the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Largeeddy simulation of airborne wind energy farms
Thomas Haas
Jochem De Schutter
Moritz Diehl
The future utilityscale deployment of airborne wind energy technologies requires the development of largescale multimegawatt systems. This study aims at quantifying the interaction between the atmospheric boundary layer (ABL) and largescale airborne wind energy systems operating in a farm. To that end, we present a virtual flight simulator combining largeeddy simulations to simulate turbulent flow conditions and optimal control techniques for flight path generation and tracking. The twoway coupling between flow and system dynamics is achieved by implementing an actuator sector method that we pair to a model predictive controller. In this study, we consider groundbased power generation pumpingmode AWE systems (liftmode AWES) and onboard power generation AWE systems (dragmode AWES). The aircraft have wingspans of approximately 60 m and fly large loops of approximately 200 m diameter centred at 200 m altitude. For the liftmode AWES, we additionally investigate different reelout strategies to reduce the interaction between the tethered wing and its own wake. Further, we investigate AWE parks consisting of 25 systems organised in five rows of five systems. For both lift and dragmode archetypes, we consider a moderate park layout with a power density of 10 MW km^{−2} achieved at a rated wind speed of 12 m s^{−1}. For the dragmode AWES, an additional park with denser layout and power density of 28 MW km^{−2} is also considered. The model predictive controller achieves very satisfactory flight path tracking despite the AWE systems operating in fully waked, turbulent flow conditions. Furthermore, we observe significant wake effects for the utilityscale AWE systems considered in the study. Wakeinduced performance losses increase gradually through the downstream rows of systems and reach up to 17 % in the last row of the liftmode AWE park and up to 25 % and 45 % in the last rows of the moderate and densedragmode AWE parks respectively. For an operation period of 60 min at a belowrated reference wind speed of 10 m s^{−1}, the liftmode AWE park generates about 84.4 MW of power, corresponding to 82.5 % of the power yield expected when AWE systems operate ideally and interaction with the ABL is negligible. For the dragmode AWE parks, the moderate and dense layouts generate about 86.0 and 72.9 MW of power respectively corresponding to 89.2 % and 75.6 % of the ideal power yield.
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The contribution of airborne wind energy (AWE) technologies to utilityscale electricity generation calls for the deployment of largescale AWE systems operating in parks. For conventional wind farms, downstream rows of wind turbines generate up to 40 % less power than the front row of the farm (Barthelmie et al., 2010). This performance decrease is due to the aggregated effect of upstream turbine wakes. Wind turbine wakes result from the interaction between wind turbines and the atmospheric boundary layer, and they are characterised by a wind speed deficit and increased turbulence intensity (Stevens and Meneveau, 2017; PorteAgel et al., 2020). For airborne wind energy systems, wake effects are generally considered small (Kruijff and Ruiterkamp, 2018) or are often ignored (Echeverri et al., 2020) due to the large swept area and relatively small wing dimensions. Hence, recent studies investigating performance losses (Malz et al., 2018) or layout optimisation (Roque et al., 2020) in airborne wind energy farms did not consider wake effects.
For individual systems, recent investigations of flow induction and wake effects were performed, mainly considering axisymmetric AWES configurations in uniform inflows using analyses based on momentum theory (Leuthold et al., 2017; De Lellis et al., 2018), vortex theory (Leuthold et al., 2019; Gaunaa et al., 2020) or the entrainment hypothesis (KaufmanMartin et al., 2022), or highfidelity CFD simulations (Haas and Meyers, 2017; Kheiri et al., 2018). Further numerical investigations of largescale airborne wind energy systems in the atmospheric boundary layer (ABL) (Haas et al., 2019b) have shown that the wake development downstream of the system is considerable; hence suggesting that wake effects of utilityscale airborne wind energy systems cannot be neglected when operating in parks.
To quantify the wakeinduced performance losses in a park of airborne wind energy systems, we have developed a computational framework combining largeeddy simulation (LES) and optimal control techniques. This framework couples the dynamics of the atmospheric boundary layer with the dynamics of several airborne wind energy systems operating in a park. The last 2 decades have seen the establishment of largeeddy simulations as an emerging tool for the modelling of wind turbines and wind farms in the atmospheric boundary layer (Jimenez et al., 2007; Calaf et al., 2010), hence we apply this technique in the context of airborne wind energy parks. The inherent unsteadiness of the wind, the effects of wake flows, and ABL turbulence make it challenging to foresee the behaviour and performance of individual AWE systems in the park. When modelling AWE systems, the wind environment is often approximated as a heightdependent power law or logarithmic distribution (Archer, 2013; Zanon et al., 2013b; Horn et al., 2013; Bauer et al., 2018). A limited number of studies have used more realistic virtual wind conditions from largeeddy simulations to assess the robustness of their control strategies (Sternberg et al., 2012; Rapp et al., 2019). However, none of the aforementioned studies have measured how the atmospheric boundary layer flow is altered by the operation of airborne wind energy systems.
Moreover in recent years, the airborne wind energy community has grown significantly and has seen the emergence of many developers of airborne wind energy systems. The designed systems may be as diverse in their operation mode as they are in the way they transform kinetic energy of the wind into usable mechanical work. A nonexhaustive list of AWE developers is presented in Cherubini et al. (2015). Arising from the seminal work of Loyd (1980), a majority of designs are based on the introduced lift and drag modes of operation, which rely on the high aerodynamic forces generated by the crosswind flight of a tethered aircraft. For the liftmode technology, power is generated onground by a generator driven by the rotation of the tether winch, which is induced as the tether is reeled out. For the dragmode technology, power is directly generated onboard by small turbines mounted onto the airframe. The working principles of both technologies are presented in detail by Diehl (2013). Although additional concepts such as kite networks (Read, 2018) and multikite systems (De Schutter et al., 2019) propose alternative upscaling strategies for the utilityscale generation, in this study we focus on singleaircraft AWE systems. In particular, we consider largescale multimegawatt systems operating in both lift and drag modes and align our design with the proposed utilityscale system targets unveiled by the companies Ampyx Power (Kruijff and Ruiterkamp, 2018) and Makani Power (Makani Power, 2012).
Hence, in this paper we address the operation of largescale AWE systems in a virtual wind environment in order to investigate the interaction between AWE systems and the ABL and quantify the impact of individual system wakes on the overall AWE farm performance. The paper is organised as follows: the methodology section, Sect. 2, details the computational framework developed for this study. The section presents the generation and tracking of AWE system trajectories by means of optimal control techniques as well as the modelling of atmospheric flows and its coupling with the AWE system dynamics by means of largeeddy simulations. Section 3 highlights the specific features of the flight path of both the lift and dragmode AWE systems, provides the configuration of the simulation domain and settings of the actuator methods, and also presents the ABL flow conditions and AWE farm layout chosen for this study. Section 4 presents the principal outcomes of the AWE farm simulation, focussing on the tracking behaviour of the AWE systems, assessing the performance of AWE farms for both operation modes, and discussing the complex interaction between AWE systems and turbulent ABL in terms of main flow and turbulent quantities. Finally, Sect. 5 highlights the main insights gained in the present study and discusses future work.
The interaction between AWE systems and the atmospheric boundary layer requires a reciprocal coupling of both the system and flow dynamics. We have therefore developed a computational framework combining largeeddy simulations and optimal control techniques. The framework consists of two distinct blocks: the virtual plant, simulating the operation of AWE systems in a turbulent wind field, and the controller, supervising the plant performance and guaranteeing its operation. Figure 1 presents an overview of the different building blocks of the framework. The virtual plant is the executing organ of the framework and consists of two interconnected modules handling the coupled simulation of the ABL flow (Sect. 2.1) and AWES dynamics (Sect. 2.2) within the LES framework. The controller block is the deciding organ of the framework and is built on two hierarchical modules, the supervision level, which defines the reference trajectories flown by each AWE system in the plant (Sect. 2.3), and the tracking level, which computes the steering actions of each individual AWE system (Sect. 2.4).
2.1 Virtual plant: wind flow dynamics
The procedure applied to simulate wind flow dynamics is shown in Fig. 1a. The dynamics of the ABL are computed by means of largeeddy simulation (LES), and the effects of AWE systems on the flow are modelled using actuator methods. The LES methodology is presented in Sect. 2.1.1, and the actuator methods are introduced in Sect. 2.1.2.
2.1.1 Atmospheric boundary layer simulation
In the last decades, LES has become a widely used tool to model conventional wind farms in the atmospheric boundary layer (Calaf et al., 2010; Munters et al., 2016; Allaerts and Meyers, 2017), such that we adopt the same approach to investigate airborne wind energy parks. In LES methodology, large and mediumscale flow structures are directly resolved with sufficient spatiotemporal resolution, whereas the influence of smallerscale dissipative motion is modelled. The large, energycontaining structures, such as wind speed variations over regions spanning several hundreds of metres, play a predominant role in the energy extraction process of conventional and airborne wind energy systems alike. In LES computations, the transfer of energy across (a large part of) the inertial subrange is further resolved, while the viscous dissipation of energy at the smallest scales is modelled using a subgridscale model.
In the current study, we neglect Coriolis and thermal effects, such that we only need to consider the turbulent surface layer of the ABL. In addition, the atmospheric flow through a wind farm is associated with a high Reynolds number such that we can omit the resolved effects of viscosity. Hence, the ABL can than be simplified to a pressuredriven boundary layer (PDBL) (Calaf et al., 2010). The PDBL is adequately modelled by the filtered incompressible Navier–Stokes equations for neutral flows, expressed by means of the continuity and momentum equations which are shown to read
The threedimensional velocity field can be decomposed into its resolved component $\stackrel{\mathrm{\u0303}}{\mathit{v}}$, for which we solve Eq. (1a) and (1b), and its residual component v^{′}. The filtered velocity field is parameterised by its streamwise ${\stackrel{\mathrm{\u0303}}{v}}_{x}$, spanwise ${\stackrel{\mathrm{\u0303}}{v}}_{y}$, and vertical ${\stackrel{\mathrm{\u0303}}{v}}_{z}$ flow components. The continuity equation, Eq. (1a), enforces the solenoidal properties of the filtered velocity field. The momentum equation, Eq. (1b), contains the local acceleration of the flow on the lefthand side, while the righthand side contains the convective acceleration and further collects the force contributions acting on the flow. The flow itself is driven by a constant imposed pressure gradient $\mathrm{\nabla}{p}_{\mathrm{\infty}}=[{\partial}_{x}{p}_{\mathrm{\infty}}/\mathit{\rho},\mathrm{0},\mathrm{0}]$. The filtering operation results in a residualstress tensor ${\mathit{\tau}}_{\mathrm{sgs}}=\stackrel{\mathrm{\u0303}}{\mathit{v}\mathit{v}}\stackrel{\mathrm{\u0303}}{\mathit{v}}\stackrel{\mathrm{\u0303}}{\mathit{v}}$ emulating the dissipation of kinetic energy at the subgrid scales. The isotropic part of the residualstress tensor is incorporated into the filtered modified pressure ${\stackrel{\mathrm{\u0303}}{p}}^{*}=\stackrel{\mathrm{\u0303}}{p}/\mathit{\rho}{p}_{\mathrm{\infty}}/\mathit{\rho}+\mathrm{tr}\left(\mathit{\tau}\right)/\mathrm{3}$, while the deviatoric part of the residualstress tensor τ_{sgs} is modelled using a closure model. Here, we opt for an eddyviscosity Smagorinsky model with a heightdependent Smagorinsky length scale (Mason and Thomson, 1992), which reinforces the loglaw behaviour of the ABL near the surface. Furthermore, an impermeable wall stress boundary condition is imposed at the bottom surface (Calaf et al., 2010). Finally, the body force term ${\stackrel{\mathrm{\u203e}}{\mathit{f}}}_{i}$ emulates the effects of AWE systems on the flow, for each of the N_{s} units of the park, by means of actuator methods, and its derivation is elaborated in Sect. 2.1.2.
Largeeddy simulations of AWE parks are performed with our inhouse solver SPWind developed at KU Leuven. The governing equations are discretised using a Fourier pseudospectral method in the horizontal directions (x,y) and a fourthorder energyconserving finitedifference scheme in the vertical direction z. These discretisation methods require periodic boundary conditions in the horizontal directions: we circumvent periodicity in the streamwise direction x by applying a fringeregion technique (Munters et al., 2016). This technique also allows one to provide unperturbed turbulent inflow conditions to the AWE park simulation by performing a concurrentprecursor periodic PDBL simulation in parallel to the main AWE park simulation. Time integration is performed using a classical fourstage fourthorder Runge–Kutta scheme.
2.1.2 Actuator methods
Actuator methods have proven their abilities to accurately reproduce wake characteristics of conventional wind turbines, such as the actuator line method (Sorensen and Shen, 2002; Troldborg et al., 2010; MartinezTossas et al., 2018) and the actuator sector method (ASM) (Storey et al., 2015). The dynamics of AWE systems and ABL flow span a large range of spatial and temporal scales: while the AWES wing experiences local wind speed fluctuations within a few metres along the wingspan, the ABL domain covers several kilometres. In addition, the flight dynamics of AWE systems are extremely fast and require a temporal resolution down to approximately 10 ms, whereas the largescale flow structures evolve 1 to 2 orders of magnitude more slowly. Therefore, we opt for an actuator sector method that can both capture the local variations in aerodynamic quantities along the wingspans of individual AWE systems and accurately operate across a larger range of temporal scales. Similar to the actuator line method (ALM), the ASM projects the force distribution along the system wingspan onto the simulation grid of the LES, with an additional temporal smoothing that allows one to consider a larger time horizon than just one instantaneous position of the AWE system. The resulting projected force distribution hence depends on the main parameters of the LES, ie. the grid resolution of the flow domain, parameterised by the cell size Δ_{x}, Δ_{y}, Δ_{z}, and the simulation time step denoted by Δt, the time step of the AWE system dynamics denoted by δt, and a set of tuning parameters. While the methodology is outlined hereafter, Appendix A addresses the choice of LES settings and tuning parameters.
For the current AWE farm simulations presented later on, the cell size is Δ_{x}=10 m in the axial direction, and the LES time step is set to Δt=0.250 s. In order to achieve a stable simulation of AWE system dynamics, which are much faster and require a smaller time step, its value is set to δt=0.010 s. Accordingly, the kinematics of the AWE system (see Sect. 2.2.3) are evaluated 25 times per LES time step. The LES flow field is however only updated after each time step Δt; hence AWE systems operate, for the duration of an entire LES time step, in a frozen flow field ${\stackrel{\mathrm{\u0303}}{\mathit{v}}}^{n}\equiv \stackrel{\mathrm{\u0303}}{\mathit{v}}(t={t}^{n})$ evaluated at the beginning of the time step.
From the perspective of the AWE system, the time horizon between the instants t^{n} and ${t}^{n+\mathrm{1}}={t}^{n}+\mathrm{\Delta}t$ provides a static snapshot of the flow field and is further discretised into a collection of 25 substeps ${t}^{l}={t}^{n}+l\mathit{\delta}t\in \left[{t}^{n},{t}^{n+\mathrm{1}}\right]$. At every substep t^{l}, the local aerodynamic forces (per unit segment length) ${\mathit{f}}_{q}^{l}\left({s}_{k}\right)$ are computed along the aircraft wingspan given its instantaneous position q^{l} in the frozen flow field. The procedure to compute ${\mathit{f}}_{q}^{l}\left({s}_{k}\right)$ is outlined in detail in Sect. 2.2.2. Subsequently, the local aerodynamic forces of each segment are smoothed out over the LES grid cells in the vicinity of the wing using a Gaussian convolution filter $G\left(\mathit{x}\right)={\left(\mathrm{6}/\left(\mathit{\pi}{\mathrm{\Delta}}_{\mathrm{f}}^{\mathrm{2}}\right)\right)}^{\mathrm{3}/\mathrm{2}}\mathrm{exp}\left(\mathrm{6}\left\right\mathit{x}{}^{\mathrm{2}}/{\mathrm{\Delta}}_{\mathrm{f}}^{\mathrm{2}}\right)$ (Pope, 2000). The variance ${\mathit{\sigma}}^{\mathrm{2}}={\mathrm{\Delta}}_{\mathrm{f}}^{\mathrm{2}}/\mathrm{12}$ of the Gaussian distribution, where the width of the filter kernel is set to Δ_{f}=2Δ_{x} (Troldborg et al., 2010), is chosen to be similar to the second moment of the box filter. The instantaneous, spatially filtered forces, integrated over the complete normalised wingspan $s\in [\mathrm{1}/\mathrm{2},+\mathrm{1}/\mathrm{2}]$, read
When flying crosswind manoeuvres at high speed, the AWE system may fly through several LES cells within one simulation time step Δt. For conventional wind turbines, blade tips sweeping several mesh cells in one time step result in a discontinuous flow solution in the near wake (Storey et al., 2015). In order to avoid this discontinuity, the individual contributions of the spatially distributed forces ${\widehat{\mathit{f}}}^{l}$ can subsequently be weighted in time using an exponential filter (Vitsas and Meyers, 2016) given a certain time horizon, hence sweeping over a sector of the LES domain. The timeaveraged force distribution is given by
The filter parameter γ is defined as $\mathit{\gamma}=\mathit{\delta}t/({\mathit{\tau}}_{\mathrm{f}}+\mathit{\delta}t)$ with the filter constant τ_{f}=2Δt_{LES}. The force distribution $\stackrel{\mathrm{\u203e}}{\mathit{f}}$ is then added to the momentum equation, Eq. (1b). The size of the sector, i.e. the number of substeps sampled, depends on the stage of the Runge–Kutta time integration scheme. At the first stage, only the force distribution evaluated at t^{n} is added to the previous sector. At the second and third stages, the force distributions between t^{n} and ${t}^{n}+\mathrm{\Delta}t/\mathrm{2}$ are further considered. While for the fourth and last stage, the new sector consists of the entire range of substeps between t^{n} and t^{n+1}. Ultimately, the added force distribution accurately captures the fast and local dynamics of each individual AWE system and emulates their collective effects on the boundary layer flow.
2.2 Virtual plant: AWE system dynamics
The simulation strategy applied to AWE systems is shown in Fig. 1b. To describe and simulate the dynamics of AWE systems, there exist numerous models of varying complexity. These models range from fast quasisteady models (Schelbergen and Schmehl, 2020) to simple 3DOF pointmass models (Zanon et al., 2013a) to complex 6DOF rigidbody models (Malz et al., 2019). The pointmass model allows the modelling of largescale AWE systems while it does not require an extensive knowledge of the aircraft dimension and inertia, nor aerodynamic parameter identification, as is the case for the rigidbody model (Licitra et al., 2017). The pointmass model is therefore highly scalable. The model is also highly versatile and allows one to easily adapt generic wing designs to both groundbased and onboard generation AWE systems. In addition, the pointmass model has fewer states and control variables compared to the rigidbody model, resulting in a less computationally intensive model. Finally the pointmass model is tailored for the optimal control applications tackled in this work such as poweroptimal trajectory generation and flight path tracking. Therefore, considering its simplicity and flexibility, we opt for the 3DOF pointmass model and present it in detail in Sect. 2.2.1.
Nevertheless, the 3DOF pointmass model also exhibits several limitations, mainly related to the lack of an explicit definition of the rotational dynamics of the system. Therefore, we derive the orientation and angular velocity of the system from statebased approximations. The approximations of orientation and angular velocity are further used to supplement the original pointmass model with a discrete lifting line model that takes into account local wind speed fluctuations for a more accurate representation of the AWE systems in the turbulent wind field. The procedure, which enables us to compute the instantaneous, local force distribution ${\mathit{f}}_{q}^{l}\left(s\right)$ required in Eq. (2), is presented in Sect. 2.2.2.
Further, the equations of motion of AWE systems are derived from Lagrangian mechanics according to the procedure introduced by Gros and Diehl (2013) and outlined in Sect. 2.2.3.
2.2.1 Pointmass model
The architecture of AWE systems can be divided into three main key components: the ground station, the tether, and the airborne device. In this work, we assume that the ground station has no influence on the atmospheric flow and can therefore be neglected. In addition, we neglect tether dynamics such as tether sag and represent the tether as a rigid rod of varying length (Malz et al., 2019), which is solely subject to aerodynamic drag. At last, we limit our investigation to rigidwing aircraft. Given the large level of approximation of the model, we simply assume that the wing planform is elliptical and without twist, which further allows us to simplify the aerodynamic properties of the finite wing (Anderson, 2010).
Hence, the tethered wing is modelled as a 3DOF point mass based on a representation of the system in nonminimal coordinates as proposed by Zanon et al. (2013b). The system state vector $\mathbf{x}={\left(\mathit{q},\dot{\mathit{q}},{C}_{\mathrm{L}},\mathrm{\Psi},\mathit{\kappa},l,\dot{l},\ddot{l}\right)}^{\top}$ is composed of position q and velocity $\dot{\mathit{q}}$ of the wing; the instantaneous aerodynamic quantities such as lift coefficient of the finite wing C_{L}, roll angle Ψ, and onboard turbine thrust coefficient κ; and the tether variables l, $\dot{l}$, and $\ddot{l}$, representing the length, speed, and acceleration of the tether respectively. In absence of explicit control surfaces such as ailerons, elevators, and rudders, we assume that the aerodynamic variables and the tether reelout speed are directly and instantaneously controlled; hence we introduce the control vector $\mathbf{u}={\left(\dot{{C}_{\mathrm{L}}},\dot{\mathrm{\Psi}},\dot{\mathit{\kappa}},\stackrel{\mathrm{\dots}}{l}\right)}^{\top}$, which consists of the time derivatives of lift coefficient, roll angle, and thrust coefficient and the tether jerk.
The 3DOF pointmass model only considers the translational motion of the AWE system, while the rotational motion is neglected. Hence, the aircraft attitude, commonly defined by the basis frame of the rigid body, is not known. Instead, we define the reference frame in which the external aerodynamic forces F_{q} acting on the system are expressed. First, we introduce the unit vector ${\mathit{e}}_{q}=\mathit{q}/\left\right\mathit{q}\left\right$ and the apparent wind speed v_{a} measured at the point mass. The apparent wind speed is defined as
where v_{w} is the threedimensional wind speed vector, computed from the neighbouring LES cells surrounding the pointmass position q using trilinear interpolation. Subsequently we define the basis vector ${\mathit{e}}_{v}={\mathit{v}}_{\mathrm{a}}/\left\right{\mathit{v}}_{\mathrm{a}}\left\right$, pointing along the apparent wind speed. Next, we introduce the perpendicular axis ${\mathit{e}}_{\u27c2}={\mathit{e}}_{q}\times {\mathit{e}}_{v}$ and the upwardpointing axis, which is orthogonal to the plane spanned by $\left\{{\mathit{e}}_{v},{\mathit{e}}_{\u27c2}\right\}$ and reads ${\mathit{e}}_{u}={\mathit{e}}_{v}\times {\mathit{e}}_{\u27c2}$. We apply a roll rotation of the perpendicular and normal axes about the axis e_{v}≡e_{D} by an angle corresponding to the state variable Ψ, which results in the transversal and lift axes
hence completing the basis of the frame of reference of the point mass ${\mathbf{B}}_{\mathrm{pm}}=\left\{{\mathit{e}}_{\mathrm{D}},{\mathit{e}}_{\mathrm{T}},{\mathit{e}}_{\mathrm{L}}\right\}$. Furthermore, the basis vectors of B_{pm} are the columns of the rotation matrix ${\mathbf{R}}_{\mathrm{pm}}=\left[{\mathit{e}}_{\mathrm{D}},{\mathit{e}}_{\mathrm{T}},{\mathit{e}}_{\mathrm{L}}\right]$.
Next, we can compute the aerodynamic forces acting on the point mass. These forces can be computed from the model states and the wind conditions, either solely measured at the pointmass position or along the complete wing span by using a more complex model, the lifting line model, introduced in the upcoming section, Sect. 2.2.2.
2.2.2 Lifting line model
The pointmass model provides the state and control variables that describe the flight path of the AWE system. The AWE system however operates in a turbulent boundary layer characterised by local fluctuations of the wind speed magnitude and direction, which, given the largescale of the system's wing, considerably influence the force distribution along the wingspan. Therefore, we supplement the original 3DOF pointmass model with the lifting line model, which allows us to consider local fluctuations of (aero)dynamic quantities along the wingspan without adding complexity to the dynamics modelling and control of the aircraft. Hence, the wing is modelled as an aligned collection of discrete wing sections of width δs, for which we compute the aerodynamic forces based on the local wind condition.
The orientation of the wing is derived from a series of statebased assumptions building on the pointmass basis B_{pm} introduced above and resulting in a new orientation basis $\mathbf{B}=\left\{{\mathit{e}}_{\mathrm{c}},{\mathit{e}}_{\mathrm{t}},{\mathit{e}}_{\mathrm{n}}\right\}$. Figure 2 shows a sketch of the wing configuration, and the derivations of the new orientation basis and of the local aerodynamic quantities are presented hereafter. First, we introduce the modelequivalent angle of attack $\stackrel{\mathrm{\u0303}}{\mathit{\alpha}}$, which we derive from the aerodynamic state C_{L} and can be computed as $\stackrel{\mathrm{\u0303}}{\mathit{\alpha}}={\mathit{\alpha}}_{\mathrm{L}=\mathrm{0}}+{C}_{\mathrm{L}}/a{C}_{\mathrm{L}}/\left(\mathit{\pi}\mathrm{AR}\right)$ for elliptical wings (Anderson, 2010). This modelequivalent angle of attack defines the a priori unknown orientation of the aircraft chord line ${\mathit{e}}_{\mathrm{c}}=\mathrm{cos}\left(\stackrel{\mathrm{\u0303}}{\mathit{\alpha}}\right){\mathit{e}}_{\mathrm{D}}\mathrm{sin}\left(\stackrel{\mathrm{\u0303}}{\mathit{\alpha}}\right){\mathit{e}}_{\mathrm{L}}$. The transverse axis of the bodyfixed frame e_{t} is aligned with the transverse axis of the pointmass frame e_{T}, and the normal axis of the bodyfixed frame is given as the cross product ${\mathit{e}}_{\mathrm{n}}={\mathit{e}}_{\mathrm{c}}\times {\mathit{e}}_{\mathrm{t}}$.
The rotation matrix R associated with the bodyfixed basis B can be formulated in terms of Euler angles (Zanon, 2015) and can further be expressed as an extrinsic sequence of three consecutive rotations about the axes of the inertial frame
with the Euler angles $\mathbf{\Theta}=(\mathit{\psi},\mathit{\theta},\mathit{\varphi})$, respectively representing yaw, pitch, and roll angles, and the associated rotations
The Euler angles are extracted from the entries r_{ij} of the orientation matrix R (Licitra et al., 2019):
The angular velocity ω of the aircraft can be derived from the Euler angle dynamics $\dot{\mathbf{\Theta}}$ . Accordingly, we first approximate $\dot{\mathbf{\Theta}}$ by a discrete backward difference,
and finally compute the angular velocity as
where, for the given sequence of rotations (Alemi Ardakani and Bridges, 2010), the mapping function inverse reads
The approximations of the angular velocity ω and the bodyfixed basis frame R can then further be used to define the position and speed of discretised wing elements of the AWE system.
From the statebased approximations of attitude and angular velocity of the aircraft, we can now evaluate the position and speed of each wing section k, defined at the section centre, expressed in the inertial frame of reference as
where s_{k} is the distance from the point mass to the segment centre given in the bodyfixed frame. In this study, each AWES wing is discretised using 61 actuator segments. The spatial discretisation of the wing in the lifting line model allows us to account for the local fluctuations of the unsteady threedimensional wind speed. The local wind speed vector v_{w,k} is hence interpolated at the locations q_{k} from the wind speed $\stackrel{\mathrm{\u0303}}{\mathit{v}}$ measured at the surrounding LES cells. We define the local apparent wind speed ${\mathit{v}}_{\mathrm{a},k}={\mathit{v}}_{\mathrm{w},k}{\dot{\mathit{q}}}_{k}$ and compute the effective angle of attack α_{k} seen by the airfoil of the wing section as
The local lift and drag coefficient c_{l}(α_{k}) and c_{d}(α_{k}) of each wing section are determined from aerodynamic polars obtained from 2D airfoil analysis mentioned in Sect. 3.1.
The air density at each wing section is computed from the standard atmosphere density model (Archer, 2013) and reads
where p_{0}=1013.25 hPa and T_{0}=288.15 K are the reference pressure and temperature measured at sea level, g=9.81 m s^{−2} is the gravity constant, $\mathrm{\Gamma}=\mathrm{6.5}\times {\mathrm{10}}^{\mathrm{3}}$ K m^{−1} is the average lapse rate in the atmosphere, and R=287.058 J kg^{−1} K^{−1} is the specific gas constant of dry air.
The chord length of each wing section ${\stackrel{\mathrm{\u0303}}{c}}_{k}$ is computed from the elliptical planform distribution of the wing according to
where b is the wingspan and AR the aspect ratio of the AWES wing. The values of b and AR are further discussed in Sect. 3.1. Finally, we can compute the local aerodynamic forces per unit segment length acting on each segment section. The instantaneous contributions of aerodynamic lift and drag forces read
Similarly, for dragmode AWE systems, the additional drag of onboard turbines is distributed over n_{g} actuator segments, covering the central third of the wingspan. We assume that the individual onboard turbines all operate at the same value of the generator thrust coefficient κ, such that the local generator drag reads
for specific segments $k\in (\mathrm{1},{n}_{g})$ or is zero otherwise. The instantaneous local section force per unit segment length ${\mathit{f}}_{q}^{l}\left({\mathit{q}}_{k}\right)$ at the time instant t^{l} contains the sum of local lift forces, drag forces, and, if applicable, onboard turbine forces. This total force reads ${\mathit{f}}_{q}^{l}\left({\mathit{q}}_{k}\right)={\mathit{f}}_{\mathrm{l}}\left({\mathit{q}}_{k}\right)+{\mathit{f}}_{\mathrm{d}}\left({\mathit{q}}_{k}\right)+{\mathit{f}}_{g}\left({\mathit{q}}_{k}\right)$ and can further be broadcast to Eq. (2) to be later added to the LES flow equations, Eq. (1b), or integrated over the complete wingspan to define the instantaneous total aerodynamic forces acting on the wing ${\mathit{F}}_{\mathrm{W}}^{l}$:
2.2.3 AWE system dynamics
In the current procedure, the system configuration is described by its set of generalised coordinates q, and its motion is restricted by the constraint c(q)=0, with which we associate the Lagrange multiplier λ. For the tethered singlewing AWE system configuration considered in this study, the point mass is constrained to the manifold described by
In order to obtain an index1 differential algebraic equation (DAE) that can be integrated with Newtonbased numerical methods, an index reduction is performed by differentiating the constraint (Eq. 22) twice (Gros and Diehl, 2013). Baumgarte stabilisation combined with periodicity constraints in the optimal control problem then ensures that the consistency conditions c(q)=0 and $\dot{c}(\mathit{q}$, $\dot{\mathit{q}})=\mathrm{0}$ are satisfied during the entire power cycle (Gros and Zanon, 2018). The motion of the AWE systems is then defined by the set of equations
In Eq. (23a), $m=\left({m}_{\mathrm{W}}+\frac{\mathrm{1}}{\mathrm{3}}{m}_{\mathrm{T}}\right)$ and $\stackrel{\mathrm{\u203e}}{m}=\left({m}_{\mathrm{W}}+\frac{\mathrm{1}}{\mathrm{2}}{m}_{\mathrm{T}}\right)$ define the effective inertial mass and the effective gravitational mass (Houska and Diehl, 2007), with m_{W} and m_{T} representing the mass of wing and tether and F_{q} the aerodynamic forces acting on the system. In Eq. (23b), p=10 is a tuning parameter of the Baumgarte stabilisation scheme.
The total aerodynamic force acting on the system F_{q} consists of the sum of wing forces F_{W} from Eq. (21) and tether drag forces F_{T}. The total tether drag (Argatov and Silvennoinen, 2013) is given by
with the tether diameter d_{T} and the drag coefficient of a cylinder C_{cyl}=1.0 at high Reynolds numbers.
The acceleration $\ddot{\mathit{q}}$ and control inputs u are obtained by respectively solving the DAE system (Eq. 23) and the path tracking problem (Eq. 36) detailed in Sect. 2.4. Finally, the kinematics of the AWE systems read
The kinematics of the systems (Eq. 25) are further integrated in time using an internal fourstage fourthorder Runge–Kutta scheme with a substep size δt as previously mentioned in Sect. 2.1.2.
Unfortunately, directly feeding the lifting line forces F_{W} (Eq. 21) into the AWE system dynamics (Eq. 23) in combination with the nonlinear model predictive control (NMPC), presented later in Sect. 2.4, is not possible: the mismatch between the aerodynamic forces generated by the lifting line and the aerodynamic model of the pointmass assumed by the controller does not lead to a stable system in our implementation. Therefore, in order to reduce the model mismatch between plant and controller, we compute the total wing force at the pointmass position using the aerodynamic states as ${\mathit{F}}_{\mathrm{W}}={\mathit{F}}_{\mathrm{L}}+{\mathit{F}}_{\mathrm{D}}+{\mathit{F}}_{\mathrm{G}}$, where the individual contributions of wing lift, wing drag, and onboard turbine drag are given by
This approach, while it still takes into account the unsteadiness of the LES wind field when computing the apparent wind speed v_{a} at the pointmass location from Eq. (4), results in a satisfactory tracking performance of the controller.
2.3 Controller: park supervision
The procedure defining the supervision level of the AWE park is shown in Fig. 1c. The supervision level contains two modules: the wind speed estimator, presented in Sect. 2.3.1, and optimal trajectory generator, presented in Sect. 2.3.2. For each AWE system, the wind speed estimator samples the local wind conditions monitored at the wing and derives an approximation of the associated vertical wind profile. These wind profiles are further communicated to the trajectory generator and used to generate periodic poweroptimal reference trajectories flown by each system individually.
2.3.1 Wind speed estimator
The controller does not have knowledge of the unsteady threedimensional wind field from the LES and hence assumes that the wind field is given by a onedimensional, heightdependent profile ${\mathit{v}}_{\mathrm{w}}=\left(U\left({\mathit{q}}_{z}\right),\mathrm{0},\mathrm{0}\right)$, where the streamwise wind speed component U(q_{z}) is given by the logarithmic distribution
The logarithmic distribution is parameterised by a reference speed U_{ref} given at an altitude z_{ref} and by a surface roughness length z_{0}. Further, we consider offshore conditions such that the surface roughness height is z_{0}=0.0002 m (Troen and Lundtang Petersen, 1989), and we will use the altitude z_{ref}=100.0 m as reference height at which the reference wind speed U_{ref} is measured.
When extracting power from the wind, conventional and airborne wind energy systems alike exert a thrust force on the incoming flow field (Jenkins et al., 2001). This process results not only in a velocity decrease downstream of the system, the wake, but also in a velocity decrease upstream of the system, the induction region. Upstream flow induction reduces the available power that wind energy systems can harvest. From onedimensional momentum theory, it was shown for conventional wind turbines modelled as actuator discs that the velocity decrease across the actuator can be defined as
where the unperturbed inflow wind speed is denoted as U_{∞}, while U_{D} and U_{W} respectively represent the wind speed at the actuator disc and in the wake. The axial induction factor a quantifies the strength of the induction phenomenon. The values of axial induction factors reported in AWE literature (Leuthold et al., 2017; Haas and Meyers, 2017; Kheiri et al., 2018) are lower than the known Betz limit $a=\mathrm{1}/\mathrm{3}$ of conventional wind turbines (Jenkins et al., 2001), suggesting that axial induction is less significant for airborne wind energy systems, although it cannot be fully neglected. In the current approach, the LESbased wind velocity sampled by the wind speed estimator already contains the effects of axial induction. Hence, optimal trajectories are later generated for reference wind speeds U_{ref}, computed by the wind speed estimator, and equivalent to the actuatorbased wind speed U_{D} instead of the inflow wind speed U_{∞}.
For each AWE system, the wind speed estimator monitors the system altitude and LESbased wind velocity measured at the point mass. In practice, the value of the wind speed can be derived from the measured airspeed using onboard instrumentation. From the monitored time series, we approximate the vertical wind speed profile by estimating the related reference wind speed U_{ref} by means of a runningtime average. For liftmode AWE systems, the wind speed and altitude are sampled during one full periodic cycle consisting of four power generation loops and one retraction phase. For dragmode AWE systems, the data series are collected during five singleloop power cycles. The range of flight altitude z≡q_{z} collected during the sample period T_{s} defines the sample space variable $Z=\left[{z}_{\mathrm{min}},{z}_{\mathrm{max}}\right]$ with $H={z}_{\mathrm{max}}{z}_{\mathrm{min}}$. Over the interval H, the mean wind speed measured at the point mass for the duration of the sampling period T_{s} is given by
The mean wind speed, integrated from the logarithmic profile over the same interval h, is a linear function of the actuator speed and is given by
Hence we can derive the reference value of the logarithmic wind profile that best approximates the wind conditions monitored by each AWE system during the sample period T_{s} as
We further use this value to generate the associated reference trajectories as we show in Sect. 2.3.2.
2.3.2 Generation of reference trajectories
In this study we generate a set of reference flight trajectories by using optimal control techniques. Optimal control techniques allow us to optimise the flight of the AWE system for different objectives while respecting a large number of constraints during operation. First, we can generate poweroptimal reference trajectories optimised for various flow conditions, as shown hereafter. Second, optimal control also allow us to perform path tracking of the reference trajectories in the turbulent LES flow field, as shown in Sect. 2.4.
The Lagrangian modelling procedure applied to a tethered point mass proposed in Sect. 2.2.1 results in an implicit index1 DAE given by Eqs. (23) and (25). This representation can be summarised by
In addition to the state and control vectors x and u, and the algebraic variables z=(λ) previously defined, we introduce an additional optimisation parameter θ and a set of constant parameters p, which allow us to apply the optimisation problem for a number of AWE designs and wind conditions. The optimisation parameter θ=d_{T} is the tether diameter and is optimised to sustain maximal tension T_{T,max} during operation. The set of constant parameters $p=({m}_{\mathrm{W}},b,\mathrm{AR},{U}_{\mathrm{ref}},{z}_{\mathrm{ref}},{z}_{\mathrm{0}}{)}^{\top}$ contains the dimensions of the aircraft – its wing mass m_{W}, wingspan b, and aspect ratio AR, and the wind conditions are parameterised by the reference wind speed and altitude ,U_{ref} and z_{ref}, and the roughness length, z_{0}.
During operation, the AWE systems have to satisfy a set of constraints $h\left(\dot{\mathbf{x}}\right(t),\mathbf{x}(t),\mathbf{u}(t),z(t),\mathit{\theta},p)\le \mathrm{0}$ that ensure a hardwarefriendly operation and the manoeuvrability of the aircraft. Hence, for the dynamics (Eq. 32) and constraints h, we can formulate a periodic optimal control problem (POCP) with a free time period T_{p} as
The cost function is defined as the average mechanical power output of the system generated over a full cycle of period T_{p}. The instantaneous power generated by lift and dragmode AWE systems respectively reads
For liftmode AWE systems, power is generated by unwinding the tether from the winch at a speed $\dot{l}$ under the high tether tension T_{T}=λl generated by the aerodynamic forces acting on the wing. The power generation phase consists of four loops and is followed by the retraction phase to wind the tether back up on the winch. For dragmode AWE systems, power is extracted from the wind by onboard generators. These generators are modelled as actuator discs such that the power is equivalent to the turbine thrust force ${\mathit{F}}_{\mathrm{G}}=\mathit{\kappa}\left\right{\mathit{v}}_{\mathrm{a}}{}^{\mathrm{2}}$ multiplied by the wind speed measured at the disc ${\mathit{\eta}}_{\mathrm{r}}\left\right{\mathit{v}}_{\mathrm{a}}\left\right$. Here, η_{r}=0.8 is the rotor efficiency and characterises the ability of the onboard turbines to extract power from the surrounding flow, ie. the axial induction associated with the actuator disc assumption. In line with Echeverri et al. (2020), where η_{r} is defined as rotor efficiency from thrust power to shaft power, Eq. (35) defines the mechanical power that dragmode AWE systems can extract from the wind.
The trajectory and performance of AWE systems depend on the path constraints and variable bounds specified in the periodic optimal control problem (Eq. 33c). Tables 1 and 2 summarise the chosen constraints and bounds specified for trajectory optimisation. The path constraints and variable bounds used in this work are compiled from the available literature (Gros and Diehl, 2013; Licitra et al., 2016; Zanon et al., 2013b; Kruijff and Ruiterkamp, 2018) and are adapted to largescale AWE systems. These choices are motivated hereafter. We set an operational upper limit to the tether length l_{max}=1000 m. We also limit the operation range of the winch by setting bounds to the tether speed $\dot{l}$ and acceleration $\ddot{l}$. In addition, the aerodynamic lift coefficient of the wing C_{L} is restricted to the range [0.0, C_{L,opt}], where ${C}_{\mathrm{L},\mathrm{opt}}=\mathrm{1.142}$. The lower bound ensures that the tether tension remains positive at all times while the upper bound limits local stall along the wing. The value of the upper bound C_{L,opt} stems from Fig. 5, and its choice is further discussed in Sect. 3.1. The roll angle Ψ is bounded in order to ensure safe operation, i.e. avoid collision between the tether and the airframe, and also take into account the reduced manoeuvrability of the largescale aircraft. Next, the maximal tether tension and the maximal acceleration are limited to ensure the integrity of the aircraft. The former also ensures that maximum tether stress is not exceeded by considering a safety factor f_{s}=3 and a tether yield strength σ_{max}=3.09 GPa. Further, we constrain the operation range of the control variables $\mathbf{u}={\left(\dot{{C}_{\mathrm{L}}},\dot{\mathrm{\Psi}},\dot{\mathit{\kappa}},\stackrel{\mathrm{\dots}}{l}\right)}^{\top}$ in order to avoid aggressive pitch and yaw manoeuvres, which are not suited to largescale aircraft. The bounds on u are derived using a heuristic approach in order to avoid unrealistic values of the approximated angular velocity. The spatial footprint of the trajectories is limited by means of spatial constraints, limiting the axial, lateral, and vertical extents of the trajectories in order to achieve system packing densities of $\mathrm{2}/{L}^{\mathrm{2}}$, as later discussed in Sect. 3.3.
In practice, the reference trajectories are optimised offline for a specific range of wind speeds. We aim to operate the AWE systems below the rated wind speed, in the socalled region II (Leuthold et al., 2018), where the harvested power increases with the wind speed. Hence we generate a library of optimal trajectories (OTL) for a range of actuatorbased wind speeds ${U}_{\mathrm{D}}\in [\mathrm{5.0},\mathrm{12.0}]$ with an increment ΔU_{D}=0.25 m s^{−1}. Figure 3 shows the different trajectories computed for the range of wind speeds. At the end of the sampling period T_{s}, the controller verifies whether the reference wind speed from Eq. (31) has changed, and if so, chooses the closest value in the range [5.0, 12.0] and commands the AWE system to track the new trajectory defined by the reference states x_{r}. The wind speed dependency of the trajectories is presented in Appendix B, Figs. B1 to B4.
2.4 Controller: flight path tracking
The procedure defining flight path tracking is shown in Fig. 1d. In the LESgenerated virtual wind environment, the operation conditions of the AWE systems differ substantially from the model assumptions in Eq. (27). The complex dynamics make the motion of the AWE system highly sensitive to fluctuations. Therefore a control algorithm is required to lead the system onto its precomputed optimised reference trajectory. Accordingly we apply nonlinear model predictive control (NMPC) (Gros et al., 2013). For a moving time horizon, the NMPC repeatedly computes the optimal control inputs that reduce the error between the current states x and the states x_{r} of the reference flight path taken from the OTL. Therefore, we can formulate for the prediction horizon T_{c} a new optimal control problem (Eq. 36).
The cost function (Eq. 36a) is formulated as a leastsquare objective, defining the tracking error between the current states x(t) and the given reference x_{r}(t), with a penalisation on the deviation from both the reference states and controls. Just like for the generation of periodic optimal trajectories, the constraints (Eqs. 36b and 36c) enforce the system dynamics and path constraints, while the initial condition (Eq. 36d) ensures that the initial states match the current estimate ${\widehat{\mathbf{x}}}_{\mathrm{0}}$. An additional terminal condition (Eq. 36e) ensures the system finds the reference at the end of the prediction horizon. Some of the path constraints in Eq. (36c), in particular the aerodynamic states and the control variables, are relaxed compared to the POCP bounds in Tables 1 and 2. This allows the controller to handle wind disturbances and plant–controller model mismatch with a wider range of steering strategies. Table 3 summarises the NMPC constraints and bounds adapted for trajectory tracking. The maximal allowed lift coefficient ${C}_{\mathrm{L},\mathrm{max}}\approx \mathrm{1.37}$ corresponds to the upper bound of the validity range of the linear lift slope of the elliptical wing. Similarly to C_{L,opt} for the trajectory generation, the value of the upper bound C_{L,max} stems from Fig. 5, and its choice is discussed in Sect. 3.1. In addition, the maximal allowed tether tension and acceleration are relaxed by respectively 10 % and 20 %.
To solve the optimal control problems, we use the awebox
toolbox (awebox, 2021). In the toolbox, both trajectory generation and flight path tracking OCPs are discretised using the direct collocation approach based on a Radau scheme with a fourthorder polynomial. The resulting nonlinear program is formulated using the symbolic framework CasADi (Andersson et al., 2018) and solved with the interiorpoint solver IPOPT (Wächter, 2002) using the linear solver MA57 (HSL, 2011). More information about the implementation can be found in De Schutter et al. (2019).
The different AWES park configurations investigated in the current work are presented hereafter. First, we detail the design choices behind the largescale lift and dragmode AWE systems in Sect. 3.1. Next we discuss the modespecific operation of both the lift and dragmode AWE systems, in particular the effects of wind speed on the system performance, in Sect. 3.2. Finally, we specify the computational settings of the performed LES of the atmospheric boundary layer and present the different AWES park layouts used in this study in Sect. 3.3.
3.1 Design of largescale AWES
For given wind conditions and space constraints, wind farm developers design their parks so as to optimise several design metrics. Traditionally, these metrics include the annual energy production (AEP) and the levelised cost of energy (LCOE) (Dykes, 2020). In order to minimise the LCOE, wind farms of conventional wind turbines in the range 10–15 MW target power densities of approximately 5 MW km^{−2} (Bulder et al., 2018). For the largescale deployment of their technologies, AWE manufacturers have engaged in the development of utilityscale multimegawatt AWE systems for offshore operation generating up to 5 MW of power (Kruijff and Ruiterkamp, 2018; Makani Power, 2012). Also, the AWE manufacturer Ampyx Power aims at achieving farm level power densities in the range of 10–25 MW km^{−2} with their commercial systems (Kruijff and Ruiterkamp, 2018). Accordingly, when designing parks of AWE systems, one has to define first the type of AWE systems used to harvest power and second the layout of the farm that will ensure a safe operation while maximising the power density.
Hence, we propose a generic design for lift and dragmode AWE systems that is easily scalable and allows one to model the operation of AWE systems in various wind conditions. The wingspan b of the wing is chosen as the driving design parameter to describe the dimensions of the different systems while the wing aspect ratio is fixed to AR=12, similar to the AWE systems developed by Ampyx Power. Design considerations such as manufacturability or structural analysis of the wing lie outside of the scope of the study. Therefore, a wing with elliptical planform is chosen here for simplicity. Elliptical wings exhibit advantageous aerodynamic properties given that their planforms generate low induced drag. Nevertheless, their stall characteristics are unfavourable: elliptical wings generate a constant downwash along the wingspan. Therefore, in the absence of geometric twist and for a uniform airfoil distribution, the induced angle of attack is constant along the wingspan. As a consequence, the effective angle of attack of each wing section is equal, which might cause the entire wing to stall simultaneously. Elliptical wings provide an analytical formulation of the aerodynamic lift and drag coefficients of the wing (Anderson, 2010):
where α_{g} is the geometric angle of attack of the wing. In addition, α_{L=0} and C_{d,0} respectively represent the zerolift angle of attack and profile drag coefficient of the airfoil. The lift slope a of the finite wing is given by $a={a}_{\mathrm{0}}/\left(\mathrm{1}{a}_{\mathrm{0}}/\left(\mathit{\pi}\mathrm{AR}\right)\right)$, with a_{0}=2π the airfoil lift slope from inviscid flow theory. The discussed properties of the elliptical wing and the aerodynamic coefficients given in Eq. (37) are valid for steadystate level flight; nevertheless they will be used as a surrogate model for the wing of the AWE system: this model is used to approximate the wing orientation in the lifting line model introduced in Sect. 2.2.2 and to derive the state bounds of C_{L} in the AWES model of the controller in Sect. 2.3 and 2.4. Unsteady aerodynamic effects, which are not considered here but might play a significant role for AWE systems, need to be addressed in the future.
In order to achieve a high lifttodrag ratio, we opt for airfoil sections of type SD 7032, also used by the manufacturer TwingTec (Gohl and Luchsinger, 2013). The lift and drag polars of the airfoil are computed from an XFOIL direct analysis using the opensource software XFLR5 (XFLR5, 2021) at a chordbased Reynolds number Re_{c}=10^{7} and shown in Fig. 4. From the airfoil analysis, we can retrieve the values of the zerolift angle of attack ${\mathit{\alpha}}_{\mathrm{L}=\mathrm{0}}=\mathrm{4.0174}$^{∘}, of the critical angle of attack α_{c}=17.7^{∘}, and of profile drag coefficient ${C}_{\mathrm{d},\mathrm{0}}=\mathrm{0.0054}$. Past the critical angle of attack α_{c}, the airfoil is subject to stall, characterised by a drop of the lift coefficient and a sharp increase in the drag coefficient.
The lift and drag predictions of XFOIL are only valid just beyond α_{c} (XFOIL, 2021). In addition, the distribution of angles of attack in the lifting line model can vary substantially along the wingspan, due to the spatial fluctuations of the LESbased wind velocity and the speed difference between the two tips of the aircraft. Therefore, the operation of the AWE system is to be optimised such that not only the adverse effects of stall are avoided but also an accurate prediction of the aerodynamic behaviour of local wing sections of the lifting line is ensured. To do so, we restrict the values of the state variable C_{L}, and equivalently the values of the modelequivalent angle of attack $\stackrel{\mathrm{\u0303}}{\mathit{\alpha}}$, to a range well below stall, as explained hereafter.
The linear approximation of the wing lift coefficient given in Eq. (37) is only valid for the range of angles of attack up to ${\mathit{\alpha}}_{\mathrm{g},\mathrm{max}}\approx \mathrm{10.6}$^{∘}, well below the critical angle of attack of the wing ${\mathit{\alpha}}_{\mathrm{c},\mathrm{W}}\approx \mathrm{20.6}$^{∘}, as shown in Fig. 5a. Therefore, the upper bound of C_{L} is set to a value ${C}_{\mathrm{L},\mathrm{opt}}\in \left[\mathrm{0.0},{C}_{\mathrm{L}}\left({\mathit{\alpha}}_{\mathrm{g},\mathrm{max}}\right)\equiv {C}_{\mathrm{L},\mathrm{max}}\right]$ that maximises the glide ratio G of the tethered wing system: the glide ratio $G={C}_{\mathrm{L}}/\left({C}_{\mathrm{D}}+{C}_{\mathrm{D},\mathrm{T}}\right)$ is defined as the ratio of aerodynamic lift forces to the combined contribution of wing and tether to the overall drag forces (Diehl, 2013). The contribution of the tether is given as ${C}_{\mathrm{D},\mathrm{T}}={C}_{\mathrm{cyl}}\left({l}_{\mathrm{max}}{d}_{\mathrm{T}}\right)\mathrm{AR}/\left(\mathrm{4}{b}^{\mathrm{2}}\right)$ with the drag coefficient of a cylinder at high Reynolds number C_{cyl}=1.0. For the targeted wingspan of 60 m and an expected tether diameter of $\mathrm{3.5}\times {\mathrm{10}}^{\mathrm{3}}$ m, the maximal glide ratio G≈16.5 is achieved at a geometric angle of attack of α_{g}≈8.1^{∘} and results in ${C}_{\mathrm{L},\mathrm{opt}}=\mathrm{1.142}$, as shown in Fig. 5b. Consequently, the upper bound of the wing lift coefficient is ${C}_{\mathrm{L},\mathrm{opt}}=\mathrm{1.142}$ in the POCP (Eq. 33) and ${C}_{\mathrm{L},\mathrm{max}}=\mathrm{1.37}$ in the NMPC (Eq. 36). A discussion of the angle of attack and load distributions of the trajectories optimised with Eq. (33) and using this approach is presented in Appendix A2.
The remaining necessary dimensions of the AWE systems are derived from a semiempirical study based on available manufacturer data, where we derive the wing mass and tension limits from the wingspan b. We set an upper bound to the tether tension and assume that it scales linearly with the surface area of the wing. Further we assume that the targeted AWE system with a 60 m wing with aspect ratio 12 can sustain up to approximately 850 kN of pulling force from the tether. Further, the wing mass is estimated by fitting a power law onto data from the manufacturer Makani Power taken from Makani Power (2012) as shown in Fig. 6. Hence wingspanbased relations for the maximal tether tension and the wing mass read
with the parameters γ_{1}≈2840.24, γ_{2}≈0.1478, and γ_{3}≈2.662. Note that for liftmode AWE systems, the wing mass is reduced by 25 % in order to account for the absence of onboard turbines. Finally, the tether material is assumed to be Dyneema for both types of AWE systems. This material is commonly used by AWE manufacturers and has a density ρ_{T}=970.0 kg m^{−3}.
3.2 Operation of largescale AWE systems
We apply the optimisation procedure outlined in Sect. 2.3.2 to optimise the dimensions and flight path of three different AWE systems targeted at offshore operation and capable of generating 5 MW of rated power for a design wind speed U_{D}=12.0 m s^{−1}. We design and optimise one dragmode AWE system and two different liftmode AWE systems, of which we discuss the operation characteristics in Sect. 3.2.1. Table 4 summarises the main dimensions of wing and tether of the three considered AWE systems. For liftmode AWE systems, preliminary work on single AWE systems has highlighted the effect of reelout speed on the instantaneous flow conditions experienced by the system. Therefore, we use two different reelout strategies for the operation of liftmode AWE systems, which we discuss in Sect. 3.2.2. The details of the single AWE system analysis are given in Appendix A. The first strategy uses the hypothetical physical limitations of the winch, where we assume that the maximal reelout speed is ${\dot{l}}_{\mathrm{max}}=\mathrm{20.0}$ m s^{−1} as given in Table 1. However, given that the optimisation procedure does not take the generation of the system wake into account, the wing can potentially interact locally with its own wake during the reelout phase as discussed in Sect. 3.2.2. Therefore, the second strategy limits the maximal reelout speed to the expected advection speed of the wake U_{W} as estimated in Eq. (28a) and (28b). While the value of the induction factor a is a priori unknown, we opt for the conservative guess a=0.25 based on an empirical choice, such that the maximal reelout speed of the tether becomes windspeeddependent and is shown to read
Given that the individual AWE systems in the farm operate in different wind conditions, we also generate their trajectories for a large range of wind speeds below rated wind speed. Hence, each system can adapt its flight path and power performance to the temporarily experienced wind field as introduced in Sect. 2.3. The effects of varying wind speed on the AWE system trajectories are discussed in Sect. 3.2.3.
3.2.1 Operation at rated wind speed
At rated wind speed, the dragmode AWE system and the two liftmode AWE systems all generate 5 MW of power. However, their flight path and power generation profile differ substantially as shown in Fig. 7. A complete description of all system states, controls, and additional metrics is given in Appendix B, and their main features are summarised here.
The dragmode AWE system operates at a constant tether length l≈650 m and follows a nearcircular flight path of diameter D≈200 m at a mean elevation of about 17^{∘}. The duration of a singleloop periodic cycle equals approximately 10 s. The system operates at maximal tether tension, maintaining a high flight speed between 60 and 80 m s^{−1} throughout the cycle. The onboard turbines of the system continuously generate power while reaching peak generation during the downward flight when the maximal flight speed is achieved.
For the two liftmode AWE systems, one periodic cycle consists of four powergenerating loops and a retraction phase. The duration of the periodic cycle ranges between approximately 64 and 66 s, while the reelout phase accounts for about 70 % of the cycle. During the reelout phase, the tether length varies approximately between 500 and 900 m, while the loop diameter ranges between 200 and 250 m and the mean elevation angle is approximately 19^{∘}. Mechanical power is generated by reeling out the tether at high speed under maximal tether tension. The retraction phase consists of a steep upward flight at maximal tether length to transition out of the last power generation loop, followed by the reelin phase of the tether at high altitude, and is completed by a dive manoeuvre to transition into to the new power generation phase. During the retraction phase, the tension on the tether is minimal, and a fraction of the generated power is used to reel in the tether. With the first reelout strategy, the reelout speed fluctuates during each individual loop, reaching its peak during the downward flight, resulting in large variations in instantaneous power generation. The flight speed of the systems remains nearly constant during the duration of the production phase, fluctuating between approximately 60 and 70 m s^{−1}. With the second strategy, the reelout speed of the tether is capped by the inductionbased constraint such that the tether is reeled out at a nearly constant speed. Accordingly, the generated power remains nearly constant during the generation phase. Given the reelout speed limitation, part of the kinetic energy of the system is used to accelerate the system during the downward flight, resulting in large fluctuations of the flight speed in the range 60 to 90 m s^{−1}.
3.2.2 Characteristics of liftmode operation
In order to assess the different reelout strategies, we investigate in detail the operation of individual liftmode AWE systems. For this analysis, the systems operate in a highresolution logarithmic inflow with reference wind speed U_{D}=10.0 m s^{−1} without ambient turbulence, and their trajectories are optimised for the same wind conditions. We further assume that the wind conditions remain constant over time and the systems are “perfectly steered”, suggesting that the systems can perfectly follow their reference flight path with the reference controls, hence making the controller part of the framework obsolete for this analysis. The detailed analysis is given in Appendix A, which also includes a grid convergence study and an evaluation of the different actuator methods introduced in Sect. 2.1.2.
Figure 8 shows the structure of the wake flow for both liftmode AWE systems at the end of the reelout phase. During the power generation phase, the wing is subject to high lift forces such that tip vortices emanate from the wing tips and are transported downstream by the background flow. These wing tip vortices generated during the individual loops start to interact with each other in the wake and eventually merge into a more significant single structure, which eventually breaks down further downstream due to the effect of mixing. In particular, the figure shows the effect of the reelout strategy on the generation of tip vortices: with the first reelout strategy, the individual tip vortices of each loop cannot be precisely identified, suggesting that the induced tip vortices are advected downstream by the background flow at a speed similar to the reelout speed of the tether. Consequently, the wing interacts with its own wake during the reelout phase. With the second strategy, the inductionbased limitations of the reelout speed reduce the interaction between the tip vortices at each loop, resulting in more distinct structures. The interaction can however not be completely prevented, such that the individual structures eventually merge later downstream.
The effects of the reelout strategy on wing–wake interaction can also be observed on the local flow conditions measured by the wing. Figure 9 shows the instantaneous streamwise wind speed component ${\stackrel{\mathrm{\u0303}}{v}}_{x}$ monitored at seven equidistant locations along the wingspan. For comparison, not that without interaction between the AWE system and the wind environment, wing sections would experience wind speeds in the range 9.7–10.9 m s^{−1}. For the first reelout strategy (see Fig. 9a), we observe large fluctuations of the instantaneous wind speed: sharp drops of the streamwise velocity components are measured during the lower part of the four power generation loops. These drops are particularly important for the entire starboard side (sections c000 to s030), suggesting that a large portion of the wing suddenly encounters a region of low wind speed that we can identify as the wake. Furthermore, the intensity of the velocity drop increases after every loop as the system reels out further downstream, indicating that the individual wakes of each loop are combined into a single wake. With the second reelout strategy (see Fig. 9b), the patterns are less distinct and the magnitude of the fluctuations less significant, hence indicating that the interaction between wing and wake is limited. We still observe that the outer starboard part of the wing (sections s020 and s030) experiences sharp fluctuations, suggesting that the starboard wing tip flies through some wake region. However, these fluctuations are very local and temporary, while for the first reelout strategy most of the wing is interacting with the wake.
Given the limited interaction between the wing and the wake when using the second reelout strategy, i.e. with an inductionbased upper limit of the reelout speed, we will perform the liftmode AWES farm simulation with the second design of liftmode AWE system.
3.2.3 Operation of largescale AWES in belowrated regime
When operating in a park, AWE systems are subject to largescale wind speed variations and wakes of upstream systems. As a result, the operation of each individual system evolves as it experiences different wind conditions. Hence, we discuss here the effect of varying wind speed on the trajectories of drag and liftmode AWE systems. The power curves and selected metrics for the expected range of wind speeds 5.0 m s^{−1} ≤ U_{D} ≤ 12.0 m s^{−1} are shown in Fig. 10, while the wind speed dependency of the flight path is shown in Fig. 3. A complete description of the effects of varying wind conditions on all system states, controls, and additional metrics is given in Appendix B.
For the expected range of wind speeds, the generated power ranges from approximately 1 to 5 MW for both operation modes. For liftmode AWE systems, decreasing wind speeds mainly affect the reelout patterns. At low wind speeds, the system only reels out, and hence generates power, during the downward part of the loops. In comparison, the upward flight is performed at constant tether length under fluctuating tether tension. Consequently, the increase in tether length during the reelout phase is much more limited and hence the flight path more compact.
For the dragmode AWE system, the shape of the singleloop flight path barely changes for varying wind speeds. However, the most noticeable difference is the contribution of the onboard turbines at low wind speeds. During the upward flight, the onboard turbines switch to propeller mode in order to overcome gravity and keep a constant flight speed and tether tension, and hence consume some power.
3.3 LES setup and AWES park layout
The turbulent ABL is modelled according to the procedure introduced in Sect. 2.1. In order to capture all the relevant spatiotemporal scales of the turbulent ABL in the precursor simulations and the wake effects in the AWE park simulations, the flow domain spans several kilometres and is resolved with high spatial resolution. The dimensions of the simulation domain are ${L}_{x}\times {L}_{y}\times {L}_{z}=\mathrm{10.0}\times \mathrm{4.0}\times \mathrm{1.0}$ km^{3}. The domain is discretised into cells of the size ${\mathrm{\Delta}}_{x}\times {\mathrm{\Delta}}_{y}\times {\mathrm{\Delta}}_{z}=\mathrm{10.0}\times \mathrm{10.0}\times \mathrm{5.0}$ m^{3}, resulting in a grid resolution ${N}_{x}\times {N}_{y}\times {N}_{z}=\mathrm{1000}\times \mathrm{400}\times \mathrm{200}$ of up to 80 millions cells for both main and precursor domains. The mean inflow velocity of main and precursor domains is U_{ref}=10.0 m s^{−1} at 100 m altitude. Note that temporally resolved subsets of the generated precursor simulations are available for download from the Zenodo platform (Haas and Meyers, 2019).
For the utilityscale deployment of their technology, Ampyx Power envisions park power densities in the range of 10–25 MW km^{−2} while targeting a system packing of about $\mathrm{2}/{L}^{\mathrm{2}}$, where L is the maximal tether length (Kruijff and Ruiterkamp, 2018). Accordingly, we investigate three different AWE park configurations with the same packing density, where each park is composed of 25 systems ordered in five rows and five columns with an equidistant spacing between each system, i.e. L in the streamwise directions and $L/\mathrm{2}$ in the spanwise direction. The top and side views of the three different AWE park layouts are shown in Fig. 11.
The first AWE park operates with liftmode AWE systems, and the reference layout length is set to L=1000 m. This park layout is the densest possible layout to be operated with the designed liftmode AWE system, such that the allocated areas do not overlap, and will further be referenced as L1 (liftmode park 1). The second AWE park operates with dragmode AWE systems and the same layout as L1 and is referenced as D1 (dragmode park 1). These two AWE parks can achieve farm power densities up to 10.0 MW km^{−2} when all systems operate at rated wind speed. The third AWE park also operates with dragmode AWE systems but with a reference layout length L=600 m. This AWE park, referenced as D2 (dragmode park 2), corresponds to the densest possible layout for the designed dragmode AWE system and can achieve a farm power density up to 27.8 MW km^{−2} in rated wind conditions. Note that the flight path constraints in Table 1 ensure a safe operation in accordance with the area allocated to each system in all park configurations. At the initial simulation time, the states of the AWE systems in the parks are initialised to the state values of the precomputed reference trajectories for U_{D}=10.0 m s^{−1}.
The simulations are performed on the highperformancecomputing infrastructure of the Flemish Supercomputer Center (VSC). The computational cost of one LES time step typically largely exceeds the computational cost of one NMPC evaluation. Hence the required computational resources depend heavily on the grid resolution, which also limits the simulation horizon. However, given that AWES dynamics are faster than ABL flow dynamics, the control actions of each AWES are evaluated several times per LES time step. In total, 54 000 evaluations per AWES are performed during the simulation horizon of 4500 s. Consequently, the total execution time of the LES time step may depend on the execution time of individual NMPC evaluations. The performance of NMPC evaluations depends on several parameters, such as the length of the prediction horizon or the number of model variables. For dragmode AWESs on the one hand, the execution time of NMPC evaluations is almost negligible, accounting on average for less than 5 % of the execution time of an LES time step. For liftmode AWESs on the other hand, the execution time of NMPC evaluations can vary substantially. While about 95 % of the evaluations are performed in less than 1.0 s, similar to dragmode AWESs, the remaining 5 % of the evaluations require about 15–30.0 s. This performance drop is generally observed when AWESs operate in heavily constrained regions of the variable space or transition between considerably different reference flight paths. As a result, the dragmode AWE park simulations require about 1200 node hours (or 52 node days) while the liftmode AWE park simulation requires about 1600 node hours (or 67 node days) on the Tier2 hardware of VSC.
In the following section, we present the main outcomes of the simulations of AWE parks in the turbulent boundary layer. Figure 12 shows instantaneous snapshots of the streamwise wind velocity ${\stackrel{\mathrm{\u0303}}{\mathit{v}}}_{x}$ for the three park configurations investigated in this study. The figure illustrates the complexity of the flow, highlighting largescale wind speed variations and large wake structures trailing behind individual AWE systems and how they affect the ABL flow at park level. First, we have a look at the timeaveraged characteristics of the ABL flow in Sect. 4.1. Next, we investigate the behaviour of individual AWE systems subject to turbulent wind conditions and how their tracking and power performance are impacted in Sect. 4.2. Last, we discuss the power performance of the complete AWE parks and in particular how wakes impact their efficiency in Sect. 4.3.
To ease the comparison, the trajectories of the AWE systems are parameterised as a circular flight path centred around a virtual trajectory centre. For the liftmode system, the diameter of the trajectory is approximately 240 m, and its centre is located 645 m downstream of the ground station at an altitude of 220 m. Equivalently, for the dragmode system, the diameter of the trajectory is approximately 200 m, and its centre is located 610 m downstream of the ground station at an altitude of 190 m.
4.1 Timeaveraged ABL flow statistics
4.1.1 Mean ABL flow characteristics
Figure 13 shows timeaveraged contours of normalised streamwise velocity deficits and turbulent kinetic energy averaged over 1 h of AWE park operation. The wake field develops through the parks as individual wakes add up with the wakes induced by consecutive AWE systems. We observe in the three cases that downstream system rows operate in fully waked conditions. At the last row of the park, the mean velocity deficit, relative to the ABL precursor, reaches about 10 % for the liftmode park (L1), while for the dragmode parks the velocity deficit increases to about 20 % (D1) and up to 30 % (D2). In the trail of the annuli swept by the wings, these velocity deficits are further accentuated and can reach up to 20 % for the liftmode AWE systems (L1) and respectively 40 % and 50 % for the dragmode AWE systems (D1 and D2). For the dragmode AWES park with dense layout (D2), the blockage effect of the dragmode AWE systems also results in an acceleration of the mean flow up to 10 % around the park, which cannot be observed for the two AWE parks with moderate layout (L1 and D1).
The mean turbulence intensity TI of the current ABL simulation ranges between 2 % and 8 % and is about 3 % at operation altitude. Hence the power extraction and the wake mixing in the AWE park lead to large increases in turbulent kinetic energy (TKE) levels. The observed TKE levels can reach up the 10fold of the precursor levels. In particular for liftmode systems (L1), we observe higher levels of turbulence compared to the same dragmode configuration (D1), possibly due to the merging effect of individual loop wakes discussed in Sect. 3.2.2. Furthermore, the liftmode AWE systems sweep over a much larger area than the dragmode AWE systems. For the latter in layout D1, the lateral extent of the wakes is more limited. Hence, individual columns of dragmode systems accumulate the effects of their wakes in streamwise corridors, while the wakes spread much farther in the spanwise direction for the liftmode AWE park (L1). The higher packing density of the second dragmode AWE park (D2) exhibits the same phenomena as park D1 but with much higher intensity, given the reduced space available for wake recovery.
4.1.2 Flow characteristics inside the parks
We can further deepen the analysis by looking at vertical and horizontal profiles of velocity deficit and turbulent kinetic energy as shown in Fig. 14. The profiles are shown one diameter upstream of the trajectory centre for AWE systems located in the central column of the parks. For the liftmode system, the diameter of the trajectory is approximately 240 m, and it is located 645 m downstream of the ground station at an altitude of 220 m. Equivalently, for the dragmode system, the diameter of the trajectory is approximately 200 m, and its centre is located 610 m downstream of the ground station at an altitude of 190 m.
The profiles provide an overview of the local flow distribution experienced by each system in downstream locations of the AWE column. The profiles confirm the prior observations relative to the downstream development of the wakes through the park: for the liftmode AWE park, the wake is much more widespread and therefore induces a weaker velocity deficit. In contrast, for dragmode AWE parks, the wake exhibits more localised and stronger velocity deficits, highlighting the annular shape of the wake. Each park configuration shows higher velocity deficits at the bottom half of the loops, induced by higher loadings on the wing. In addition, wake recovery in the upper part of the loop is eased by higher turbulence levels.
The wake impacts are much stronger for the dragmode AWE park (D2) with a higher packing density. The horizontal profiles show that the wake merges with the wakes of neighbouring AWES columns as its radius extends laterally the further downstream it progresses. Hence, wakes do not only aggregate as they advect downstream but also merge in the spanwise direction, therefore resulting in the stronger velocity deficits and higher TKE levels observed for the higher farm power density.
4.2 AWES operation in turbulent boundary layer
AWE systems operate in very different wind conditions depending on the operation mode, the park layout, and their own position within the park as seen in the previous section, Sect. 4.1. In this section, we discuss how these parameters affect the operation of the systems, in particular how they adapt their respective trajectories and how accurately the NMPC can track their flight path.
Figure 15 shows the reference wind speed of the trajectories tracked by the AWE systems as directed by the supervision level of the controller. Each reference trajectory is tracked for the duration of respectively one cycle for liftmode systems (≈65 s) and five cycles for dragmode systems (≈50 s) before it is updated by the controller based on the averaged velocity sampled by the wind speed estimator over that time period. In particular, we show the tracking behaviour of the five rows of AWE systems of the central column of the three park configurations.
At the startup of the operation, each system experiences similar conditions from the unperturbed ABL flow. As systems begin to harvest power and induce their own wakes, upstream wakes travel downstream and start to impact the operation of downstream rows until the complete park operates in fully waked conditions at the end of the spinup phase at t=900 s. The variations in the tracked value for the front row AWE system (AWES 003) are due to the inherent unsteadiness of the ABL flow. We observe how these largescale wind fluctuations experienced at the front row propagate with some delay through the downstream rows of the park. However, the large decreases in tracked speed for the downstream systems are due to the effects of upstream wakes. As discussed above, dragmode AWE systems experience much stronger wakes and hence track lower wind speeds than liftmode AWE systems. In particular, a larger decrease is observed from the front row to the second row.
Note that the amplitude of the largescale variations in the ABL varies from one column to another. Consequently, the different columns of the parks do not operate similarly and impose a different forcing on the ABL flow field. This effect can explain the asymmetry observed in the timeaveraged quantities shown in Fig. 13. In particular, the averaging period of 1 h may not be sufficient to entirely smooth out these temporal and spatial variations across the entire AWE farms.
We can further look at the instantaneous streamwise wind speed measured in flight at the wing as shown in Fig. 16 for two systems located in the front and back rows. Despite the ambient turbulence of the ABL flow, the wind speed monitored by the frontrow systems coincides quite well with the reference wind speed distribution of the tracked trajectories. However for the liftmode system, we observe additional sharp fluctuations resulting in abrupt increases and decreases in the monitored streamwise wind speed. These effects are amplified as the system progresses further in the reelout phase, as seen in the time window $t\in [\mathrm{2200},\mathrm{2350}$ s]. This indicates that the wing may still interact with its own wake in some sections of the flight path. In order to ensure a reliable operation, the reelout speed limitation was lifted off for flight path tracking to allow a better reactivity of the systems to the unsteady wind conditions. Hence, the systems can achieve higher reelout speeds than the reference.
The strong velocity deficits and the higher levels of TKE observed in the back rows of the parks greatly impact the instantaneous wind speed monitored. We observe a much larger range of fluctuations for AWE systems located in the last row than in the first row. The effects described for the front row liftmode system are accentuated for the back row system, suggesting the presence of compact wake regions from upstream systems advecting through the park.
The unsteady wind conditions also impact the tracking accuracy of the NMPC. Figure 17 shows the position offset of the systems relative to the tracked flight. For the dragmode systems operating in the front row of parks D1 and D2, the tracking error is almost negligible. This error increases for the systems in the back row as they experience larger wind speed fluctuations. For the liftmode park L1, we observe larger position tracking error, partly due to the additional degree of freedom related to the reelout and reelin of the tether. The highest offsets occur however after the AWE system flies through a lowwindspeed region. Nevertheless, the error remains marginal compared to the dimensions of the flight path, as it rarely exceeds 12 m. Hence, the NMPC algorithm tends to always react reliably to the wind speed fluctuations and steers the system back onto its reference flight path.
4.3 Power performance at system and park level
The unsteady wind conditions not only affect the tracking behaviour of each AWE system but also impact their performance. Figures 18 and 19 respectively show the tether tension and mechanical power of the front and back row systems in the central column of the parks. For the liftmode systems, the lowspeed regions crossed by the wing generally lead to a drop of the tether tension that must be compensated for by steering manoeuvres. These tether tension variations negatively impact the amount of generated mechanical power such that we observe power losses of about 10 % to 15 % relative to the reference power of the tracked trajectories. For the two dragmode park configurations, the wind fluctuations barely impact the operation of the systems, and we observe a good tracking of the power generation profiles. In the back row however, the tracked flight path and the low wind speed available require the onboard turbines to operate as propellers in order to overcome gravity while flying upward. Hence, some dragmode systems end up also consuming some of the harvested power. Note that the tether tension maxima monitored at each system match the relaxed upper bound used for flight path tracking as discussed in Sect. 2.4.
We further address the power extraction process from the perspective of the complete AWE park, and in particular from the system location in the park. Figure 20 shows the mean power generation of each row relative to the reference power of the tracked trajectories for the three different park configurations. In this way we can quantify the power losses due to local wind speed fluctuations and the steering efforts they cause. For the liftmode park, every system across each row is only capable of harvesting a fraction of the tracked power. While in the front row up to 91 % of the tracked power can be harvested, the efficiency of the power tracking decreases gradually for the downstream rows, such that approximately 85 % of the tracked power can be harvested in the last row. For the dragmode AWE parks, individual systems are able to harvest more power from the wind than expected. For the dragmode park D1, each row harvests on average about 4 % more power. With the denser layout (D2), the systems in the front row harvest 5 % more power while this increases to up to 7 % in the last row. This suggests that the supervision level underestimates the local wind conditions experienced by the systems.
Figure 21 shows the mean power generation of each row relative to the front row of the farm. This allows us to quantify the wakeinduced power losses for the three different park configurations. For the liftmode AWE park, the relative power decreases through the farm; i.e. the power deficit between front row and downstream rows reaches up to approximately 17 %. For the dragmode AWE parks, the wake losses are much more important and reach approximately 25 % in the last row of AWE park D1 and up to 45 % for AWE park D2 with a denser layout. While for the liftmode AWE park the power decrease across the subsequent rows is progressive, for dragmode AWE parks, the power losses increase abruptly between the first and second rows. This drop accounts for about 62 % and 52 % of the row losses for the park configurations D1 and D2 respectively.
In this study we have investigated the wake interaction and power extraction for large parks of utilityscale AWE systems. Flight path tracking by means of model predictive control (MPC) has proven to be a robust control strategy for both lift and dragmode systems when operating in turbulent wind conditions: the path tracking results in position offsets generally up to 12 m, while satisfying the set variable bounds and tracking the power profiles quite accurately, in particular in drag mode. Liftmode AWE systems encounter however important fluctuations in local wind speeds during operation, which can lead to temporary large decreases in tether tension and significant reductions of instantaneous power. First, we have observed that liftmode systems can interact with their own wake: during reelout, the wing can fly (partially) through or in the direct vicinity of the wake induced in the previous powergenerating loops. Hence, the interaction with its own wake needs to be considered already during the design phase: when generating optimal reference trajectories, it is crucial to define reeling strategies that avoid these situations and incorporate induction phenomena in the modelling procedure. This is an active topic in current AWE research. Second, these large wind speed fluctuations can be associated with the characteristic wakes induced by liftmode AWE systems: while the flow forcing is negligible during the retraction phase, the wakes induced during consecutive loops of the power generation phase tend to merge into a singlewake structure. This interaction leads to compact, lowspeed flow regions which get advected downstream with the mean flow and negatively impact the operation of downstream systems.
Moreover we showed that for the three investigated configurations, wake effects and wakeinduced losses in utilityscale AWE parks are significant. The induced wakes display very characteristic annular shapes; however the strength and the width of the wakes highly depends on the operation mode and the park layout. In terms of park performance, we observed wakeinduced performance losses of up to approximately 15 % in the last row for the liftmode AWE park. For the two dragmode AWE parks with moderate and dense farm power densities, the wakeinduced power losses increase up to 25 % and 45 % respectively in the last row of the parks. Hence, the layout of the farm also plays a considerable role in the performance of the park. However, note that wake losses and tracking losses/gains can not completely be isolated from each other: improving the tracking behaviour of liftmode systems by limiting the large tension fluctuations in the tether would result in a stronger axial flow induction, hence leading to larger wake losses and vice versa.
The current predictions formulated in this study can be refined by increasing the resolution of the LES flow domain. With a higher grid resolution, the representation of the individual AWE systems can be enhanced, limiting their forcing effects to the direct vicinity of the wing instead of large sectors. The estimation of the aerodynamic quantities along the wingspan of the aircraft would also benefit from higher resolutions as a larger range of turbulent motions are resolved, hence increasing the modelling accuracy of the wing behaviour. However, increasing the grid resolution is not the only approach to improve the accuracy of the framework. The fully coupled computational framework integrates numerous building blocks into a single simulation platform with a high level of complexity. The overall fidelity of the simulations can however be improved in the future by increasing the complexity of individual components of the framework and addressing the following known limitations: first, we can improve the modelling of the atmospheric boundary layer. The description of the ABL flow can be enhanced by including Coriolis forces and thermal effects in the LES framework (Allaerts and Meyers, 2015). The inclusion of these effects significantly modifies the structure of the flow, in particular capturing the inversion layer separating the turbulent boundary layer from the free atmosphere above. The height of the inversion layer, the strength of wind veer in the boundary layer, or the occurrence of lowlevel jets will certainly affect the flight path characteristics of AWE systems, such as optimal heights and tether length. These effects will further impact the controllability and power performance of the systems and hence require further investigations. Second, the complex dynamics of AWE systems can be better characterised by more detailed models. One possible alternative to the pointmass model is the 6DOF rigidbody model (Malz et al., 2019). This model includes the rotational dynamics of the complete system, hence removing the need for a statebased assumption of the wing orientation and further providing a more accurate prediction of the flight manoeuvres. In addition, this model includes realistic control capabilities by steering the aircraft using the deflection of control surfaces such as ailerons, elevators, and rudders. Third, the modelling of the tethered aircraft can further be improved by incorporating unsteady aerodynamics. Fast pitching manoeuvres of the wing and sharp turns of the aircraft can have considerable effects on the local aerodynamic characteristics of the wing sections and hence have a significant influence upon the resulting aerodynamic forces of the system. Additionally, the aeroelastic response of the aircraft to the unsteady loading should be considered in order to prevent fatigue of the structure. Although the rigidrod assumption performs well for the hightension power generation phases (Malz et al., 2019), this model does not capture the tether dynamics. The incorporation of tether sag (Trevisi et al., 2020) would benefit the completeness of the modelling effort.
In terms of tracking performance, the improved system modelling should help reduce the mismatch between model and virtual plant such that an enhanced NMPC algorithm could accurately track the flight path while considering the integrated lifting line forces to compute the AWES dynamics. Also, in order to further improve the closedloop performance of the systems in terms of power output, one can consider applying approximate or exact economic NMPC formulations instead of the pure flight path tracking scheme used in this study.
Finally, the wake profiles show quite large deviations from the logarithmic mean flow distribution assumed by the highlevel controller. Therefore in the future, we can implement supervision strategies that take the wake shape into consideration and make use of the inherent flexibility and adaptability of AWE systems: we can further use the wake profiles to generate new optimal flight trajectories in which individual systems adapt the elevation and azimuthal angles of operation to eventually avoid the upstream wakes.
To conclude, this study demonstrates the considerable interaction between largescale AWE systems and the atmospheric boundary layer for the considered park configurations and motivates further investigations to achieve an efficient operation of utilityscale AWE parks.
Ahead of the investigation of utilityscale AWE parks, we have conducted a series of singleAWES analyses in order to assess the computational setup used in this study. In this appendix, we address in particular the tuning of the actuator parameters in Sect. A1, the detailed analysis of wake structure by means of highresolution simulation in Sect. A2, the grid dependency of wake characteristics in Sect. A3, and finally the convergence of the supervision level of the controller in Sect. A4.
Figure A1 shows the computational setup used in this study of both lift and dragmode AWE systems. The domain dimensions are ${L}_{x}\times {L}_{y}\times {L}_{z}=\mathrm{2800}\times \mathrm{1000}\times \mathrm{1000}$ [m^{3}]. We use three different resolutions to discretise the flow domain as specified in Table A1. The AWE systems operate in a sheared inflow without ambient turbulence parameterised by a logarithmic profile given in Eq. (27), where U_{ref}=10.0 m s^{−1}.
A1 Tuning of actuator parameters
The dependency of the actuator line and actuator sector methods to their (temporal) parameters is investigated for the simulation of a single dragmode AWE system. The system operates at a reference trajectory ${U}_{\mathrm{D}}={U}_{\mathrm{ref}}=\mathrm{10.0}$ m s^{−1}, and the flow domain is discretised using the finest grid resolution with cell size $\mathrm{\Delta}=\mathrm{4.0}\times \mathrm{4.0}\times \mathrm{2.0}$ m^{3}. The filter width of the Gaussian filter is uniform in all three spatial directions and is set for all actuator methods to Δ_{f}=2Δ_{x} following the recommendations in Troldborg et al. (2010).
The chosen grid resolution requires a minimal simulation time step Δt=0.1 s. Accordingly, we test three ALM simulations with LES time steps of Δt=0.1 s, Δt=0.05 s, and Δt=0.025 s and three ASM simulations with time step Δt=0.1 s and filter constants τ_{f}=1Δt, τ_{f}=2Δt, and τ_{f}=3Δt. The states of the AWE system are interpolated from the OCP reference, and the local aerodynamic quantities of each wing section are computed using the lifting line method presented in Sect. 2.2.2.
Figure A2 shows time series of the angle of attack, measured at the wing tip sections, and of the specific aerodynamics forces added onto the flow. The ALM simulations exhibit large fluctuations of the added forces due to the varying flow conditions experienced by the wing. Within one LES time step, the local flow conditions monitored by individual wing section vary greatly due to the very localised effects of the ALM forces. The ASM simulations, on the other hand, weight the individual force contributions at discrete AWES time steps, hence resulting in smoother force distribution, while introducing a slight time delay, but also smoother variations in angle of attack in time given the low velocity gradients encountered due to the wider smearing of the forces onto the LES domain. The forces of the optimised reference trajectory using the pointmass model are shown for comparison: over the period of one full periodic cycle, the model mismatch between the forces computed with actuator methods and the reference OCP forces is less than 4 %.
Given the high flight speed of the system, the wing navigates through several grid cells during one time step for the simulations with Δt=0.1 s and Δt=0.05 s. Therefore, the ALM requires the smallest time step, Δt=0.025, to ensure a smooth transition as the wing sweeps across the flow domain. This results however in a 4fold increase in the computational effort. The ALM simulation with Δt=0.025 hence serves as a reference simulation. The ASM technique can be applied to simulations with Δt=0.1 and allows the approximate reproduction of the unsteady forcing of the reference ALM with an error less than 2 %. Increasing the value of the time filter constant τ_{f} tends to reduce the error but also increases the computational cost. Hence, in order to keep a reasonable computational cost and achieve a sufficient accuracy, we perform further AWE simulations with the ASM techniques using τ_{f}=2Δt.
A2 Highresolution simulations of single AWESs
We can extend the previous highresolution simulations of a single dragmode AWE systems to liftmode AWE systems in order to investigate the structure of the wake flow and loads acting on the wing.
Figure A3 shows the structure of the wake flow for each AWE system simulated with ASM on the fine grid by visualising positive levels of Q criterion (Jeong and Hussain, 1995). For the dragmode AWE system, tip vortices emanate continuously from the wing tips and are transported downstream in a coherent manner by the background flow. For the liftmode AWE systems, the wing tip vortices generated during the individual loops start to interact with each other in the wake and eventually merge into a more significant single structure, which eventually breaks down further downstream due to the effect of mixing. The pumping behaviour of the liftmode systems is also clearly visible: during the retraction phase, the aircraft takes a different path while being subject to a much smaller wing loading, hence interrupting the wake generation for a short amount of time. The grid resolution and the actuator settings enable us to resolve individual flow structures and provide interesting insights into the complex characteristics of the wake flow of single AWE systems.
Figure A4 shows the spanwise distribution of angle of attack and wing loading for the three different systems. During power generation, the angle of attack is kept below the critical angle of attack. For the liftmode AWE systems, the wing tips stall briefly during the turn manoeuvre, initiating the reelin phase. This effect is reduced when using the inductionbased limitation of the reelout speed and lasts less than 1 s without significantly modifying the total aerodynamic force acting on the wing. The force distribution along the wingspan is not elliptically distributed, as opposed to the assumptions of the pointmass model: the starboard side of the wing, the wing half flying at the outer side of the flight path, experiences a stronger wing loading than the port side due to the wing angular velocity, in particular during upward flight. For the dragmode AWE system, the additional loading of the onboard turbines only slightly modifies the spanwise load distribution of the wing, in particular during the downward path of the loop.
A3 Analysis of grid dependency
Next, we perform the simulation of the single dragmode AWES and assess the timeaveraged flow quantities using the three grid resolutions specified in Table A1. In this grid analysis, the ratio of filter width to grid size is kept constant; hence ${\mathrm{\Delta}}_{\mathrm{f}}/{\mathrm{\Delta}}_{x}=\mathrm{2}$. The simulation with the fine grid, which explicitly captures the individual tip vortices during the reelout phase of liftmode AWE systems, is considered the reference simulation.
Figure A5 shows time series of the aerodynamic wing forces integrated over its wingspan acting on the dragmode AWE system for the three grid resolutions. The decrease in grid resolution comes with a simultaneous increase in the simulation time step, hence drastically reducing the computational expense of the simulations. This comes however at the cost that the larger spatial filtering and temporal smoothing result in a much wider sector, representing the wing more as a blunt body in the LES domain. The wide spread of the added forces in turn reduces the accuracy of the local aerodynamic quantities of each wing section compared to the reference simulation. Nevertheless, the integrated forces overestimate the reference by less than 2 % such that lower grid resolution can still sufficiently capture the resulting aerodynamic forces in order to accurately compute the AWES dynamics.
Figure A6 shows timeaveraged profiles of axial flow velocity and turbulent kinetic energy at different locations in the wake of the dragmode AWE system for three different grid resolutions. The velocity profiles show that all resolutions can capture the general annular shape of the wake and its strength up to 1200 m behind the system. This good agreement allows us to use the coarse grid resolution for the AWE park simulations, where the downstream spacing between two rows of AWE systems does not exceed 1000 m and which would be too computationally expensive to perform on the finer resolutions. In terms of resolved turbulent kinetic energy, the medium and coarse grids cannot capture the same levels of turbulence as the fine grid. This is however less crucial given that in the fully turbulent ABL simulations, ambient turbulence triggers wake breakdown and turbulent mixing earlier than in the turbulencefree sheared inflow investigated here. While for the coarser resolution it cannot be assured that liftmode AWE systems, even with the inductionbased reelout speed limitation, will not interact with their own wakes, this effect will be weakened given that the force smearing also reduces the strength of the tip vortices.
A4 Convergence of supervision level
Finally we can also verify the convergence of the supervision level of the controller and assess the different reelout strategies employed for the liftmode systems. Figure A7 shows the tracking profiles of the three AWE systems by the timeaveraged wind speed values sampled by the controller and the associated reference velocity of the tracked trajectories. All systems show good convergence towards an asymptotic value between 9 and 10 m s^{−1}. As discussed in Sect. 3.2.2, liftmode AWE systems may interact with their own wakes; therefore we have used two different reelout strategies. Figure A7 shows that the inductionbased reelout strategy used by the liftmode system (1) allows us to track higher wind speed references, showing that the wing experiences less interaction with its wake. Therefore, this strategy also allows us to operate the system along trajectories generating more power and hence increases the performance of the AWE park.
The virtual flight data of each individual AWE system, including all states and controls, are monitored for the different AWE park simulations. The monitored flight data, the tracking scheme of each AWE system, and the tracked reference trajectories of the AWE systems, are openly available on the research data repository Zenodo from the dataset “Largeeddy simulations of airborne wind energy farms: AWES virtual flight data” (https://doi.org/10.5281/zenodo.5705563). The different files are organised as Python dictionaries and are stored as pickle objects. Python 3 scripts are provided to handle the files and visualise the data.
The reference trajectories are optimised for the lift and dragmode AWE systems presented in Sect. 3.2.1 for the wind speed range ${U}_{\mathrm{ref}}\in [\mathrm{5.0},\mathrm{12.0}]$ with increments of 0.25 m s^{−1}. The total size of the trajectory files is approximately 158 MB. The trajectories are stored in the following files:

drag_mode_awes_opt_trajectories.pckl
: dragmode AWES. 
lift_mode_awes_opt_trajectories.pckl
: liftmode AWES with original reelout strategy. 
lift_mode_awes_ind_trajectories.pckl
: liftmode AWES with inductionbased reelout strategy.
The states, controls, and other quantities can be visualised over a periodic cycle, as shown for liftmode AWE systems in Figs. B1 and B2 and for dragmode AWE systems in Figs. B3 and B4, using the Python script:

visualize_reference_trajectories.py
.
The instantaneous inflight values of states, controls, and additional metrics are monitored and can be compared to their reference values in order to investigate in details the tracking behaviour of each individual AWE system. The available data cover 1 h of operation of the fully developed AWES farms ($t\in [\mathrm{900},\mathrm{4500}$ s]). For the 25 AWE systems operating in each farm, the monitored flight data are stored as individual pickle objects referenced from 001 to 025, according to the position of the system in the farm. The total size of the flight data files is approximately 8.5 GB (3 parks × 25 AWES × ≈113 MB). The virtual flight data are stored in the following files (Set XXX to the index of the system):

lift_mode_farm_layout1_awes_XXX.pckl
: liftmode AWES park L1. 
drag_mode_farm_layout1_awes_XXX.pckl
: dragmode AWES park D1. 
drag_mode_farm_layout2_awes_XXX.pckl
: dragmode AWES park D2.
The record of tracked references is also organised as a Python dictionary and is stored as a pickle object for the three park configurations of the study. The size of the tracking record files is about 1.4 MB, and the files are stored as

lift_mode_farm_layout1_tracking_
scheme.pckl
: liftmode AWES park L1. 
drag_mode_farm_layout1_tracking_
scheme.pckl
: dragmode AWES park D1. 
drag_mode_farm_layout2_tracking_
scheme.pckl
: dragmode AWES park D2.
Each monitored quantity can be compared to its reference for a given time horizon, as shown for liftmode AWE systems in Figs. B5 and B6 and for dragmode AWE systems in Figs. B7 and B8, using the Python script:

visualize_virtual_flight_data.py
.
The SPWind flow solver is a proprietary software of KU Leuven while the toolbox awebox
is openly accessible on GitHub (https://github.com/awebox, awebox, 2021). Timeresolved threedimensional subsets of the ABL precursor simulations are openly accessible from the AWESCO Wind Field Datasets (https://doi.org/10.5281/zenodo.1418676) (Haas and Meyers, 2019). Virtual flight data of the AWE systems for the three AWES park configurations are presented in Appendix B and are openly accessible from the dataset “Largeeddy simulation of airborne wind energy farms: AWES virtual flight data” (https://doi.org/10.5281/zenodo.5705563) (Haas and Meyers, 2021).
TH and JM jointly set up the concept and objectives of the current work, partially based on a research collaboration plan previously devised by JM and MD. The PhD work of TH was supervised by JM and the PhD work of JDS by MD. TH performed code implementations in the LES code SPWind and carried out the AWES park simulations. TH generated the reference AWES trajectories using the awebox
toolbox with support from JDS. JDS implemented the NMPC capability in the awebox
toolbox and assisted TH for the coupling with SPWind. TH performed the data analysis and data visualisation. TH wrote the manuscript with major contributions from JM. JDS, JM, and MD revised the manuscript.
At least one of the (co)authors is a member of the editorial board of Wind Energy Science. The peerreview process was guided by an independent editor, and the authors also have no other competing interests to declare.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
The computational resources and services in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation Flanders (FWO) and the Flemish Government department EWI.
This research has been supported by the H2020 Marie SkłodowskaCurie Actions (grant nos. 642682 and 953348) and the Deutsche Forschungsgemeinschaft (grant nos. 277012063 and 424107692).
This paper was edited by Roland Schmehl and reviewed by two anonymous referees.
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 Abstract
 Introduction
 Methodology
 Simulation setup
 AWE farm simulation and results
 Conclusions
 Appendix A: Investigation of singleAWES wake characteristics
 Appendix B: Data accessibility
 Code and data availability
 Author contributions
 Competing interests
 Disclaimer
 Acknowledgements
 Financial support
 Review statement
 References
 Abstract
 Introduction
 Methodology
 Simulation setup
 AWE farm simulation and results
 Conclusions
 Appendix A: Investigation of singleAWES wake characteristics
 Appendix B: Data accessibility
 Code and data availability
 Author contributions
 Competing interests
 Disclaimer
 Acknowledgements
 Financial support
 Review statement
 References