The water level forecasting system represented by the hydrodynamic model relies too much on the input data and the forecast value of the boundary, therefore introducing uncertainty in the prediction results. Tide tables ignore the effect of the residual water level, which is usually significant. Therefore, to solve this problem, a water level forecasting method for the regional short-term (3 h) is proposed in this study. First, a simplified MIKE21 flow model (FM) was established to construct the regional major astronomical tides after subdividing the model residuals into stationary constituents (surplus astronomical tides, simulation deviation) and nonstationary constituents (residual water level). Harmonic analysis (HA) and long short-term memory (LSTM) were adopted to forecast these model residuals, respectively. Finally, according to different spatial background information, the prediction for each composition was corrected by the inverse distance weighting (IDW) algorithm and its improved IDW interpolation algorithm based on signal energy and the spatial distance (IDWSE) from adjacent observation stations to nonmeasured locations. The developed method was applied to Narragansett Bay in Rhode Island. Compared with the assimilation model, the root-mean-square error (RMSE) of the proposed method decreased from 12.3 to 5.0 cm, and R² increased from 0.932 to 0.988. The possibility of adding meteorological features into the LSTM network was further explored as an extension of the prediction of the residual water level. The results show that the accuracy was limited to a moderate level, which is related to the difficulty presented by using only wind features to completely characterize the regional dynamic energy equilibrium process.

In recent years, ultra-large-scale offshore wind turbines have attracted widespread attention. However, accurately evaluating the motion responses of offshore wind turbines under extreme conditions, especially for semisubmersible floating off-shore wind turbines, is often challenging. In order to assess the operational behavior of wind turbines under wind and wave loads, this paper adopted a numerical analysis method to solve the motion responses under extreme conditions. It specifically examines the motion responses of the IEA 15 MW wind turbine in terms of surge, heave, and pitch direction, focusing on environmental loads that occur once every 50 years. The results show that the wind turbine can still operate normally under the Ultimate condition. However, the average amplitude increased by 7% in the pitch direction and decreased by 4% in the heave direction compared to the rated condition. Under extreme conditions (occurring once every 50 years), with the wind turbine parked, the average amplitude in the surge direction reduced by 33%, while the average amplitude in the pitch direction reduced by 106%. Thus, it is essential to pitch the blades and brake the generator in extreme environmental conditions to ensure the safety of the wind turbine.

In order to solve the problem of fixed ambiguity and decreased accuracy in GNSS displacement monitoring of the offshore floating platforms, an attitude correction algorithm based on the fusion of a multi-antenna GNSS and an accelerometer was proposed using the Kalman filtering method. The algorithm was validated on a physical simulation platform and a real offshore floating platform. The results indicate that this fusion method effectively compensates for the loss of high-frequency displacement information caused by low GNSS sampling rates, improves situations in which the fusion effect deteriorates due to attitude changes, and enhances the accuracy of GNSS and accelerometer fusion monitoring through offshore buoy testing.

This study presents a comprehensive investigation into multi-directional fatigue damage characteristics of fixed offshore wind turbine tower roots through comparative analysis using FAST (3.5.0) and Bladed (4.3) software platforms. The research methodology encompasses three principal phases: First, a stochastic wind field model was developed through statistical analysis of historical wind speed measurements, achieving superior correlation (R² = 0.983) in goodness-of-fit tests. Subsequently, the rain flow counting technique was employed to characterize equivalent cyclic load spectra. Building upon these foundations, an integrated predictive fatigue life evaluation framework was formulated by synergistically combining S–N curve principles with Palmgren–Miner’s linear cumulative damage theory. The methodology was further validated through cross-platform verification with Bladed software, revealing only a 7.4% deviation in predicted fatigue lives between the two computational models, confirming the technical feasibility of the proposed simplified model.