To address the severe scouring challenges faced by offshore wind power infrastructure,

the scouring problem of the four-pile jacket foundation of offshore wind power under the action of ocean currents through numerical simulation. Using FLOW-3D software is studied, adopting the large eddy simulation (LES) turbulence model and the sediment transport model, the validity of the numerical model was verified through comparisons with experimental results, the scouring process of the four-pile jacket foundation under the action of a single steady flow is simulated. The development and changes in the scour pit morphology around the foundation over time are analyzed, and the effects of different flow velocities, incoming flow angles, and pile spacings on the scouring of the four-pile foundation are studied.

The results show that the group pile effect is central to the scouring characteristics of four-pile jacket foundations. The incident flow angle alters the shielding interactions among piles, leading to an asymmetric distribution of scour morphology. Pile spacing modulates the intensity of interference between adjacent piles; as the spacing increases, the group pile effect gradually weakens, and the scour pattern transitions from a unified, interconnected scour hole to relatively independent local scour holes. The maximum scour depth is primarily governed by flow velocity and exhibits only minor variation with changes in pile spacing.

The research findings provide a reference for the scouring of four-pile jacket foundations in offshore wind farms.

To effectively reduce fatigue damage, a reasonable dynamic cable design is required.

An optimization model based on an improved hybrid particle swarm optimization algorithm is established. It employ MATLAB to develop a genetic-chaotic particle swarm dynamic factor optimization algorithm and utilize the Orcaflex software for the overall design and optimization of platform dynamic cables. The optimization problem of deepwater dynamic cables is treated as the objective function, with parameters such as cable length, buoyancy block and counterweight block positions, and spacing as optimization variables. Building upon the foundation of the standard particle swarm algorithm and integrating genetic algorithms, it effectively prevent dynamic cable optimization parameters from falling into local optima. Chaotic initialization of initial particles is applied to ensure a uniform distribution in high-dimensional solution spaces. Dynamic inertia weight factors and learning factors are introduced to balance global and local search capabilities during optimization. Adhering to the Pareto principle, It formulate an objective function to facilitate multi-objective constrained optimization. The improved optimization algorithm shows better performance in terms of convergence, accuracy, and convergence speed.

It quickly and effectively balances the relationship between the maximum axial tension and the minimum bending radius of the cable and pipe, achieves the optimal design.

It provides strong support and guidance for practical engineering applications.

Offshore wind speed observations often suffer from data gaps, limiting the accuracy of wind resource assessment and wind farm operation.

A ratio-based interpolation method for reconstructing missing wind speed data using ERA5 reanalysis and floating LiDAR observations is proposed. Taking 100 m wind speed data from a coastal buoy as a case study, the method is evaluated across annual scale, seasonal variability, wind speed levels, and typical extreme weather events.

Results show that the method effectively captures temporal wind speed trends, with an annual average correlation coefficient of 0.839. However, it tends to underestimate wind speed magnitudes, with errors increasing notably under high wind conditions, especially during convective summer periods and typhoon events. Compared to traditional linear regression methods, the ratio method performs better in maintaining trends and controlling errors, and it demonstrates greater stability and robustness under conditions of severe wind speed fluctuations or extreme weather.

Overall, the ratio method demonstrates good applicability in stable wind environments and is suitable for long-term wind resource evaluation and data reconstruction. Nevertheless, its accuracy under extreme weather remains limited, suggesting the need for integration with high-resolution simulations or multi-source data fusion approaches.