Wind energy is the world's fastest growing source of electricity production; if this trend is to continue, sites that are plentiful in terms of wind velocity must be efficiently utilized. Many such sites are located in cold, wet regions such as the Swiss Alps, the Scandinavian coastline, and many areas of China and North America, where the predicted power curves can be of low accuracy, and the performance often deviates significantly from the expected performance. There are often prolonged shutdown and inefficient heating cycles, both of which may be unnecessary. Thus, further understanding of the effects of ice formation on wind turbine blades is required. Experimental and computational studies are undertaken to examine the effects of ice formation on wind turbine peiformance. The experiments are conducted on a dynamically scaled model in the wind turbine test facility at ETH Zurich. The central element of the facility is a water towing tank that enables full-scale nondimensional parameters to be more closely matched on a subscale model than in a wind tunnel. A novel technique is developed to yield accurate measurements of wind turbine performance, incorporating the use of a torquemeter with a series of systematic measurements. These measurements are complemented by predictions obtained using a commercial Reynolds-Averaged Navier-Stokes computational fiuid dynamics code. The measured and predicted results show that icing typical of that found at the Guetsch Alpine Test Site (2330 m altitude) can reduce the power coefficient by up to 22% and the annual energy production (AEP) by up to 2%. icing in the blade tip region, 95-100% blade span, has the most pronounced effect on the wind turbine's performance. For wind turbines in more extreme icing conditions typical of those in Bern Jura, for example, icing can result in up to 17% losses in AEP. Icing at high altitude sites does not cause significant AEP losses, whereas icing at lower altitude sites can have a significant impact on AEP. Thus, the classification of icing is a key to the further development of prediction tools. It would be advantageous to tailor blade heating for prevention of ice buildup on the blade's tip region. An "extreme" icing predictive tool for the project development of wind farms in regions that are highly susceptible to icing would be beneficial to wind energy developers.

The application of the commercial CFD code, FLUENT, to sports ball aerodynamics was assessed and a validated 3D analysis technique was established for balls that have been scanned with a 3D laser scanner or drawn in CAD. The technique was used to examine the effects of surface geometry on the aerodynamic behaviour of soccer balls by comparing the flow around different balls and predicting the aerodynamic force coefficients. The validation process included performing CFD studies on 3D smooth spheres and various soccer balls, and comparing the results to wind tunnel tests and flow visualisation.The CFD technique used a surface wrapping meshing method and the Reynolds-Averaged NavierStokes approach with the realizable k-ε turbulence model, which was found to be able to predict the drag, lift and side force coefficients (C D , C L and C S ) reliably, to compare the wake behaviour, and to give good pressure distributions near the stagnation point. The main limitations of the technique with the available computational resources were its inability to accurately predict boundary layer transition or growth, but despite this, several conclusions could be drawn regarding soccer ball aerodynamics. C D was not significantly different between balls. C L and C S were found to be significantly affected by the orientation of the ball relative to its direction of travel, meaning that balls kicked with low amounts of spin could experience quasi-steady lift and side forces and move erratically from side-to-side or up and

In the next decade, further digitalisation of the entire wind energy project lifecycle is expected to be a major driver for reducing project costs and risks. In this paper, a literature review on the challenges related to implementation of digitalisation in the wind energy industry is first carried out, showing that there is a strong need for new solutions that enable co-innovation within and between organisations. Therefore, a new collaboration method based on a digital ecosystem is developed and demonstrated. The method is centred around specific “challenges”, which are defined by “challenge providers” within a topical “space” and made available to participants via a digital platform. The data required in order to solve a particular “challenge” are provided by the “challenge providers” under the confidentiality conditions they specify. The method is demonstrated via a case study, the EDP Wind Turbine Fault Detection Challenge. Six submitted solutions using diverse approaches are evaluated. Two of the solutions perform significantly better than EDP’s existing solution in terms of Total Prediction Costs (saving up to €120,000). The digital ecosystem is found to be a promising solution for enabling co-innovation in wind energy in general, providing a number of tangible benefits for both challenge and solution providers.

Abstract. The accuracy of the estimation of the wind resource has an enormous effect on the expected rate of return of a wind energy project. Due to the complex nature of the weather and the wind flow over the earth's surface, it can be very challenging to measure and model the wind resource correctly. For a given project, the modeller is faced with a difficult choice of a wide range of simulation tools with varying accuracies (or skill) and costs. In this work, a new method for helping wind modellers choose the most cost-effective model for a given project is developed by applying six different Computational Fluid Dynamics tools to simulate the Bolund Hill experiment and studying appropriate comparison metrics in detail. This is done by firstly defining various parameters for predicting the skill and cost scores before carrying out the simulations as well as for calculating skill and cost scores after carrying out the simulations. Weightings are then defined for these parameters, and values assigned to them for the six tools using a template containing pre-defined limits in a blind test. An iterative improvement process is applied by collecting inputs from the participants of the study. This allows a graph of predicted skill score against cost score to be produced, enabling modellers to choose the most cost-effective model without having to carry out the simulations beforehand. The most effective model is the one with the highest skill score for the lowest cost score, at the flattening-off part of the curve. The results show that this new method is successful, and that it is generally possible to apply it in order to choose the most appropriate model for a given project in advance. This is demonstrated by the good match between the shapes of the skill score against cost score curves before and after the simulations, and by the fact that the tool at the flattening-out point of the curve is the same before and after carrying out the simulations. It is also shown how important it is to take into account other factors that may affect the accuracy and costs of a wind modelling simulation as well as the quality of the aerodynamic equations and the run-time. Several improvements to the method are being worked on, by further examining the discrepancies between the predicted and actual cost and skill scores. Additionally, the method is being extended for calculating all wind directions and the Annual Energy Production, as well as to include mesoscale nesting or forcing. A large number of inputs are being collected as part of a simulation challenge in collaboration with IEA Wind Task 31. The method has a high potential to be extended to a wide range of other simulation applications.

In wind energy, the accuracy of the estimation of the wind resource has an enormous effect on the expected rate of return of a project. For a given project, the modeller is faced with a difficult choice of a wide range of simulation tools with varying accuracies and costs. In previous work, a new method for estimating the skill and cost scores of different wind modelling tools for a given project has been developed. Although this method worked well, it was shown that further studies are required for a wide range of input conditions and project types in order to develop project-specific transfer functions between the predicted and actual cost and skill scores. In this paper therefore, a new simulation challenge is designed with the goal of collecting comparison metrics data regarding the skill and cost scores of a range of different simulation tools for a complex terrain site, both before and after carrying out the simulations. The complex terrain site Perdigao is chosen for the challenge, due to the volume and quality of available measurement data and the complexity of the terrain. An initial data analysis and WAsP simulations allow mast 29 to be chosen for the input data and masts 7, 10, 20, 22, 25, 27, 34 and 37 for the validation data. The WAsP simulations are compared to previous WRF simulations and are found to capture the main features of the flow over the two ridges. The wind acceleration over the two ridges and the resulting maximum wind speeds at the peak of the ridges as well as the reduced velocity regions in the valley between the ridges are well captured. However, smaller-scale features such as small areas of separated flow seen in the WRF simulations cannot be captured by WAsP. This choice of mast for the challenge will therefore allow the capabilities of different tools for calculating flow in separated regions as well as on top of hills to be assessed.

Leading edge surface erosion is an emerging issue in wind turbine blade reliability, causing a reduction in power performance, aerodynamic loads imbalance, increased noise emission, and, ultimately, additional maintenance costs, and, if left untreated, it leads to the compromise of the functionality of the blade. In this work, we first propose an empirical spatio-temporal stochastic model for simulating leading edge erosion, to be used in conjunction with aeroelastic simulations, and subsequently present a deep learning model to be trained on simulated data, which aims to monitor leading edge erosion by detecting and classifying the degradation severity. This could help wind farm operators to reduce maintenance costs by planning cleaning and repair activities more efficiently. The main ingredients of the model include a damage process that progresses at random times, across multiple discrete states characterized by a non-homogeneous compound Poisson process, which is used to describe the random and time-dependent degradation of the blade surface, thus implicitly affecting its aerodynamic properties. The model allows for one, or more, zones along the span of the blades to be independently affected by erosion. The proposed model accounts for uncertainties in the local airfoil aerodynamics via parameterization of the lift and drag coefficients’ curves. The proposed model was used to generate a stochastic ensemble of degrading airfoil aerodynamic polars, for use in forward aero-servo-elastic simulations, where we computed the effect of leading edge erosion degradation on the dynamic response of a wind turbine under varying turbulent input inflow conditions. The dynamic response was chosen as a defining output as this relates to the output variable that is most commonly monitored under a structural health monitoring (SHM) regime. In this context, we further proposed an approach for spatio-temporal dependent diagnostics of leading erosion, namely, a deep learning attention-based Transformer, which we modified for classification tasks on slow degradation processes with long sequence multivariate time-series as inputs. We performed multiple sets of numerical experiments, aiming to evaluate the Transformer for diagnostics and assess its limitations. The results revealed Transformers as a potent method for diagnosis of such degradation processes. The attention-based mechanism allows the network to focus on different features at different time intervals for better prediction accuracy, especially for long time-series sequences representing a slow degradation process.

Two different measurement techniques are used to examine the effect of surface geometry on soccer ball trajectories. Five professional players are observed using high-speed video when taking curling free kicks with four different soccer balls. The input conditions are measured and the average launch velocity and spin are found to be approximately 24 m/s and 106 rad/s. It is found that the players can apply more spin (*50%) on average to one ball, which has a slightly rougher surface than the other balls. The trajectories for the same four balls fired at various velocities and spin rates across a sports hall using a bespoke firing device are captured using high-speed video cameras, and their drag and lift coefficients estimated. Balls with more panels are found to experience a higher lift coefficient. The drag coefficient results show a large amount of scatter, and it is difficult to distinguish between the balls. Using the results in a trajectory prediction programme it is found that increasing the number of panels from 14 to 32 can significantly alter the final position of a 20 m-curling free kick by up to 1 m.

Abstract. Understanding the uncertainties of wind resource assessments (WRAs) is key to reducing project risks, and this is particularly challenging in mountainous terrain. In the academic literature, many complex flow sites have been investigated, but they all focus on comparing wind speeds from selected wind directions and do not focus on the overall annual energy production (AEP). In this work, the importance of converting wind speed errors into AEP errors when evaluating wind energy projects is highlighted by comparing the results of seven different WRA workflows at five complex terrain sites. Although a systematic study involving the investigation of all possible varying parameters is not within the scope of this study, the results allow some of the different factors that could lead to this discrepancy being identified. The wind speed errors are assessed by comparing simulation results to wind speed measurements at validation locations. This is then extended to AEP estimations (without wake effects), showing that wind profile prediction accuracy does not translate directly or linearly to AEP accuracy. This is due to the specific conditions at the site, to differences in workflow set-ups between the sites and to differences in workflow AEP calculation methods. The results demonstrate the complexity of the combined factors contributing to WRA errors – even without including wake effects and other losses. This means that the wind model that produces the most accurate wind predictions for a certain wind direction over a certain time period does not always result in the most suitable model for the AEP estimation of a given complex terrain site. In fact, the large number of steps within the WRA process often lead to the choice of wind model being less important for the overall WRA accuracy than would be suggested by only looking at wind speeds. It is therefore concluded that it is vitally important for researchers to consider overall AEP – and all the steps towards calculating it – when evaluating simulation accuracies of flow over complex terrain. Future work will involve a systematic study of all the factors that could contribute to this effect.