As wind turbines increase in size, blades become longer, thinner, and more flexible, making them more susceptible to large geometric nonlinear deformations, which pose challenges for aeroelastic simulations. This study presents a nonlinear aeroelastic model that accounts for large deformations of slender, flexible blades, coupled through the Actuator Line Method (ALM) and Geometrically Exact Beam Theory (GEBT). The accuracy of the model is validated by comparing it with established numerical methods, demonstrating its ability to capture the bending–torsional coupled nonlinear characteristics of highly flexible blades. A bidirectional fluid–structure coupling simulation of the IEA 15MW wind turbine under uniform flow conditions is conducted. The effect of blade nonlinear deformation on aeroelastic performance is compared with a linear model based on Euler–Bernoulli beam theory. The study finds that nonlinear deformations reduce predicted angle of attack, decrease aerodynamic load distribution, and lead to a noticeable decline in both wind turbine performance and blade deflection. The effects on thrust and edgewise deformation are particularly significant. Additionally, nonlinear deformations weaken the tip vortex strength, slow the momentum exchange in the wake region, reduce turbulence intensity, and delay wake recovery. This study highlights the importance of considering blade nonlinear deformations in large-scale wind turbines.

Routing based on a connected dominating set (CDS) is a promising approach in wireless ad hoc networks. Wu and Li proposed a distributed approximation algorithm for calculating CDS in a given connected graph. However, this algorithm is difficult when there are obstacles in the network topology. Obstacle hybrid routing algorithm (OHRA), presented here, addresses these issues. OHRA consists of mobility model, CDS election and hybrid routing. In obstacle mobility model, we introduce STANDBY nodes as relaying nodes between two nodes that are invisible. This paper then proposes a distributed CDS election algorithm in the presence of obstacles. This algorithm extends Wu and Li's algorithm and utilizes STANDBY nodes to connect the existing dominating-nodes belonging to dominating set. In addition, OHRA based on a CDS uses hybrid routing scheme (flooding-based approach and position-based approach) to forward around any obstacles. Eventually, an example is given to show that the proposed approach can form a CDS and successfully construct routes.

This research tackles the challenge of ice-induced vibrations in fixed-bottom offshore wind turbines, particularly the risk of frequency lock-in (FLI), a critical loading condition arising from slow ice movement against structures. We introduce an innovative FLI analysis process grounded in the ductile damage–collapse (DDC) mechanism, which offers a more accurate and significantly lower probability of FLI occurrence than conventional methods. Through dynamic evaluations employing a time-domain ice load model and FLI displacement analyses, we demonstrate that FLI can lead to higher structural vibrations than those caused by continuous brittle ice crushing. A case study of a 5 MW monopile wind turbine tower utilising Abaqus confirms the necessity for incorporating FLI considerations into structural design to ensure safety and performance in ice-prone environments. Comparing the DDC mechanism with the ISO method, our study reveals that the DDC approach predicts higher displacement and acceleration values during FLI, nearly an order of magnitude greater than those induced by ice loads with a 50-year return period. The research underscores the importance of robust ice-load integration in design strategies for offshore wind turbines, especially in regions susceptible to ice. It highlights the DDC mechanism as a novel strategy for enhancing structural resilience against ice-induced hazards.

The randomness and complexity of ice loads present major challenges to the safety and stability of offshore platforms. Traditional methods for identifying ice loads often lack accuracy and adaptability under changing environmental conditions. This study proposes a novel inversion method based on Temporal Convolutional Networks (TCNs), integrating finite element simulation with deep learning to effectively identify random ice loads. A random ice load model is first developed, and its dynamic characteristics are validated through finite element analysis. The TCN model is then applied to capture the time-dependent features of ice loads. To improve the model’s generalization ability, its hyperparameters are optimized using particle swarm optimization (PSO). The results show that the TCN model achieves goodness-of-fit (R²) values of 0.821 and 0.808 on the training and test sets, respectively, indicating strong predictive performance. Under different ice thickness and velocity conditions, the model achieves R² values close to 0.99, demonstrating high robustness. This work represents the first application of TCN to ice load identification. By combining it with simulation data, we offer a high-precision, data-driven approach for dynamic load identification, enhancing the efficiency and reliability of safety assessments for conical offshore platforms.

This paper addresses a detailed analysis of the ice–structure interaction process of the phase-locked ice crushing (PLC) against offshore structures. Directly measured ice load, structure response data, and in situ observation from the field measurements on the Molikpaq lighthouse and jacket platform were used in the study. This paper summarizes a new ductile damage-collapse (DDC) failure mechanism for the PLC process. The DDC mechanism shows that the ice failure is a discrete ductile crushing process rather than a ductile–brittle transition process. The analysis identifies that the ice has a failure length in PLC and this failure length plays an important role in understanding the interaction. It reveals that PLC can occur on most vertical-sided offshore structures when the velocity of the ice sheet falls within the range of the failure length divided by the natural period of the structure. This paper proposes that this relationship between ice failure length and the natural period of the structure can be used as one of the PLC occurrence conditions. The DDC failure mechanism provides a basis for another technical route to solve the PLC problem.