Actuator line modeling of wind turbines requires the definition of a free-stream velocity in a computational mesh and a regularization kernel to project the computed body forces onto the domain. Both choices strongly influence the results. In this work, a novel velocity sampling method—the so-called effective velocity model (EVM)—is implemented in the CFD software SOWFA, validated, and compared to pre-existing approaches. Results show superior method robustness with respect to the regularization kernel width ((Formula presented.)) choice while preserving acceptable accuracy. In particular, the power predicted by the EVM is nearly independent of the (Formula presented.) value.

In this work, we find a reduced-order model for the wake of a wind turbine controlled with dynamic induction control. We use a physics-informed dynamic mode decomposition algorithm to reduce the model complexity in a way such that the physics of the wake mixing can be investigated and that the model itself can be easily embedded into control-oriented frameworks. After discussing the advantage of forcing the linear system resulting from the algorithm to be conservative (as a consequence of the periodicity of the pitch excitation) and the choice of observables, we describe a procedure for calculating the energy associated with individual modes. The considered data-set is composed of large eddy simulation (LES) results for a single DTU 10 MW wind turbine in uniform flow. Simulations were performed first with baseline control (for reference) and then with the Pulse and the Helix approaches with constant excitation amplitude and different excitation frequencies. The frequencies and energies associated with the resulting modes are discussed.

Simulating entire wind farms with an actuator line model requires significant computational effort, especially if one is interested in wake dynamics and wants to resolve the tip vortices. A need to explore unconventional approaches for this kind of simulation emerges. In this work, the actuator line method is implemented within a lattice-Boltzmann flow solver, combined with a sliding mesh approach. Lattice-Boltzmann solvers have advantages in terms of performance and low dissipation, while the sliding mesh allows for local refinement of the blade and tip vortices. This methodology is validated on a well-documented case, the NREL Phase VI rotor, and the local refinement is demonstrated on the NREL 5 MW rotor. Results show good agreement with reference Navier–Stokes simulations. Advantages and limitations of the sliding mesh approach are identified.

In this work we test a Fluid–Structure Interaction (FSI) method based on the SOWFA+ OpenFAST framework. The linear structural module of FAST is coupled to SOWFA's Actuator Line simulations to perform the aeroelastic analysis of a wind tunnel scaled model (1:75) of the DTU 10 MW turbine. The objective is to give a quantitative description of the turbine stiffness influence on the wake flow by varying the model structural properties. The simulations are performed in two different operating conditions: below rated (TSR=7.5) and above rated (TSR=5.5). Turbulence is generated by positioning disturbing elements at the inlet, analogously to what was done in the reference wind tunnel tests. Results show that flexibility starts to have a considerable impact on the wake velocity deficit when the tip deflection is somewhere between 4% and 12% of the blade length. Based on these results, an indication is given of when the accuracy obtainable with the CFD-CSD coupling justifies the increased computational cost.

Dynamic Mode Decomposition (DMD) is a fully data-driven method to extract a linear system from experimental or numerical data. It has proven its suitability for modeling wind turbine wakes, particularly those generated with Dynamic Induction Control (DIC), a method to reduce the wake deficit by enhancing its mixing with the surrounding flow. In this context, DMD may be used to build computationally efficient aerodynamic models suitable for model-based wind farm control algorithms. However, these standard DMD models are only valid for the flow conditions of the training data. This paper presents a novel approach to generalize a DMD model for DIC wakes from the training wind speed to various wind speeds by scaling the DMD modes. For this purpose, we first extract the DMD modes from numerical simulations of a DIC wake at a constant, homogeneous wind speed. Then, we adapt the obtained modes to a different wind speed with a scaling law for the frequency and magnitude derived from the definition of the Strouhal number. This allows for a versatile, efficient application of the DMD model in heterogeneous wind conditions at low computational costs. For validating the presented method, we model a helix wake at 6 ms^-1 based on the DMD modes from Large Eddy Simulations (LES) at 9 ms^-1. The DMD model coincides at a high level with validation simulations, resolving even mid- to small-scale structures.

Dynamic Induction Control (DIC) is a novel, exciting branch of Wind Farm Control. It makes use of time-varying control inputs to increase wake mixing, and consequently improve the velocity recovery rate of the flow and the power production of downstream turbines. The Pulse and the Helix are two promising DIC strategies that rely on sinusoidal excitations of the collective pitch and individual pitch of the blades, respectively. While their beneficial effects are evident in simulations and wind tunnel tests, we do not yet fully understand the physics behind them. We perform a systematic analysis of the dynamics of pulsed and helicoidal wakes by applying a data-driven approach to the analysis of data coming from Large Eddy Simulations (LES). Specifically, Dynamic Mode Decomposition (DMD) is used to extract coherent patterns from high-dimensional flow data. The periodicity of the excitation is exploited by adding a novel physics informed step to the algorithm. We then analyze the power spectral density of the resulting DMD modes as a function of the Strouhal number for different pitch excitation frequencies and amplitudes. Finally, we show the evolution in time and space of the dominant modes and comment on the recognizable patterns. By focusing on the modes that contribute the most to the flow dynamics, we gather insight on what causes the increased wake recovery rate in DIC techniques. This knowledge can then be used for the optimization of the signal parameters in complex layouts and conditions.