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We present a new model to estimate the performance of a wind turbine operating in misaligned conditions. The model is based on the classic momentum and lifting-line theories, considering a misaligned rotor as a lifting wing of finite span, and accounts for the combined effects of both yaw and uptilt angles. Improving on the classical empirical cosine law in widespread use, the new model reveals the dependency of power not only on the misalignment angle, but also on some rotor design parameters and – crucially – on the way a rotor is governed when it is yawed out of the wind. We show how the model can be readily integrated with arbitrary control laws below, above, and around the rated wind speed. Additionally, the model also shows that a sheared inflow is responsible for the observed lack of symmetry for positive and negative misalignment angles. Notwithstanding its simplicity and insignificant computational cost, the new proposed approach is in excellent agreement with large eddy simulations (LESs) and wind tunnel experiments. Building on the new model, we derive the optimal control strategy for maximizing power on a misaligned rotor. Additionally, we maximize the total power of a cluster of two turbines by wake steering, improving on the solution based on the cosine law.

Control co-design is a promising approach for wind turbine design due to the importance of the controller in power production, stability, load alleviation, and the resulting coupled effects on the sizing of the turbine components. However, the high computational effort required to solve optimization problems with added control design variables is a major obstacle to quantifying the benefit of this approach. In this work, we propose a methodology to identify if a design problem can benefit from control co-design. The estimation method, based on post-optimum sensitivity analysis, quantifies how the optimal objective value varies with a change in control tuning. The performance of the method is evaluated on a tower design optimization problem, where fatigue load constraints are a major driver, and using a linear quadratic regulator targeting fatigue load alleviation. We use the gradient-based multi-disciplinary optimization framework Cp-max. Fatigue damage is evaluated with time-domain simulations corresponding to the certification standards. The estimation method applied to the optimal tower mass and optimal cost of energy show good agreement with the results of the control co-design optimization while using only a fraction of the computational effort. Our results additionally show that there may be little benefit to using control co-design in the presence of an active frequency constraint. However, for a soft–soft tower configuration where the resonance can be avoided with active control, using control co-design results in a taller tower with reduced mass.

This paper demonstrates the observation of wind shear and veer directly from the operational response of a wind turbine equipped with blade load sensors. Two independent neural-based observers, one for shear and one for veer, are first trained using a machine-learning approach and then used to produce estimates of these two wind characteristics from measured blade load harmonics. The study is based on a dataset collected at an experimental test site featuring a highly instrumented 8 MW wind turbine, an IEC-compliant (International Electrotechnical Commission) met mast, and a vertical profiling lidar reaching above the rotor top. The present study reports the first demonstration of the measurement of wind veer with this technology and the first validation of shear and veer with respect to lidar measurements spanning the whole rotor height. Results are presented in terms of correlations, exemplary time histories, and aggregated statistical metrics. Measurements of shear and veer produced by the observers are very similar to the ones obtained with the widely adopted profiling lidar while avoiding its complexity and associated costs.

Wind energy is foundational for achieving 100 % renewable electricity production, and significant innovation is required as the grid expands and accommodates hybrid plant systems, energy-intensive products such as fuels, and a transitioning transportation sector. The sizable investments required for wind power plant development and integration make the financial and operational risks of change very high in all applications but especially offshore. Dependence on a high level of modeling and simulation accuracy to mitigate risk and ensure operational performance is essential. Therefore, the modeling chain from the large-scale inflow down to the material microstructure, and all the steps in between, needs to predict how the wind turbine system will respond and perform to allow innovative solutions to enter commercial application. Critical unknowns in the design, manufacturing, and operability of future turbine and plant systems are articulated, and recommendations for research action are laid out. This article focuses on the many unknowns that affect the ability to push the frontiers in the design of turbine and plant systems. Modern turbine rotors operate through the entire atmospheric boundary layer, outside the bounds of historic design assumptions, which requires reassessing design processes and approaches. Traditional aerodynamics and aeroelastic modeling approaches are pressing against the limits of applicability for the size and flexibility of future architectures and flow physics fundamentals. Offshore wind turbines have additional motion and hydrodynamic load drivers that are formidable modeling challenges. Uncertainty in turbine wakes complicates structural loading and energy production estimates, both around a single plant and for downstream plants, which requires innovation in plant operations and flow control to achieve full energy capture and load alleviation potential. Opportunities in co-design can bring controls upstream into design optimization if captured in design-level models of the physical phenomena. It is a research challenge to integrate improved materials into the manufacture of ever-larger components while maintaining quality and reducing cost. High-performance computing used in high-fidelity, physics-resolving simulations offer opportunities to improve design tools through artificial intelligence and machine learning, but even the high-fidelity tools are yet to be fully validated. Finally, key actions needed to continue the progress of wind energy technology toward even lower cost and greater functionality are recommended.

Wind turbines are designed to minimize the economic cost of energy, a metric aimed at making wind competitive with other energy-producing technologies. However, now that wind energy is competitive, how can we increase its value for the environment and for society? And how much would environmental and societal gains cost other stakeholders, such as investors or consumers? This paper tries to answer these questions, limitedly to climate-related environmental impacts, from the perspective of wind turbine design. Although wind turbines produce green renewable energy, they also have various impacts on the environment, as do all human endeavors. Among all impacts, the present work adopts the environmental effects produced by a turbine over its entire life cycle, expressed in terms of CO2-equivalent emissions. A new approach to design is proposed, whereby Pareto fronts of solutions are computed to define optimal trade-offs between economic and environmental goals. The new proposed methodology is demonstrated on the redesign of a baseline 3 MW wind turbine at two locations in Germany, differing for typical wind speeds but within the same energy market. Among other results, it is found that, in these conditions, a 1 % increase in the cost of energy can buy about a 5 % decrease in the environmental impact of the turbine. Additionally, it is also observed that in the specific case of Germany, very low-specific-power designs are typically favored, because they produce more energy at low wind speeds, where both the economic and environmental values of wind are higher. Furthermore, it is found that the CO2-equivalent emissions displaced by a wind turbine are 1 order of magnitude larger than the produced emissions. Although limited to the sole optimization of wind-generating assets at two different locations, these results suggest the existence of new opportunities for the future development of wind energy where, by shifting the focus slightly away from a purely cost-driven short-term perspective, longer-term benefits for the environment (and, in turn, for society) may be obtained.

This paper describes a method to identify the heterogenous flow characteristics that develop within a wind farm in its interaction with the atmospheric boundary layer. The whole farm is used as a distributed sensor, which gauges through its wind turbines the flow field developing within its boundaries. The proposed method is based on augmenting an engineering wake model with an unknown correction field, which results in a hybrid (grey-box) model. Operational SCADA (supervisory control and data acquisition) data are then used to simultaneously learn the parameters that describe the correction field and to tune the ones of the engineering wake model. The resulting monolithic maximum likelihood estimation is in general ill-conditioned because of the collinearity and low observability of the redundant parameters. This problem is solved by a singular value decomposition, which discards parameter combinations that are not identifiable given the informational content of the dataset and solves only for the identifiable ones. The farm-as-a-sensor approach is demonstrated on two wind plants with very different characteristics: a relatively small onshore farm at a site with moderate terrain complexity and a large offshore one in close proximity to the coastline. In both cases, the data-driven correction and tuning of the grey-box model results in much improved prediction capabilities. The identified flow fields reveal the presence of significant terrain-induced effects in the onshore case and of large direction and ambient-condition-dependent intra-plant effects in the offshore one. Analysis of the coordinate transformation and mode shapes generated by the singular value decomposition help explain relevant characteristics of the solution, as well as couplings among modeling parameters. Computational fluid dynamics (CFD) simulations are used for confirming the plausibility of the identified flow fields.

The formulation of parametric online rainflow counting implements the standard fatigue estimation process and a stress history in the cost function of a model predictive controller. The formulation is tested in realistic simulation scenarios in which the states are estimated by a moving horizon estimator and the wind is predicted by a lidar simulator. The tuning procedure for the controller toolchain is carefully explained. In comparison to a conventional model predictive controller (MPC) in a turbulent wind setting, the novel formulation is especially superior with low lidar quality, benefits more from the availability of wind prediction, and exhibits a more robust performance with shorter prediction horizons. A simulation excerpt with the novel formulation provides deeper insight into the update of the stress history and the fatigue cost parameters. Finally, in a deterministic gust setting, both the conventional and the novel MPC – despite their completely different fatigue costs – exhibit similar pitch behavior and tower oscillations.

This paper presents a feasibility analysis of vertical wake steering for floating turbines by differential ballast control. This new concept is based on the idea of pitching the floater with respect to the water surface, thereby achieving a desired tilt of the turbine rotor disk. The pitch attitude is controlled by moving water ballast among the columns of the floater. This study considers the application of differential ballast control to a conceptual 10 MW wind turbine installed on two platforms, differing in size, weight, and geometry. The analysis considers the following: (a) the aerodynamic effects caused by rotor tilt on the power capture of the wake-steering turbine and at various downstream distances in its wake; (b) the effects of tilting on fatigue and ultimate loads, limitedly to one of the two turbine-platform layouts; and (c) for both configurations, the necessary amount of water movement, the time to achieve a desired attitude, and the associated energy expenditure. Results indicate that – in accordance with previous research – steering the wake towards the sea surface leads to larger power gains than steering it towards the sky. Limitedly to the structural analysis conducted on one of the turbine-platform configurations, it appears that these gains can be obtained with only minor effects on loads, assuming a cautious application of vertical steering only in benign ambient conditions. Additionally, it is found that rotor tilt can be achieved on the order of minutes for the lighter of the two configurations, with reasonable water ballast movements. Although the analysis is preliminary and limited to the specific cases considered here, results seem to suggest that the concept is not unrealistic and should be further investigated as a possible means to achieve variable tilt control for vertical wake steering in floating turbines.

This paper describes the design and characterization of a scaled wind turbine model, conceived to support wake and wind farm control experiments in a boundary layer wind tunnel. The turbine has a rotor diameter of 0.6 m and was designed to match the circulation distribution of a target conceptual full-scale turbine at its design tip speed ratio. In order to enable the testing of plant-level control strategies, the model is equipped with pitch, torque and yaw actuation and is sensorized with integrated load cells, rotor azimuth and blade pitch encoders. After describing the design of the turbine, its steady-state performance and wake characteristics are assessed by conducting experiments in two different wind tunnels, in laminar and turbulent conditions, collecting wake data with different measurement techniques. A large-eddy simulator coupled to an actuator-line model is used to develop a digital replica of the turbine and of the wind tunnel. For increased accuracy, the polars of the low-Reynolds-number airfoil used in the numerical model are tuned directly from measurements obtained from the rotor in operation in the wind tunnel. Results indicate that the scaled turbine performs as expected: measurements are repeatable and consistent, and the wake appears to have a realistic behavior in line with expectations and with a similar but slightly larger scaled model turbine. Furthermore, the predictions of the numerical model are well in line with experimental observations.

This paper explores the potential benefits brought by the integration of lidar-assisted control (LAC) in the design of a wind turbine. The study identifies which design drivers can be relaxed by LAC, as well as by how much these drivers could be reduced before other conditions become the drivers. A generic LAC load-reduction model is defined and used to redesign the rotor and tower of three representative turbines, differing in terms of wind class, size, and power rating. The load reductions enabled by LAC are used to save mass, increase hub height, or extend lifetime. For the first two strategies, results suggest only modest reductions in the levelized cost of energy, with potential benefits essentially limited to the tower of a large offshore machine. On the other hand, lifetime extension appears to be the most effective way of exploiting the effects of LAC.

The aim of this paper is to analyze to which extent wind tunnel experiments can represent the behavior of full-scale wind turbine wakes. The question is relevant because on the one hand scaled models are extensively used for wake and farm control studies, whereas on the other hand not all wake-relevant physical characteristics of a full-scale turbine can be exactly matched by a scaled model. In particular, a detailed scaling analysis reveals that the scaled model accurately represents the principal physical phenomena taking place in the outer shell of the near wake, whereas differences exist in its inner core. A large-eddy simulation actuator-line method is first validated with respect to wind tunnel measurements and then used to perform a thorough comparison of the wake at the two scales. It is concluded that, notwithstanding the existence of some mismatched effects, the scaled wake is remarkably similar to the full-scale one, except in the immediate proximity of the rotor.

This paper formulates laws for scaling wind turbine rotors. Although the analysis is general, the article primarily focuses on the subscaling problem, i.e., on the design of a smaller-sized model that mimics a full-scale machine. The present study considers both the steady-state and transient response cases, including the effects of aerodynamic, elastic, inertial, and gravitational forces. The analysis reveals the changes to physical characteristics induced by a generic change of scale, indicates which characteristics can be matched faithfully by a subscaled model, and states the conditions that must be fulfilled for desired matchings to hold. Based on the scaling laws formulated here, the article continues by considering the problem of designing scaled rotors that match desired indicators of a full-scale reference. To better illustrate the challenges implicit in scaling and the necessary tradeoffs and approximations, two different approaches are contrasted. The first consists in a straightforward geometric zooming. An analysis of the consequences of zooming reveals that, although apparently simple, this method is often not applicable in practice, because of physical and manufacturing limitations. This motivates the formulation of scaling as a constrained optimal aerodynamic and structural matching problem of wide applicability. Practical illustrations are given considering the scaling of a large reference 10 MW wind turbine of about 180 m in diameter down to three different sizes of 54, 27, and 2.8 m. Results indicate that, with the proper choices, even models characterized by very significant scaling factors can accurately match several key performance indicators. Additionally, when an exact match is not possible, relevant trends can at least be captured.

A previously published wind sensing method is applied to an experimental dataset obtained from a 3.5 MW turbine. The method is based on a load-wind model that correlates once-per-revolution blade load harmonics to rotor-equivalent shears and wind directions. Loads measured during turbine operation are used to estimate online – through the load-wind model – the inflow at the rotor disk, thereby turning the whole turbine into a sort of generalized anemometer. The experimental dataset consists of synchronous measurements of loads, from blade-mounted strain gages, and of the inflow, obtained from a nearby met mast. As the mast reaches only to hub height, a second independent method is used to extend the met-mast-measured shear above hub height to cover the entire rotor disk. Part of the dataset is first used to identify the load-wind model, and then the performance of the wind observer is characterized with the rest of the data. Although the experimental setup falls short of providing a thorough validation of the method, it still allows for a realistic practical demonstration of some of its main features. Results indicate a good quality of the estimated linear shear both in terms of 1 and 10 min averages and of resolved time histories, with mean average errors around 0.04. A similarly accuracy is found in the estimation of the yaw misalignment, with mean errors typically below 3∘.

The concept of wake steering on wind farms for power maximization has gained significant popularity over the last decade. Recent field trials described in the literature not only demonstrate the real potential of wake steering on commercial wind farms but also show that wake steering does not yet consistently lead to an increase in energy production for all inflow conditions. Moreover, a recent survey among experts shows that validation of the concept currently remains the largest barrier to adoption. In response, this article presents the results of a field experiment investigating wake steering in three-turbine arrays at an onshore wind farm in Italy. This experiment was performed as part of the European CL-Windcon project. While important, this experiment excludes an analysis of the structural loads and focuses solely on the effects of wake steering on power production. The measurements show increases in power production of up to 35 % for two-turbine interactions and up to 16 % for three-turbine interactions. However, losses in power production are seen for various regions of wind directions too. In addition to the gains achieved through wake steering at downstream turbines, more interesting to note is that a significant share in gains is from the upstream turbines, showing an increased power production of the yawed turbine itself compared to baseline operation for some wind directions. Furthermore, the surrogate model, while capturing the general trends of wake interaction, lacks the details necessary to accurately represent the measurements. This article supports the notion that further research is necessary, notably on the topics of wind farm modeling and experiment design, before wake steering will lead to consistent energy gains on commercial wind farms.

A new method is described to identify the aerodynamic characteristics of blade airfoils directly from operational data of the turbine. Improving on a previously published approach, the present method is based on a new maximum likelihood formulation that includes errors in both the outputs and the inputs, generalizing the classical error-in-the-outputs-only formulation. Since many parameters are necessary to meaningfully represent the behavior of airfoil polars as functions of angle of attack and Reynolds number, the approach uses a singular value decomposition to solve for a reduced set of observable parameters. The new method is demonstrated by identifying high-quality polars for small-scale wind turbines used in wind tunnel experiments for wake and wind farm control research.

The performance of an open-loop wake-steering controller is investigated with a new unique set of wind tunnel experiments. A cluster of three scaled wind turbines, placed on a large turntable, is exposed to a turbulent inflow and dynamically changing wind directions, resulting in dynamically varying wake interactions. The changes in wind direction were sourced and scaled from a field-measured time history and mirrored onto the movement of the turntable. Exploiting the known, repeatable, and controllable conditions of the wind tunnel, this study investigates the following effects: fidelity of the model used for synthesizing the controller, assumption of steady-state vs. dynamic plant behavior, wind direction uncertainty, the robustness of the formulation in regard to this uncertainty, and a finite yaw rate. The results were analyzed for power production of the cluster, fatigue loads, and yaw actuator duty cycle. The study highlights the importance of using a robust formulation and plant flow models of appropriate fidelity and the existence of possible margins for improvement by the use of dynamic controllers.

This paper presents the field validation of a method to estimate the local wind speed on different sectors of a turbine rotor disk. Each rotating blade is used as a scanning sensor that, traveling across the rotor disk, samples the inflow. From the local speed estimates, the method can reconstruct the vertical wind shear and detect the presence and location on an impinging wake shed by an upstream wind turbine. Shear and wake awareness have multiple uses, from turbine and farm control to monitoring and forecasting. This validation study is conducted with an experimental data set obtained with two multi-megawatt wind turbines and a hub-tall met mast. Practical and simple procedures are presented and demonstrated to correct for the possible miscalibration of sensors. Results indicate a very good correlation between the estimated vertical shear and the one measured by the met mast. Additionally, the proposed method exhibits a remarkable ability to locate and track the motion of an impinging wake on an affected rotor.

This paper describes a method to improve and correct an engineering wind farm flow model by using operational data. Wind farm models represent an approximation of reality and therefore often lack accuracy and suffer from unmodeled physical effects. It is shown here that, by surgically inserting error terms in the model equations and learning the associated parameters from operational data, the performance of a baseline model can be improved significantly. Compared to a purely data-driven approach, the resulting model encapsulates prior knowledge beyond that contained in the training data set, which has a number of advantages. To assure a wide applicability of the method – also including existing assets – learning here is purely driven by standard operational (SCADA) data. The proposed method is demonstrated first using a cluster of three scaled wind turbines operated in a boundary layer wind tunnel. Given that inflow, wakes, and operational conditions can be precisely measured in the repeatable and controllable environment of the wind tunnel, this first application serves the purpose of showing that the correct error terms can indeed be identified. Next, the method is applied to a real wind farm situated in a complex terrain environment. Here again learning from operational data is shown to improve the prediction capabilities of the baseline model.

In this paper, an analytical wake model with a double-Gaussian velocity distribution is presented, improving on a similar formulation by Keane et al. (2016). The choice of a double-Gaussian shape function is motivated by the behavior of the near-wake region that is observed in numerical simulations and experimental measurements. The method is based on the conservation of momentum principle, while stream-tube theory is used to determine the wake expansion at the tube outlet. The model is calibrated and validated using large eddy simulations replicating scaled wind turbine experiments. Results show that the tuned double-Gaussian model is superior to a single-Gaussian formulation in the near-wake region.

The present paper characterizes the performance of non-intrusive uncertainty quantification methods for aeroservoelastic wind turbine analysis. Two different methods are considered, namely non-intrusive polynomial chaos expansion and Kriging. Aleatory uncertainties are associated with the wind inflow characteristics and the blade surface state, on account of soiling and/or erosion, and propagated throughout the aeroservoelastic model of a large conceptual offshore wind turbine. Results are compared with a brute-force extensive Monte Carlo sampling, which is used as benchmark. Both methods require at least 1 order of magnitude less simulations than Monte Carlo, with a slight advantage of Kriging over polynomial chaos expansion. The analysis of the solution space clearly indicates the effects of uncertainties and their couplings, and highlights some possible shortcomings of current mostly deterministic approaches based on safety factors.

The present paper further develops and experimentally validates the previously published idea of estimating the wind inflow at a turbine rotor disk from the machine response. A linear model is formulated that relates one per revolution (1P) harmonics of the in- and out-of-plane blade root bending moments to four wind parameters, representing vertical and horizontal shears and misalignment angles. Improving on this concept, the present work exploits the rotationally symmetric behavior of the rotor in the formulation of the load-wind model. In a nutshell, this means that the effects on the loads of the vertical shear and misalignment are the same as those of the horizontal quantities, simply shifted by π∕2. This results in a simpler identification of the model, which needs a reduced set of observations. The performance of the proposed method is first tested in a simulation environment and then validated with an experimental data set obtained with an aeroelastically scaled turbine model in a boundary layer wind tunnel.

This paper applies a large-eddy actuator line approach to the simulation of wind turbine wakes. In addition to normal operating conditions, a specific focus of the paper is on wake manipulation, which is performed here by derating, yaw misalignment and cyclic pitching of the blades. With the purpose of clarifying the ability of LES methods to represent conditions that are relevant for wind farm control, numerical simulations are compared to experimental observations obtained in a boundary layer wind tunnel with scaled wind turbine models. Results indicate a good overall matching of simulations with experiments. Low-turbulence test cases appear to be more challenging than moderate- and high-turbulence ones due to the need for denser grids to limit numerical diffusion and accurately resolve tip-shed vortices in the near-wake region.

The size of wind turbines has been steadily growing in the pursuit of a lower cost of energy by an increased wind capture. Within this trend, the vast majority of wind turbine rotors have been designed based on the conventional three-bladed upwind concept. This paper aims at assessing the optimality of this configuration with respect to a three-bladed downwind design, with and without an actively controlled variable coning used to reduce the cantilever loading of the blades. Results indicate that a conventional design appears difficult to beat even at these turbine sizes, although a downwind nonaligned configuration might be an interesting alternative.

In this work, a new algorithm is presented to correct for pitch misalignment imbalances of wind turbine rotors. The method uses signals measured in the fixed frame of the machine, typically in the form of accelerations or loads. The amplitude of the one per revolution signal harmonic is used to quantify the imbalance, while its phase is used to locate the unbalanced blade(s). The near linearity of the unknown relationship between harmonic amplitude and pitch misalignment is used to derive a simple algorithm that iteratively rebalances the rotor. This operation is conducted while the machine is in operation, without the need for shutting it down. The method is not only applicable to the case of a single misaligned blade, but also to the generic case of multiple concurrent imbalances. Apart from the availability of acceleration or load sensors, the method requires the ability of the rotor blades to be commanded independently from one another, which is typically possible on many modern machines. The new method is demonstrated in a realistic simulation environment using an aeroservoelastic wind turbine model in a variety of wind and operating conditions.

The wind field leaves its fingerprint on the rotor response. This fact can be exploited by using the rotor as a sensor: by looking at the rotor response, in the present case in terms of blade loads, one may infer the wind characteristics. This paper describes a wind state observer that estimates four wind parameters, namely the vertical and horizontal shears and the yaw and upflow misalignment angles, from out-of-plane and in-plane blade bending moments. The resulting observer provides on-rotor wind inflow characteristics that can be exploited for wind turbine and wind farm control. The proposed formulation is evaluated through extensive numerical simulations in turbulent and nonturbulent wind conditions using a high-fidelity aeroservoelastic model of a multi-MW wind turbine.

This paper investigates the load alleviation capabilities of an articulated tip device, where the outermost portion of the blade can rotate with respect to the rest of the blade. Passive, semi-passive and active solutions are developed for the tip rotation. In the passive and semi-passive configurations tip pitching is mainly driven by aerodynamic loads, while for the active case the rotation is obtained with an actuator commanded by a feedback control law. Each configuration is analyzed and tested using a high-fidelity aeroservoelastic simulation environment, by considering standard operative conditions as well as fault situations. The potential benefits of the proposed blade tip concepts are discussed in terms of performance and robustness.

The formulation is model-independent, in the sense that it does not require knowledge of the equations of motion of the periodic system being analyzed, and it is applicable to an arbitrary number of blades and to any configuration of the machine. In addition, as wind turbulence can be viewed as a stochastic disturbance, the method is also applicable to real wind turbines operating in the field. The characteristics of the new method are verified first with a simplified analytical model and then using a high-fidelity multi-body model of a multi-MW wind turbine. Results are compared with those obtained by the well-known operational modal analysis approach.

This paper is concerned with the holistic optimization of wind turbines. A multi-disciplinary optimization procedure is presented that marries the overall sizing of the machine in terms of rotor diameter and tower height (often termed “preliminary design”) with the detailed sizing of its aerodynamic and structural components. The proposed combined preliminary–detailed approach sizes the overall machine while taking into full account the subtle and complicated couplings that arise due to the mutual effects of aerodynamic and structural choices. Since controls play a central role in dictating performance and loads, control laws are also updated accordingly during optimization. As part of the approach, rotor and tower are sized simultaneously, even in this case capturing the mutual effects of one component over the other due to the tip clearance constraint. The procedure, here driven by detailed models of the cost of energy, results in a complete aero-structural design of the machine, including its associated control laws. The proposed methods are tested on the redesign of two wind turbines, a 2.2 MW onshore machine and a large 10 MW offshore one. In both cases, the optimization leads to significant changes with respect to the initial baseline configurations, with noticeable reductions in the cost of energy. The novel procedures are also exercised on the design of low-induction rotors for both considered wind turbines, showing that they are typically not competitive with conventional high-efficiency rotors.

The European Academy of Wind Energy (eawe), representing universities and institutes with a significant wind energy programme in 14 countries, has discussed the long-term research challenges in wind energy. In contrast to research agendas addressing short- to medium-term research activities, this eawe document takes a longer-term perspective, addressing the scientific knowledge base that is required to develop wind energy beyond the applications of today and tomorrow. 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 eawe, but other views may be possible. We sincerely hope that it will spur an even more intensive discussion worldwide within the wind energy community.