Explore the vast range of titles available.

The structural dynamic characteristics of offshore wind turbines are directly related to the operational safety and equipment reliability of these turbines in service. However, due to the complex working conditions, a single load analysis fails to accurately reflect the structural dynamic characteristics during actual operation. In this study, we focus on the 5 MW offshore wind turbines and establish a three-dimensional turbulent flow field model at sea using the Kaimal wind speed spectrum. Additionally, we incorporate the Kärnä ice force spectrum to develop a mathematical model for floating ice. By combining multiple working conditions through permutation and combination techniques, we replicate the actual operating environment of offshore wind turbines. Leveraging OpenFAST’s open computing capabilities and EDEM’s discrete element analysis method, we investigate the dynamic response characteristics of wind turbines under separate and coupled effects of wind load, wave load, and ice load across different offshore working conditions. Our findings indicate that under coupling effects from wind-wave-ice loads, lateral and fore-aft displacement at the tower top as well as lateral and fore-aft bending moment at the tower foundation are greater compared to individual loads. However, cumulative fatigue damage caused by coupling loads on wind turbines is less than that resulting from individual loads.

Ice-breaking methods have become increasingly significant with the ongoing development of the polar regions. Among many ice-breaking methods, ice-breaking that utilizes a moving load is unique compared with the common collision or impact methods. A moving load can generate flexural-gravity waves (FGWs), under the influence of which the ice sheet undergoes deformation and may even experience structural damage. Moving loads can be divided into above-ice loads and underwater loads. For the above-ice loads, we discuss the characteristics of the FGWs generated by a moving load acting on a complete ice sheet, an ice sheet with a crack, and an ice sheet with a lead of open water. For underwater loads, we discuss the influence on the ice-breaking characteristics of FGWs of the mode of motion, the geometrical features, and the trajectory of motion of the load. In addition to discussing the status of current research and the technical challenges of ice-breaking by moving loads, this paper also looks ahead to future research prospects and presents some preliminary ideas for consideration.

Sea ice and the resulting loads are major safety concerns for vessels operating in ice-covered regions. This study presents a tailored sea ice and local ice load monitoring system specifically designed for polar cargo vessels. The system employs shipboard cameras coupled with a DeepLab v3+-based algorithm to achieve real-time ice concentration identification, demonstrating 90.68% accuracy when validated against historical Arctic Sea ice imagery. For structural load monitoring, we developed a hybrid methodology integrating numerical simulations, full-scale strain measurements, and classification society standards, enabling the precise evaluation of ice-induced structural responses. The system’s operational process is demonstrated through comprehensive case studies of characteristic ice collision scenarios. Furthermore, this system serves as an exemplary implementation of a navigation assistance framework for polar cargo vessels, offering both real-time operational guidance and long-term reference data for enhancing ice navigation safety.

Load identification algorithms are crucial for the real-time assessment of structural safety, integrity, and durability in ice-going vessel monitoring systems. However, factors such as the ill-conditioning of the response matrix and noise in the measured ice-induced strains can severely degrade the inversion accuracy, thereby compromising the reliable judgment of structural safety. This paper investigates the ill-posed nature of the ice load identification system. A dynamic ice load identification model is first established based on the time-domain deconvolution method which can incorporate the dynamic effects of ice loads, mitigates solution anomalies caused by loading at unexpected locations, and offers broader applicability to various engineering scenarios. Using singular value decomposition (SVD) and Picard theory, a comprehensive analysis is conducted to examine the influencing factors and ill-posed characteristics of the identification system. Subsequently, a low-pass signal filter and a regularization operator are introduced to mitigate the ill-posedness. Finally, three typical ice load identification scenarios are simulated and an experimental validation is executed, and the effectiveness of the proposed mitigation strategies is evaluated. This study may provide supports for the reliability assessment of field measurements.

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.

The mechanical behaviour of ice is a complex phenomenon that is influenced by various factors, such as temperature, loading conditions, and structural geometry. To accurately predict the response of ice structures and estimate ice loads, appropriate models are required. In this article, we have reviewed several widely known material models for ice, including elastic, viscoelastic, plastic, damage, and fracture models. Elastic models are simple and easy to use, but they do not account for the time-dependent behaviour of ice. Viscoelastic models, on the other hand, can predict the evolution of damage and failure in ice structures but can be computationally ex-pensive. Plastic models can simulate the ductile behaviour of ice under high stress but do not account for damage and fracture. Damage models can simulate the evolution of damage and failure in ice structures but can also be computationally expensive. Fracture models can simulate the brittle behaviour of ice and predict crack propagation but require accurate input data. In practice, a combination of models is often used to account for different aspects of ice behaviour. With the advances in computer technology and simulation techniques, it is be-coming increasingly possible to simulate more complex ice structures and loading conditions. This could lead to the development of more accurate and efficient ice models that can be used for a wider range of applications, such as predicting the behaviour of ice structures in response to climate change. The effects of climate change on the behaviour of ice and the resulting impact on infrastructure are a growing concern. Therefore, the development of more accurate and efficient ice models is critical for the sustainable development of these regions.

In this work, the finite element method (PD-FEM) coupling strategy is used to simulate ship-ice interaction. Two numerical benchmark tests are selected to validate the coupling approach and its program. During the ice-breaking process simulation, the generation and propagation of radial and circular cracks in level ice are modeled and phenomena such as the shedding of wedge ice, flipping of brash ice, and cleaning of the channel are observed to be broadly consistent with experimental observation. The influence of ship speed and ice thickness on the ice load are investigated and analyzed. The ice load obtained from the numerical simulations is in general agreement with that given by Lindqvist’s empirical formula. The boundary effect on the crack path can also be avoid with the current coupling method.

Ice loads are critical forces that impact the structural integrity of offshore equipment in high-latitude sea areas and play a pivotal role in the design of structures in ice-prone regions. The primary objective of this study is to investigate both experimental and numerical approaches to analyze ice loads on marine structures, elucidate their characteristics and patterns, and offer technical support for the design of structures in ice-prone areas. To achieve this goal, an ice model was built using polypropylene material, and experiments were conducted in a wave flume at room temperature to measure the ice resistance on cylindrical structures. Structural loads were assessed at various ice velocities while maintaining a fixed ice concentration. Furthermore, a high-performance discrete element technology was employed to develop a numerical simulation method for calculating ice resistance on cylindrical structures. Sensitivity analysis was conducted to evaluate the influence of discrete element density on the resistance outcomes. The predicted structural resistance for ice velocities corresponding to the experimental conditions was compared with the results obtained from the model experiment. The research findings indicate that the primary cause of ice resistance is the interaction between the structure and fragmented ice, which leads to collisions, friction, rotation, and local ice accumulation. To quantify the resistance, ice resistance coefficients were defined using an average resistance formula, representing different statistical values. These coefficients were found to remain relatively constant at varying sailing speeds. The results obtained through the discrete element method for ice resistance demonstrated a remarkable agreement with the experimental findings, both in terms of observed phenomena and numerical values. This agreement serves as evidence substantiating the effectiveness of the numerical approach. These methods offer efficient and accurate load prediction solutions for the design of structures in cold regions.

In this study, a numerical method was developed based on peridynamics to determine the ice loads for a ship navigating in level ice. Convergence analysis of threedimensional ice specimen with tensile and compression loading are carried out first. The effects of ice thickness, sailing speed, and ice properties on the mean ice loads were also investigated. It is observed that the ice fragments resulting from the icebreaking process will interact with one another as well as with the water and ship hull. The ice fragments may rotate, collide, or slide along the ship hull, and these ice fragments will eventually drift away from the ship. The key characteristics of the icebreaking process can be obtained using the peridynamic model such as the dynamic generation of cracks in the ice sheet, propagation and accumulation of ice fragments, as well as collision, rotation, and sliding of the ice fragments along the ship hull. The simulation results obtained for the ice loads and icebreaking process were validated against those determined from the Lindqvist empirical formula and there is good agreement between the results.

The structural safety of polar ships is critically dependent on local ice loads acting in the ship–ice interaction area. Ice conditions and ship speeds play dominant roles in influencing local ice loads. Field measurement serves as a crucial approach for accurately assessing and scientifically understanding local ice loads and ice conditions. The instrumentation for the field measurement on RV Xuelong-2 is discussed in this study. In the 12th Chinese National Arctic Research Expedition, digital processing technologies are employed for image recognition and statistical analysis of ice concentrations and thicknesses. The influence coefficient matrix method is validated by a physical experiment and applied to identify local ice loads from ice-induced strains. Subsequently, the relationship between local ice loads, ice conditions, and ship speeds is statistically analyzed and mechanistically explained. The results show that the coupling effect between ship speeds and ice parameters, along with the competition between ice failure modes, may cause ice load peaks to transition from increasing to decreasing at a specific ship speed and ice thickness. A prolonged ice load duration under high ice concentrations is an important factor contributing to the positive correlation between ice load peaks and ice concentrations.