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The platform motions of floating offshore wind turbines generate unsteady wake dynamics that affect downstream power production and fatigue loading. Modelling floating wind farm performance therefore requires an improved understanding of these wake dynamics. However, the aerodynamic influence of the tower and nacelle is commonly neglected in numerical simulations. This study has two objectives: (i) identify the influence of the tower and nacelle on wake dynamics for different platform motions, and (ii) quantify the ability of lower-fidelity meshless methods as a means to avoid costly dynamic meshing. The results demonstrate that while blade-scale effects associated with the tower and nacelle are not fully resolved by lower-fidelity methods, the dominant platform-induced wake dynamics are captured well. Furthermore, the impact of the tower and nacelle model on wake coherence appears to depend on the platform motion degree of freedom: for the motion frequencies and amplitudes explored, inclusion of the model amplifies wake meandering for rolling, while pitching-induced wake dynamics are largely unaffected.

Nacelle‐mounted, forward‐facing light detection and ranging (LIDAR) technology can deliver benefits to rotor speed regulation and loading reductions of floating offshore wind turbines (FOWTs) when assisting with blade pitch control in above‐rated wind speed conditions. Large‐scale wind turbines may be subject to significant variations in structural loads due to differences in the wind profile across the rotor‐swept area. These loading fluctuations can be mitigated by individual pitch control (IPC). This paper presents a novel LIDAR‐assisted feedforward IPC approach that uses each blade's rotor azimuth position to allocate an individual pitch command from a multi‐beam LIDAR. In this study, the source code of OpenFAST wind turbine modelling software was modified to enable LIDAR simulation and LIDAR‐assisted control. The LIDAR simulation modifications were accepted by the National Renewable Energy Laboratory (NREL) and are now present within OpenFAST releases from v3.5 onwards. Simulations of a 15 MW FOWT were performed across the above‐rated wind spectrum. Under a turbulent wind field with an average wind speed of 17 ms−1, the LIDAR‐assisted feedforward IPC delivered up to 54% reductions in the root mean squared errors and standard deviations of key FOWT parameters. Feedforward IPC delivered enhancements of up to 12% over feedforward collective pitch control, relative to the baseline feedback controller. The reductions to the standard deviation and range of the rotor speed may enable structural optimization of the tower, while the reductions in the variations of the loadings present an opportunity for reduced fatigue damage on turbine components and, consequently, a reduction in maintenance expenditure.

Acoustic impedance models are the most common method for representing the properties of engine nacelle liners. Although various models have been developed, it is difficult to validate the accuracy due to the limitation of impedance eduction techniques. For obtaining accurate impedance results, a series of perforated liners are manufactured and the impedance of all is measured by using both the microphone array method and in-situ method. The results of the test demonstrate that acoustic resistances are not sensitive to percent open areas, hole diameter, and the face sheet’s thickness, but the Mach number of the grazing flow in the duct can have a considerable effect on resistance. Furthermore, excellent agreement between predictions and measured data gives confidence that the Goodrich perforated liner impedance model is suitable for the design of engine nacelle liner.

In the simulation of fixed offshore wind support structures, it is important to accurately model structural damping. Loading scenarios such as earthquakes can excite modes beyond the support structure fundamental frequency, so control of damping on higher frequency modes becomes relevant. In the aeroelastic simulation of such structures, the foundation is often modelled using a superelement approach, where the tower and foundation are separate structural bodies. In industry-standard practice, damping is specified separately on the tower and foundation and tuned to achieve overall support structure damping ratios. Using standard methods, it can be challenging to control the damping on more than one support structure mode, without introducing unwanted artifacts like negative damping on higher modes. This paper introduces, elaborates and compares two improved methods for specifying damping in the aeroelastic simulation, which can achieve target damping ratios on the higher frequency support structure modes of dynamic interest, under the assumption of a rigid rotor-nacelle assembly. The first method specifies the damping directly on the integrated support structure modes, while the second method relies on automated linear tuning of a general superelement damping matrix and tower modes to achieve the desired overall damping. Next, a flexible rotor-nacelle assembly is considered, where it is observed that the resulting system damping ratios are not consistent between the three studied methods. The reasons for these differences are explored and explained, and a method is proposed to align the two improved damping methods by repeating the automated linear tuning with a flexible rotor-nacelle assembly.

In this study, we investigate the dynamics of the stable boundary layer (SBL) in the context of wind energy by combining Large Eddy Simulations (LESs) including wind turbines modeled by the Actuator Disc Method (ADM) with field measurements from the WiValdi wind park in Krummendeich, Germany. We focus on typical SBL conditions including the temperature inversion due to surface cooling, the Low Level Jet (LLJ) and wind veer. From there on, we examine the impact of the resulting anisotropic turbulence on wind turbine wakes. Simulations of wind inflow and turbine wakes are validated using ground LiDAR data, as well as nacelle LiDAR data with different elevation angles. The wind turbine inflow and the veering of the wake can be reproduced accurately by the simulations. A smaller gradient in the cross-sections of the measurements velocity deficit compared to the LES can be explained by the nacelle LiDARs volume averaging effect.

This paper presents an experimental investigation of the nacelle transfer function (NTF) under varying inflow and operating conditions, based on wind tunnel measurements of the flow above a wind turbine nacelle. The results reveal that the flow in the nacelle region is highly complex, with significant spatial variations in the mean nacelle wind speed that depend strongly on the free-stream wind speed and the tip-speed ratio. These variations are linked to distinct flow features developing above the nacelle, including local flow acceleration and the formation of a swirl structure. The study provides a physical interpretation linking flow structures above the nacelle to the shape of the NTF, laying the groundwork for future experimental and numerical validation.

Because of the intermittency problem of wind power, co-located energy storage for wind farms is becoming more common, especially for grids with high shares of wind energy. Currently, most co-located wind farm energy storage is achieved with Lithium-Ion Batteries (LIB). LIB provides high round-trip efficiency but has high costs, which limit the average capacity duration to about 2 hours. The two primary energy storage technologies that offer lower cost for capacity are Pumped Hydro Storage and Compressed Air Energy Storage, but both are challenged by topographical and geographic limitations, which limit site opportunities. Herein, a new concept Turbine-integrated Isothermal Compressed Air Energy Storage (TICAES) is proposed, which allows much more site opportunities by using underground reservoir that are small enough (e.g., 40,000 m3) to allow deployment in bedded salt formations, which are relatively common and often located at or near wind farms leverages. TICAES further leverages existing components in the nacelle (gearbox, generator, etc.) and employs a near-isothermal reciprocating compressor/expander for high efficiency. However, this new concept requires further validation of techno-economic feasibility and consideration of complex nacelle integration issues.

Nacelle-mounted lidar alignment is critical for accurate performance assessment, but difficult to achieve on wind turbines using standard methods. This paper presents a two-step framework combining onshore inter-sensor calibration of the wind lidar with a 2D lidar scanner and offshore near real-time geometric tracking of the rotor plane. Field trials show a reduction in residual misalignment from 3.5° to 0.6° with a quantified uncertainty of ±0.65° at 95% confidence.

The increasing demand for floating offshore wind turbines installations highlights the need to find less costly solutions for their implementation. This paper considers a reduction in the platform’s size, and therefore a reduction in its steel mass and materials used to reduce its costs. The VolturnUS-S platform with the 15-MW reference wind turbine is considered as the baseline. In the static analysis, a sensitivity study evaluated the effects of pontoon width and length, and radial column diameter, on static pitch angle, metacentric height, and maximum stress at the pontoon-central column interface. Two configurations were selected based on the criterion that the static pitch angle should not deviate significantly from the baseline value, leading to empty platform mass reductions of approximately 5% and 9%. Afterwards, dynamic analyses are conducted for a stand-alone floating offshore wind turbine. The generated power, pitch angle and maximum nacelle accelerations are studied for the environmental conditions in Viana do Castelo, in the North of Portugal. The platform configuration with a mass reduction of 9% was selected for further analysis since it provides the lowest mass and hence material usage, as well as lower maximum nacelle accelerations, while not leading to a significant decrease in generated power. Finally, simulations for a simplified wind farm, of two streamwise aligned wind turbines, were carried out for the original and the new platform configuration. The power curves corresponding to both configurations present very similar values across the considered wave characteristics and directions. This leads to a decrease in levelized cost of energy of around 1.5-1.6%.

In floating wind turbines, mooring systems are vital for station-keeping and operational safety. Ensuring the safe and cost-effective maintenance of these systems is of great importance. In this research, we have investigated the performance of nacelle motion accelerometers in predicting mooring line tensions under various operational conditions using deep learning algorithms. An accelerometer with varying degrees of freedom (DOFs) is assumed to be placed inside the nacelle to measure platform vibrations. To predict dynamic mooring tensions, we propose a hybrid model (CNN-BiLSTM) that combines Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks, leveraging the strengths of both algorithms to improve prediction accuracy. Our findings show that including the surge motion accelerometer has the greatest effect in predicting mooring tensions, with an R2 value of 0.9941, compared to an R2 of 0.9907 for the 6-DOF model without accelerometers, representing a 0.34% increase. However, the sway motion shows a 0.15% increase with an R2 value of 0.9956, and the heave motion shows a 0.17% increase with an R2 value of 0.9956, both relative to the surge motion accelerometer. The robustness of the model under varying dynamic conditions suggests its potential utility in other predictive maintenance tasks in the marine energy sector.