Efficient Large-­eddy Simulation for Wind Energy Applications

Modelling the interaction of wind turbines with the ambient flow is essential for almost all technical aspects of wind energy exploitation. Large-eddy simulation (LES) is the most detailed approach feasible to model this complex interaction of wind turbines with the atmospheric boundary layer and the wakes of upstream turbines. Despite more than twenty years of fundamental research on wind turbine modelling with LES, applications of the method remain limited to academic use cases to date. The main bottleneck hindering a broader adoption of LES in the industrial practice is the large computational demand of the method. Nevertheless, it holds enormous potential for addressing various modelling challenges arising from current trends in wind energy. A promising alternative to classical numerical approaches for LES is the lattice Boltzmann method (LBM). In particular, GPU-based (graphics processing unit) implementations of the method provide significant performance gains and have enabled unprecedented computational efficiencies for LES in different fields of fluid dynamics. Still, the LBM´s potential for wind energy applications remains untapped due to open questions, some of which are specific to the field. This thesis addresses two specific problems in applications of LES to wind turbine and farm simulations. First, is the representation of wind turbines with the actuator line technique. And, second, is the modelling of the surface shear stress in simulations of atmospheric boundary layers. Both aspects are crucial to enable LES for wind energy applications with the LBM, as is usually done with conventional approaches. As for the former, an LBM implementation of the actuator line model is applied in multiple studies on wind turbine wakes. Code-to-code comparisons and experimental validations show that the model can accurately capture the aerodynamic forces acting on the turbine blades as well as the wake characteristics. For the simulation of boundary layer flows a novel LBM-specific wall model is developed. The model, referred to as inverse momentum exchange method, imposes the surface shear stress at the first offwall grid points by adjusting the slip velocity in bounce-back boundary schemes. Simulations are compared to theoretical, numerical, and experimental reference data of isothermal boundary layer flows. It is consistently found that both mean quantities and higherorder turbulence statistics can be well-captured by wall-modelled lattice Boltzmann LES using the presented wall model and the employed cumulant collision scheme. The results presented illustrate that the LBM is a suitable approach for state-of-the-art LES of wind turbine wakes and boundary layer flows. Moreover, the applied method is shown to be robust, and, above all, extremely computationally efficient. Based on the observed computational efficiencies, it is concluded that industry LES for wind energy applications is possible with GPU-based LBM solvers. Furthermore, additional studies presented in this thesis illustrate further potentials of the method. Such are applications of reinforcement learning to wind farm control or large-scale data generation for the training of deep learning models for wake predictions