## 1 Introduction  
As a wind turbine extracts energy from the wind, it creates a region downstream where the wind velocity is decreased, and the turbulence intensity is increased. This region is commonly called the wake region and represents the effect of the wind turbine on the free flow of the wind. Turbines are currently placed in close configurations for several economic, environmental, and technical reasons [1]. The results of these configurations are that turbines often operate in the wake of other turbines which results in reduced production. In the context of an offshore wind farm this power loss is in the order of 5% to 20% [2]. It is thus apparent that the ability to accurately predict wind turbine wakes has a significant impact on increasing wind farm profitability. This insight can provide value in at least two stages of a wind farm's project lifespan, i.e., the pre-construction phase and the operational phase. In the pre-construction phase the value is added by being able to calculate with higher accuracy the wind flow conditions within the planned wind farm and layouts that decrease wake losses and turbine loads may be designed for. During the operational phase wind farm managers in many cases sacrifice power production in favor of decreasing wind flow related loads to extend the lifetime of the wind turbines. Subsequently, by accurately modelling wind farm wakes, wind farm managers could create better controller strategies per inflow condition that may increase the power production without negatively impacting the operational lifetime of the wind farm. Recently [PERSON] et al. (2017) [3] developed a novel wind farm optimization layout for offshore wind farm micro siting. Traditionally the impact of wakes is accounted for by using analytical wake models such as the [PERSON] [4] and the [PERSON] [5] models. Even though these models are extremely fast in computing the wake deficit of an entire wind farm, they depend on coefficients that need to be empirically determined per case, making them not generically applicable to all sites. An example of one such empirical variable is the wake expansion coefficient of the Jensen model, presented later in Section 2.2. Furthermore, they also suffer from intrinsic simplifications when calculating, for instance, the wake to wake interaction and the turbulence characteristics of the wake [6; 7]. A recent validation of three wind turbine analytical wake models in a complex terrain context using wind farm production data may be found in [8]. Furthermore, three wake models [4; 9; 10] for two wind turbine farms where benchmarked in [11]. A similar validation for the standalone DWM model and the actuator line (ACL) method in OpenFOAM(r)[12] are performed in [13]. The validation is done for the Lillgrund offshore wind farm located in \(\Orcount\)resund, between Sweden and Denmark and the ACL data is provided by [14]. More advanced computational fluid dynamics (CFD) techniques to assess wind turbine wakes are performed by modelling the wind turbine forces in a spatially filtered Navier-Stokes equation e.g., Large Eddy Simulations (LES). Two widely used methods to model the wind turbine forces are the ACL method and the actuator disc (ACD) methods. The ACL method, proposed by [15], involves distributing the calculated forces along rotating lines that represent the blades of an actual wind turbine. Research with LES and the ACL method has been performed by e.g., [16; 17; 18]. The ACD method, in which the wind turbine rotor is represented by distributing the forces over a porous disc, has been investigated with LES by e.g., [19; 20; 21; 22; 23]. Although LES provide high fidelity results compared to field measurements, the computational requirements of the method are far too high and therefore not currently suitable for the engineering requirement of computing wakes for an entire wind farm [14; 22]. A less computationally expensive alternative to LES are the Reynolds averaged Navier-Stokes simulations (RANS).