A wind turbine is designed to produce a maximum amount of energy at minimum cost while it withstands any possible wind condition. In the wind conditions with the highest wind speeds the wind turbine has to cope with the most extreme loads. In these most extreme cases the wind turbine is parked, the blades do not rotate, and the blades deform elastically under these wind loads. An increase in the flexibility of the blades results in higher elastic blade deformations. The main objective of this project is to design blades in a way that the extreme loading on the blades reduces with more than 10 % without compromising the energy yield by making use of flexible materials and the corresponding increased deformations. The reason to focus on load reduction is that a wind turbine can be made cheaper if these loads are reduced. The research methodology is a three-step approach: Firstly a modelling tool is created to design and evaluate blades with new flexible materials at different locations in the blade. Secondly a verification procedure is performed to check the accuracy of the modelling tool. Thirdly this tool is used to design blades with highly flexible materials and to perform an iteration procedure to design the best flexible blade design. The modelling tool is based on the cross sectional software BECAS to design new flexible blades and on the aeroelastic software HAWC2 to analyse the behaviour. This BECAS-HAWC2 modelling tool is based on the existing XANT M-21 wind turbine of which only the blade materials are variable parameters, the rest of the wind turbine remains as it is. A verification procedure compares the modelling tool with two other models of the same blade. The minor differences between several modelled parameters increase the confidence in the BECASHAWC2 model. A material with unidirectional fibres and a highly flexible matrix material is stacked in different orientations to design different blade materials. These flexible materials are introduced in specific locations of the blade. The design exploration approach makes it possible to design and evaluate many different blades using different flexible materials at different locations. The current results show that the best option is to use the flexible material with fibres only in longitudinal and transverse direction in the skin of the blade. Not replacing the full skin but only the part of the root up to the middle of the blade results in the best flexible design. This best design has a reduction in maximum thrust force and maximum root bending moment of respectively 23 % and 26 % compared with the original blade, easily exceeding the predefined goal of 10 %. This significant load reduction is due to a significant blade twist rotation thereby reducing the area exposed to the wind. The annual energy yield is not compromised, it even shows a considerable 11 % increase due to a stall delay effect in the higher wind regimes which is also caused by an increase in blade twist. The best flexible design is a preliminary design that shows promising results. These results show that by using flexible materials in the blade skin a significant load reduction is combined with an increase in energy. This indicates an untapped potential for future wind energy which makes further research on this topic recommended.

Traditionally, the design of the Horizontal Axis Wind Turbine (HAWT) has relied on momentum-based methods such as the Blade Element-Momentum (BEM) models. With the growth of computational power, optimization of wind turbines by using Computational Fluid Dynamics (CFD) for the explicit wake modelling is becoming more mainstream. Wake modelling is also conveniently handled by Lagrangian vortex methods, which offer the advantage of auto-adaptivity regarding multi-scale vortical flows and distribution of vorticity in space. The current thesis work focusses on the class of Vortex Particle Methods (VPM) which allows for the efficient treatment of complex wake aerodynamics, especially when the connectivity of vortex filaments plays a significant role, such as with blade-wake interactions and viscous or turbulent diffusion.

This book presents the view of European wind energy experts on the long-term research challenges to be solved in order to develop wind energy beyond the applications of today and tomorrow. By this book, the European Academy of Wind Energy (eawe), representing universities and institutes with a significant wind energy programme in 14 countries, wants to: •identify current technological and scientific barriers and to stimulate new creative ideas to overcome these barriers •define priorities for future scientific research •rethink our scientific view of wind energy •stimulate the cooperation among researchers in fundamental and applied sciences towards wind energy research The eawe has discussed these long-term research with an explicit focus on a longer-term perspective, in contrast to research agendas addressing short- to medium-term research activities. In other words, this long-term research agenda is driven by problems and curiosity, addressing basic research and fundamental knowledge in 11 research areas, ranging from physics and design to environmental and societal aspects. Because of the very nature of this initiative, this document does not intend to be permanent or complete. It shows the vision of the experts of the European Academy of Wind Energy, but other views may be possible. The eawe sincerely hopes that it will spur an even more intensive discussion worldwide within the wind energy community.@en

Blade Element Momentum (BEM) is the most important aerodynamic analysis method for wind turbines. BEM is derived assuming stationary conditions, which limits its ability to model the unsteady aerodynamic effects. This becomes increasingly relevant for the flexible blades of current large-scale turbines, and the employment of passive and active aerodynamic control strategies, such as yaw, pitch control and smart rotor control. Currently, sub-models are included to consider the unsteady aerodynamic effects for wind turbine design. Previous research developed several dynamic-inflow engineering models to be integrated into BEM, to account for the unsteady flow acceleration. However, their applicability for unsteady load and the relative performance between the models are not fully known. The development of the dynamic wake of an actuator disc under unsteady load needs further understanding, to improve the engineering prediction of dynamic-inflow effect. This research aims to evaluate the accuracy of BEM with current dynamic-inflow engineering models; to further understand the dynamic wake flow-field of an actuator disc undergoing unsteady load; to improve current dynamic-inflow engineering models for wind turbine design using numerical and experimental approaches. A free wake vortex ring (FWVR) model is firstly developed. The accuracy of BEM with current dynamic-inflow engineering models of Pitt-Peters, Øye and ECN in predicting the induction of an actuator disc with unsteady load is verified using the developed FWVR model. The wake flow response of an actuator disc undergoing unsteady loads is studied experimentally by using a disc model with variable porosity. The unsteady load is generated by a ramp type variation of porosity of the disc, at several reduced times of the ramp motion. The wake development of an actuator disc undergoing the same unsteady load tested in the experiments is further studied using the FWVR model. The steady actuator-disc model is extended to unsteady load. Results from this linear actuator-disc model are compared with those from the FWVR model. Finally, a new engineering model is developed using the differential form of the Duhamel’s integrals of indicial response of the actuator-disc type vortex-models. The time constants of the indicial functions are obtained by the indicial responses of a linear and a nonlinear actuator-disc model, respectively. The work provides more insights into the wake development of an unsteady actuator disc. The experimental results create a database for validation of unsteady numerical models, in prediction of the dynamic induction in the near wake of an actuator disc or a rotor. The limitation of current dynamic-inflow engineering models are evaluated and discussed. The new engineering model, which is developed based on the indicial response of the nonlinear actuator-disc model, can better predict the dynamic-inflow effects, especially for the radial distribution of the dynamic-inflow effect. @en

Wind energy has reached a high degree ofmaturity: for wind-rich onshore locations, it is already competitive with conventional energy sources. However, for low-wind, remote and offshore regions, research efforts are still required to enhance its economic viability. While it is possible to reduce the cost of energy by upscaling wind turbines, it is believed that we may be approaching a plateau in turbine size. Beyond this plateau, the material costs associated with the high dynamic turbine loads would outweigh the benefits of increased energy capture. To postpone this plateau, research is currently being carried out in the active control of loads for lightweight, flexible rotors. Traditional control for wind turbines involves the use of fixed-structure low order controllers, the gains of which are often hand-tuned separately for each turbine class. However, for the increasingly multivariable plant, such time-invariant approaches may no longer yield good performance. As such, the thesis focusses specifically on datadriven control for these flexible turbines. First, different data-driven approaches in the literature are evaluated and categorised as two-step approaches; which involves distinct online identification and control steps; and direct approaches, which uses data to iteratively tune fixed-structure controller gains. The approaches need to be modified to be made tractable in real time for implementation on wind turbines. For time-varying plants, such as wind turbines, it is often interesting to performidentification repeatedly over time for the two-step data-driven approach. Conventional recursive identification is extended in this thesis through the use of the nuclear norm. The benefit of the nuclear norm is evident in that it increases responsiveness of the algorithm, through the suppression of the effect of external noise. Identification can be readily combined with repetitive control for reducing periodic loads in the Subspace Predictive Repetitive Control (SPRC) technique. SPRC can be performed in a restricted basis function subspace, thus reducing the computational complexity and providing smooth control signals. The control law is stabilising and performs well as long as the identification converges to relatively good estimates, and the system dynamics change slowly. For varying wind speed, the approach above would require continuous reïdentification. As an alternative, a direct data-driven approach, Iterative Feedback Tuning (IFT) has been extended to gain-schedule tuning and for designing a Linear Parameter- Varying (LPV) controller for an LPV plant. This requires an exponential increase in the number of tuning experiments per iteration; however, structure can be used to reduce computational complexity. IFT-LPV converges to a locally optimal low-order controller. These data-driven approaches are evaluated for the load control of flexible rotors. A review of the state of the art shows that, for the low-frequency region of the load spectrum, full-span pitch control has demonstrable control authority. For higher frequencies, among the new actuators, it is found that trailing-edge flaps have the highest level of technological maturity. Aeroservoelastic simulations are carried out to show the potential of the data-driven approaches. SPRC is able to adaptively tune itself to achieve average blade load reductions close to those achieved by conventional approaches under similar conditions. For these load reductions the actuator duty is roughly half of that with the conventional approach. IFT-LPV has been used to tune a feedforward controller that works on similar basis functions scheduled on the azimuth. It can provide the correct control action irrespective of wind conditions. To expand the load control design space, pitch control is designed to stabilise an upwind turbine in yaw, without the yaw drive. This approach enables a trade-off between blade and support structure loads. SPRC is then investigated with wind tunnel experiments for pitch control of a scaled wind turbine. It reduces deterministic loads by over 60%with strict control over the pitch activity, and can also compensate for asymmetric blade control authority and changed operating conditions adaptively. Further, on this setup, the concept of IPC has been shown to perform yaw stabilisation for an upwind turbine for the first time. The setup blades are then redesigned to include free-floating trailing-edge flaps. First-principles models are set up for the system, and it is found that the system shows a low wind-speed form of flutter; this is validated experimentally. Recursive identification, using the nuclear norm, is able to track the unstable mode damping, and detect flutter twice as fast as conventional methods. Finally, a feedforward controller is tuned using IFT for combined pitch and flap control; the load peaks at 1P and 2P are almost entirely attenuated. IFT is also able to tune an linear gain schedule for operation across a range of wind speeds. It is concluded that iterative methods for data-driven control perform well for the highly uncertain control problem of flexible rotor load alleviation. For this, use has to be made of the structure of the problem. The two-step approach (such as SPRC), with combined recursive identification and control law synthesis, provides a convex first approximation of the desired controller. With the help of direct approaches, (like IFT-LPV), the controller structure can be reduced and fine-tuned to improve the control performance. Such quasi-feedforward data-driven approaches can complement the existing turbine control structure and achieve enhanced load control performance for flexible rotors. @en