD (2016) method presented here when using the \(k-\varepsilon\) turbulence model were obtained in [44]. In addition, they showed that their results improved with the \(k-\varepsilon\,f_{p}\) turbulence model. In [44, 47] it was shown with wind measurements and LES that the \(k-\varepsilon\) turbulence closure model is not capable of replicating wakes of isolated wind turbines. Specifically, the \(k-\varepsilon\) turbulence closure model, under-predicts the wake effects due to it being too diffusive. Here the goal is not to investigate the influence of the effect of using different turbulence closer models, rather we are interested in a comparative analysis of two ACD methods and two analytical wake models for a wind farm configuration. Moreover, LES with an ACD and ACL method have been performed by [14, 23] respectively, with quite good accuracy. The researchers in [23] have generally slightly over predicted the measurements whereas results of [14] vary depending on the particular wind turbine examined. While this study has provided a clear comparison between the ACD (2016) and ACD (2008) actuator disc methods and two analytical wake models with experimental data, it is also important to mention its limitations:  
* When presenting the results, the directional uncertainty as proposed by [48] has not been considered. * Yaw misalignment errors are not considered. * The data has not been filtered for stability. Figure 6: Normalized power production for the \(222^{\circ}\) direction (**a**) Column B and (**b**) Column D.  
* The study has been performed only for cases where the wind direction is in-line to the row of wind turbines. * Only the \(k-\varepsilon\) turbulence model is used. * In the simulations one power curve is used; in reality depending on the environmental conditions e.g., turbulence intensity or air density, wind turbines operate on a range of power curves. Despite these limitations, the present study has presented a straightforward comparison among different methods to estimate the power production of a wind farm. Even though the ACDs used in this study are both based on the 1D momentum theory, it is shown that they provide different results as a consequence of how the forces are calculated and distributed over the disc and how the power is estimated. The ACD (2016) method and the Larsen model, in most cases, seem to better estimate the power production in comparison to the other method and model. The Jensen model in particular seems in general to greatly underestimate the power production of the downwind turbines.