The rapid development of offshore wind energy has led to increasingly large wind farms, where complex interactions between turbines and the atmosphere play a critical role in determining both power performance and structural loading. Understanding these interactions is vital for optimizing wind farm design, improving operational strategies, and ensuring long-term reliability. In large offshore wind parks, wake interactions between turbines can substantially alter the local flow field and increase non-uniform loading across the farm, contributing to higher fatigue and maintenance costs.
This study investigates the atmospheric flow field and load distribution within the Sørlige Nordsjø II (SNII) offshore wind park, focusing on the first development phase (1.5 GW) with a regular turbine layout. A representative case corresponding to stable atmospheric stratification is selected from the NORA3 reanalysis dataset, and high-resolution large-eddy simulations (LES) are performed using the Parallelized Large-Eddy Simulation Model (PALM). The simulations resolve detailed flow structures within and around the wind farm, providing insights into wake formation, interaction, and recovery, and how these processes affect the load map—the spatial distribution of aerodynamic loads across the turbines.
While the primary focus is on flow–structure interaction and load characterization, the study may also consider the potential influence of yaw steering as a wake control strategy, on how yaw-induced wake deflection could modify local loading patterns and influence the atmospheric energy budget under stable stratification.
The main objectives of this work are:
To characterize the atmospheric flow field and wake-induced load variations in the Sørlige Nordsjø II wind park (Phase 1, 1.5 GW) under stable conditions using high-resolution PALM simulations.
To evaluate how wake interactions shape the load map across the farm and the potential effects of yaw steering on wake and load behaviour.

Large Eddy Simulation (LES) is a crucial methodology for analyzing the complex interactions between wind turbines and the atmospheric boundary layer (ABL). However, the high spatial and temporal resolution required makes LES computationally expensive. This computational burden is significantly amplified in simulations requiring a specific background flow, such as those for turbine wake studies. Such simulations often necessitate prolonged spin-up periods, often on the order of a day, to achieve a quasi-equilibrium state due to initial imbalances and inertial oscillations. Furthermore, the inherent Ekman spiral effect means the resulting wind at hub height deviates in both speed and direction from the geostrophic wind driving the simulation. Consequently, achieving desired wind conditions requires an inefficient, trial-and-error tuning process.

We introduce a novel technique for precise and direct control of the hub-height wind vector within ABL LES, addressing these critical inefficiencies. Our method dynamically adjusts the geostrophic wind components using an exact mathematical relationship derived from the momentum equations and the instantaneous turbulent stress divergence. This approach forces the horizontally-averaged wind at a specified height to rapidly converge with and accurately maintain target values. By implementing this dynamic control, the method effectively eliminates both the extended spin-up periods and the need for iterative tuning of the geostrophic winds.

The efficacy of this method is first demonstrated in an idealized one-dimensional column model, which shows swift convergence to the target wind state. Following this, the technique is successfully implemented and validated within the Weather Research and Forecasting (WRF) model, configured for a state-of-the-art three-dimensional LES environment. These comprehensive tests confirm the method's applicability and substantial benefits for complex simulations. The proposed technique offers a significant enhancement in the efficiency and accuracy of setting up ABL LES, thereby streamlining the entire workflow for wind energy applications.

The UK aims to reach 5 GW of floating offshore wind turbine (FOWT) capacity by 2030 but currently operates only 0.07 GW. High costs and slow deployment rates hinder this goal, largely due to a lack of standardisation across the more than 100 existing FOWT designs. Standardised, mass-produced designs could significantly reduce costs and accelerate deployment, yet current offshore standards lack a comprehensive framework for classifying metocean (meteorological and ocean) conditions crucial to FOWT design.

Existing guidelines, such as DNV-ST-0119, encourage “environmental classes” to simplify design for specific condition sets. Although the IEC wind turbine class system standardises wind conditions for rotor-nacelle assemblies, no equivalent exists for the broader metocean parameters—wind, wave, current, and water level—that define FOWT performance environments. Challenges remain in determining which environmental drivers most affect design, how to select the minimal yet representative set of parameters, and how to define robust, interpretable severity thresholds based on physical principles.

This research directly addresses these gaps. Through a data-driven approach, it aims to develop a comprehensive, transparent framework for classifying metocean conditions. The methodology involves:
1) Identifying design-driving design load cases (DLCs).
2) Determining the critical metocean parameters.
3) Accounting for temporal variability.
4) Building a dataset of key parameters.
5) Applying clustering analysis to derive environmental classes.
6) Evaluating the effects of classification on design key performance indicators through a case study.

The research draws on established standards such as IEC 61400-3-2 (2025), which defines 36 DLCs for power production, parked, and fault scenarios. However, for conceptual FOWT design, only the most influential DLCs are selected based on literature review and industry input. Focusing mainly on wind and wave parameters measurable during early design stages, the study emphasises statistical processing to represent extreme conditions. It applies both univariate and multivariate extreme value models to capture relationships between variables and tail behaviours that drive structural loads.

The data foundation is the ERA5 reanalysis dataset (1980–2024), sampled hourly across 0.5° spatial grids in Northwest Europe. From this dataset, return-period statistics for relevant parameters (e.g., significant wave height, 10 m mean wind speed) are derived and used for unsupervised clustering. The study explores multiple clustering algorithms—k-means, hierarchical clustering, and DBSCAN—to define regions (“zones”) with similar environmental characteristics. DBSCAN is particularly suitable given its ability to recognise irregular geospatial patterns without requiring a pre-defined number of clusters.

The framework aims to determine an optimal balance between zone granularity and design generality. A small number of broad zones favours standardisation but may over-engineer designs, while finer zoning yields optimal site-specific solutions at the cost of efficiency. The case study illustrates how the number of zones affects FOWT design performance and economics.

Key contributions include:
- Identifying the concept-level design-driving DLCs and associated metocean parameters.
- Developing a data-driven, physics-grounded classification method for defining environmental classes.

This work provides a foundational step toward a standardised, evidence-based metocean classification framework for FOWTs, enabling design efficiency, reduced costs, and accelerated deployment.

Accurate forecasts of metocean conditions are critical for the safety, efficiency, and sustainability of offshore wind energy operations, particularly in harsh Arctic environments. This study introduces a prototype forecasting system that dynamically downscales DestinE/ECMWF’s Weather-Induced Extremes Digital Twin (Extremes DT) to deliver short-term, high-resolution (~1 km) forecasts of ocean waves and vessel icing in the Barents Sea. Designed to support offshore wind energy operations, as well as shipping and emergency response, the system provides actionable insights to mitigate risks and enhance operational planning.

The workflow leverages the open-source DNORA framework to automate the operational setup, integrating cutting-edge modeling tools. The spectral wave model WAVEWATCH III is configured with advanced source-term physics and wave-ice interaction processes, while the Marine Icing model for the Norwegian Coast Guard (MINCOG) simulates vessel icing under extreme Arctic conditions. Forecast performance is validated against satellite observations, with a particular focus on extreme weather phenomena such as cold air outbreaks and polar lows. This high-resolution forecasting capability offers significant potential to improve decision-making and operational resilience for offshore wind energy projects in Arctic waters.