Yan Qu

Numerical simulations are conducted to investigate the near-bed flow behaviors of submarine canyons with periodic ridge-valley topography on continental slopes. In the numerical simulation, two topographic parameters are defined: α represents the basal slope angle of the continental slope, and β denotes the side slope angle of the submarine canyon. The topographic modulation of these two parameters on near-bed flow velocity and pipeline on-bottom stability is explored. The results show that the ribbed slope exerts two distinct modulation mechanisms on near-bed flow fields, which are governed by the relative angle between incoming flow and ridge-valley structures. When the flow is perpendicular to the canyon axis (θ = 0°), local topographic contraction and expansion accelerate flow over ridge crests and reduce near-bed velocity in valleys as β increases. In contrast, when the flow is parallel to the canyon (θ = 90°), lateral confinement by bilateral ridges forms a topographic channel, leading to persistent flow acceleration throughout the entire valley area. Moreover, such directional velocity differences substantially alter the on-bottom stability of pipelines deployed within the canyon. Two pipe-soil interaction models are employed in the stability analysis: the Verley-Lund model for a flat clayey seabed and the Slope-Silt model for a sloping silty seabed. The sensitivity of their predictions to soil undrained shear strength is further evaluated.

This paper addresses the computational scalability challenges in automating inspection tasks across large-scale spatial domains, where a fundamental trade-off exists between modeling fidelity for obstacle avoidance and planning tractability. While motivated by the autonomous inspection of offshore wind farms using Unmanned Surface Vessels (USVs), the proposed hierarchical framework offers a generalizable solution for navigating vast environments with sparse obstacles. Existing planners often suffer from prohibitive computational costs when processing environments with millions of grid cells. To overcome this, we present a novel multi-stage framework integrating hierarchical environment modeling with optimized search techniques. The core contributions are threefold: (1) A layered refinement grid strategy that unlike generic hierarchical structures, optimizes memory efficiency for extreme scale disparities to reduce the total grid count by 99.9%; (2) A hexagonal grid compression method that further decreases search nodes by over 95%, thereby mitigating directional bias and preserving topological clustering fidelity; and (3) A bidirectional shortcut-embedded A algorithm that executes efficient local detour planning within constrained corridors. Experimental validation in a large-scale real-world scenario (54 turbines) demonstrates the framework’s efficacy. The proposed method achieves hierarchically-refined path inspection paths in 1.21 seconds, significantly outperforming a baseline approach (5.97 seconds). This work provides a scalable, computationally efficient solution applicable to broad classes of automated inspection and logistics systems.

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.

Objective: This study aims to improve the safety of subsea oil and gas production facilities by designing an integrated fiberglass protective structure. Glass fiber-reinforced materials, noted for their lightweight, high strength, and corrosion resistance, are evaluated for subsea protection applications. The focus is on assessing the structural stability and impact resistance under falling object impacts and fishing net drags. Methods: A fiberglass protective structure was designed based on the layout and protection needs of subsea oil and gas facilities. Finite element numerical calculations (ABAQUS) analyzed the structure's performance under impact scenarios. Key parameters like deformation and stress responses were evaluated to ensure the design meets safety criteria. Results: Simulations showed the fiberglass structure performed well under tested conditions. A falling object impact of 0.24s resulted in a maximum deformation of 1,187.6mm and a stress of 172.3MPa. At 0.31s, the highest stress was 228.2MPa, with a deformation of 1,031.9mm. Under a 50kN drag load, directional load impacts on structural integrity were negligible, indicating robust performance. Conclusion: The fiberglass protective structure offers excellent stability and impact resistance, effectively protecting subsea facilities. Numerical simulations confirm that the structure meets design requirements, with controlled deformation and stress levels under impact and drag loads, highlighting the potential of fiberglass for offshore applications.

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.

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.

中国共产党一直以来高度重视边疆民族地区的发展问题，边疆少数民族大学生是边疆民族地区建设和发展的重要力量，对其进行思想政治教育，有利于民族团结、国家稳定。本文首先分析了加强边疆少数民族大学生思想政治教育的重要意义，然后指出了边疆少数民族大学生思想政治教育存在的问题进，最后提出边疆少数民族大学生思想政治教育的路径：加强自信心；加强思想政治教育工作队伍建设；有针对性地进行思想政治教育；丰富思想政治教育方式方法。The CPC has always attached great importance to the development of border ethnic areas. Border ethnic minority college students are an important force in the construction and development of border ethnic areas. Ideological and political education for them is conducive to national unity and national stability. This article first analyzes the important significance of strengthening the ideological and political education of ethnic minority college students in the border areas, then points out the problems in the ideological and political education of ethnic minority college students in the border areas, and finally proposes the path of ideological and political education for ethnic minority college students in the border areas: strengthening self-confidence; Strengthen the construction of the ideological and political education work team; Targeted ideological and political education; Enrich the methods and methods of ideological and political education.

Ice induced vibration is a serious problem for offshore structures in the ice-covered sea area. Especially when ice moves at low speeds against structures, the coupling of the ice crushing failure process and structural vibration will induce lock-in between ice loads and structural vibration. The strong vibration of the structure has been found in the lock-in process and the loading scenario has been named as frequency lock-in (FLI) vibration, which is a dangerous loading condition that needs to be considered in the design. However, no practical analysis method for FLI vibration is available in existing industry standards due to the lack of understanding of the underlying mechanism. To solve this problem, an FLI analysis method is proposed in this paper based on the ductile damage-collapse mechanism for offshore wind turbine structures. The sample study of a 5MW fixed monopile wind turbine shows that the probability of FLI occurrence on an offshore wind turbine is less than that on an oil platform and lighthouses because the wind turbine has a high rise and low-frequency characteristics. The analysis also shows that the magnitude of the FLI vibration is larger than the random vibration caused by continuous brittle ice crushing.