As the size of offshore wind turbines increases, a realistic representation of the spatiotemporal distribution of the incident wind field becomes crucial for modeling the dynamic response of the turbine. The International Electrotechnical Commission (IEC) standard for wind turbine design recommends two turbulence models for simulations of the incident wind field, the Mann spectral tensor model, and the Kaimal spectral and exponential coherence model. In particular, for floating wind turbines, these standard models are challenged by more sophisticated ones. The characteristics of the wind field depend on the stability conditions of the atmosphere, which neither of the standard turbulence models account for. The spatial and temporal distribution of the turbulence, represented by coherence, is not modeled consistently by the two standard models. In this study, the Mann spectral tensor model and the Kaimal spectral and exponential coherence model are compared with wind fields constructed from offshore measurements and obtained from large‐eddy simulations. Cross sections and durations relevant for offshore wind turbine design are considered. Coherent structures from the different simulators are studied across various stability conditions and wind speeds through coherence and proper orthogonal decomposition mode plots. As expected, the standard models represent neutral stratification better than they do stable and unstable. Depending upon the method used for generating the wind field, significant differences in the spatial and temporal distribution of coherence are found. Consequently, the computed structural design loads on a wind turbine are expected to vary significantly depending upon the employed turbulence model. The knowledge gained in this study will be used in future studies to quantify the effect of various turbulence models on the dynamic response of large offshore wind turbines.

We describe the process of using large-eddy simulations of wind turbine wake flow to help design a wake measurement campaign. The main goal of the experiment is to measure wakes and wake deflection that result from intentional yaw misalignment under a variety of atmospheric conditions at the Scaled Wind Farm Technology facility operated by Sandia National Laboratories in Lubbock, Texas. Prior simulation studies have shown that wake deflection may be used for wind-plant control that maximizes plant power output. In this study, simulations are performed to characterize wake deflection and general behavior before the experiment is performed to ensure better upfront planning. Beyond characterizing the expected wake behavior, we also use the large-eddy simulation to test a virtual version of the lidar we plan to use to measure the wake and better understand our lidar scan strategy options. This work is an excellent example of a "simulation-in-the-loop" measurement campaign.

The influence of 3 MW Hywind-II wind turbine wakes from an upstream offshore floating wind turbine on a downstream turbine with a separation distance of seven rotor diameters was studied for a site in the Gulf of Maine. The turbines and the platforms were subjected to atmospheric boundary layer flows. Various sensitivity studies on fatigue loads with respect to the positions of the downstream turbine were performed and validated with a large-eddy simulation tool. In particular, the effect of various lateral positions of the downstream turbine relative to the upstream turbine were considered using time-series turbine wake data generated from the large-eddy simulation tool which served as an input to an aero-elastic wind turbine model to assess the loads. The load response from the rotor, tower, and the floating platform for the downstream turbine were sensitive to the lateral offset positions where turbines that were partially exposed to upstream turbine wakes yielded significant increases in the cyclic load range. For the given set of lateral positions for the downstream turbine, the largest damage equivalent load occurred when the turbine was one rotor diameter to the left of the centerline, when looking upstream, which is the position of the turbine fully exposed to upstream turbine wake. On the other hand, the fatigue load on the downstream turbine placed on the right side of the position fully exposed to the upstream turbine wake, yielded lower stress due to the non-symmetric shape of the turbine wake. The configuration associated with the largest damage equivalent loads was further investigated in a large-eddy simulation, modeling both the upstream and downstream turbines. It was found that the energy spectra at the blade rotational frequency were a magnitude order higher for the downstream turbine, especially for surge, heave, pitch, and yaw motion of the platform. The increase of the damage equivalent load for the flapwise blade root moment was 45% compared to the upstream turbine, which can potentially reduce the turbine service life time.

To meet the Biden-Harris administration's goal of deploying 30 GW of offshore wind power by 2030 and 110 GW by 2050, expansion of wind energy into U.S. territorial waters prone to tropical cyclones (TCs) and extratropical cyclones (ETCs) is essential. This requires a deeper understanding of cyclone-related risks and the development of robust, resilient offshore wind energy systems. This paper provides a comprehensive review of state-of-the-science measurement and modeling capabilities for studying TCs and ETCs, and their impacts across various spatial and temporal scales. We explore measurement capabilities for environments influenced by TCs and ETCs, including near-surface and vertical profiles of critical variables that characterize these cyclones. The capabilities and limitations of Earth system and mesoscale models are assessed for their effectiveness in capturing atmosphere–ocean–wave interactions that influence TC/ETC-induced risks under a changing climate. Additionally, we discuss microscale modeling capabilities designed to bridge scale gaps from the weather scale (a few kilometers) to the turbine scale (dozens to a few meters). We also review machine learning (ML)-based, data-driven models for simulating TC/ETC events at both weather and wind turbine scales. Special attention is given to extreme metocean conditions like extreme wind gusts, rapid wind direction changes, and high waves, which pose threats to offshore wind energy infrastructure. Finally, the paper outlines the research challenges and future directions needed to enhance the resilience and design of next-generation offshore wind turbines against extreme weather conditions.

Scanning LiDARs can be used to obtain three-dimensional wind measurements in and beyond the atmospheric surface layer. In this work, metrics characterizing wind turbine wakes are derived from LiDAR observations and from large-eddy simulation (LES) data, which are used to recreate the LiDAR scanning geometry. The metrics are calculated for two-dimensional planes in the vertical and cross-stream directions at discrete distances downstream of a turbine under single-wake conditions. The simulation data are used to estimate the uncertainty when mean wake characteristics are quantified from scanning LiDAR measurements, which are temporally disjunct due to the time that the instrument takes to probe a large volume of air. Based on LES output, we determine that wind speeds sampled with the synthetic LiDAR are within 10% of the actual mean values and that the disjunct nature of the scan does not compromise the spatial variation of wind speeds within the planes. We propose scanning geometry density and coverage indices, which quantify the spatial distribution of the sampled points in the area of interest and are valuable to design LiDAR measurement campaigns for wake characterization. We find that scanning geometry coverage is important for estimates of the wake center, orientation and length scales, while density is more important when seeking to characterize the velocity deficit distribution.

ITM Web of Conferences, open-access proceedings