We investigate wind–wave interactions within the marine atmospheric boundary layer during a storm event over the North Sea using a multiscale coupled modeling framework. Large-eddy simulation (LES) domains within the Weather Research and Forecasting model have been coupled with the Simulating WAves Nearshore (SWAN) model through the Coupled Ocean–Atmosphere–Wave–Sediment Transport (COAWST) system. The simulation consists of six nested domains, three outer mesoscale domains with horizontal grid spacings of 9.9, 3.3, and 1.1 km and three inner large-eddy simulation domains with coarse (100 m) and fine horizontal grid spacings. Wind and wave outputs from both the finest mesoscale domain and the coarsest LES domain are evaluated against metocean and lidar measurements collected during the storm. The results show that the coarsest LES domain represents wind speeds up to 150 m height more accurately than the mesoscale output, which is however associated with excessive simulated vertical wind shear. Above this height, the mesoscale and LES winds become much closer, both overestimating the measured winds. The measured wind direction is well captured across all domains. The significant wave height, peak wave period, and mean wave direction simulated by SWAN show better agreement with observations when forced by the LES output than the mesoscale output. Additionally, the coupled simulations exhibit stronger turbulence fluxes than the uncoupled simulations, which is clearly shown in the vertical profiles of velocity variances and covariances. Although it is a case study, these findings demonstrate the impact of high-resolution coupled modeling for capturing offshore boundary layer dynamics and on wind and wave predictions under severe weather conditions.
High-fidelity aeroelastic simulations provide accurate predictions of electrical power and structural fatigue loads in wind farms but remain computationally expensive for optimisation, control, and digital-twin applications. Machine-learning surrogate models offer an efficient alternative, and recent studies have shown that both surrogate architecture and inflow representation influence predictive performance. However, it remains unclear how the amount and form of retained rotor-plane inflow information affect electrical power and fatigue-load prediction. This study evaluates four input configurations derived from temporally aggregated rotor-plane inflow data from the publicly available HAWC2Farm Lillgrund dataset. A minimal baseline representation retains only the spatial averages of the temporal-mean wind-speed and temporal-variance fields, while a flattened full-grid representation retains all rotor-plane measurements. Principal component analysis (PCA) provides a compact representation of the full input, and a two-dimensional representation preserves the original rotor-plane topology. The baseline, flattened, and PCA configurations are compared using the same SimpleMLP architecture under 5-fold GroupKFold cross-validation, while the two-dimensional spatial representation is evaluated separately using a convolutional neural network as a topology-aware modelling configuration. For electrical power prediction, the two-variable baseline already retained most of the predictive information, achieving an RMSE of approximately 2.2 % of rated power, while the distributed representations further reduced the absolute prediction error. For fatigue-load prediction, the effect of distributed inflow information was substantially more load-channel dependent, with only a marginal difference for tower-base fore–aft bending but much larger differences for blade-root edgewise and main-bearing bending. Within the fixed-architecture comparison, the PCA representation achieved the highest accuracy for electrical power prediction while reducing the input dimensionality from 1800 variables to 128 principal components. In contrast, the topology-aware CNN achieved the lowest prediction errors across all 12 fatigue-load components. These results show that the value of distributed rotor-plane information differs between the two prediction tasks and that the most effective representation–architecture configuration among those evaluated depends on the prediction objective.
We present the economic lifetime-aware formulation of wind farm control (WFC), a novel approach characterized by two key innovations with respect to the state of the art. First, the optimization is guided by an economic model of profit. By targeting profit instead of the more widely adopted power yield, the operational optimization considers not only the revenue generated by selling electricity (possibly at a variable price when operating on spot markets) but also the costs due to operation and maintenance (O&M). Second, since fatigue-induced damage is a finite-budget quantity, we explicitly consider that any effect on damage generated by a control action will leave more or less room to other control actions, a coupling that is neglected by current approaches that arbitrarily limit or penalize fatigue-inducing loads. To achieve this goal, the optimization is subjected to explicit constraints on lifetime, which ensure that the turbines in a farm and their components can indeed reach a desired goal duration. As for other optimization-based WFC approaches, the proposed method results in the offline calculation of optimal control set points for each turbine, which are scheduled and dispatched at run time in an open loop as functions of ambient conditions. After introducing the economic lifetime-aware WFC approach, the paper analyzes two case studies. The first represents an idealized scenario characterized by a simplified set of environmental conditions, designed to demonstrate the behavior of the new control strategy and its impact on fatigue loads in conditions of very frequent waking. The second case features a more realistic small wind farm, with wind climate data derived from an existing meteorological mast. The benefits of the proposed methodology are highlighted by comparing it to alternative, previously published control strategies, which either ignore component damage or address fatigue loads in alternative, simplified ways. Results show that the lifetime-aware formulation always ensures the achievement of the desired lifetime targets, whereas methods that do not enforce such a condition may fail to do so. Additionally, the proposed method also results in the best economic performance, i.e., in the highest profit.
As an opportunity to increase turbine and wind park efficiencies, multi- and dual-rotor turbines have received great attention among research and industry. The goal of this experimental study is to validate and add findings to numerical simulations on dual rotors. It will be shown that the rotational sense greatly influences the lateral interaction and therefore the downstream evolution of a dual wake. In the experiments, 19 hot wires on a downstream traversing array are used to acquire the velocities in the wake at high temporal and spatial resolution. Two laterally spaced counter-rotating model wind turbines with a diameter of 0.58 m (=1 D) generate the wake, which is analyzed between 0.75 and 10.0 D downstream. The benefits in dual-rotor wake control will be demonstrated through symmetrical yaw-alternation of the setup in dependency of the positioning of the turbines with different rotational sense. To achieve a beneficial wake evolution for a downwind turbine, two main strategies are presented. One is a wake centroid deflection away from a possible downstream turbine, causing a flow entrainment from above the dual-rotor setup. The other one is an induced wake collision, resulting in strong turbulent interaction between the two single wakes and faster wake recovery. The knowledge of these principles and their application to full scale show great potential for large efficiency gains for wind park operation.
The wake of a laboratory-scale floating offshore wind turbine model is investigated under prescribed sinusoidal surge, sway, roll, pitch, and yaw motions using large-eddy simulations coupled to an actuator-line model. The study aims to assess how the wake of a moving turbine evolves in a high-blockage-ratio wind tunnel and to compare the results with the literature on full-scale models and experiments. The present work covers sinusoidal motions in five of the six floating offshore wind turbine degrees of freedom in a single study and uses radial probes that sample circular two-dimensional cross-sections of the wake at several downstream positions instead of the commonly used linear probes. Two cases per degree of freedom are considered, corresponding to two distinct wake regimes: one with a low frequency and high amplitude and one with a high frequency and low amplitude. The low-frequency/high-amplitude cases exhibit wake behavior close to the fixed-bottom case, as the prescribed frequency falls outside the high-energy spectral range naturally developed by the fixed-bottom wake. Conversely, the high-frequency/low-amplitude cases, whose prescribed frequency is within this high-energy range, produce strongly amplified perturbations, more irregular wake boundaries, earlier tip and root vortex trail expansion and merger, sharper turbulence intensity peaks, and faster wake recovery. The amplification is concentrated at the tip and root vortex trails, where the shear flow instability is strongest. An exception is the high-frequency surge case, which hampers wake recovery at the simulated frequency. Despite the high blockage ratio and wake confinement, all phenomena identified are consistent with the literature, confirming that the fundamental floating-wind-turbine wake dynamics are captured in this setup.
