Bucket foundations have been widely used in marine engineering, such as offshore wind power, due to their anti-overturning performance and convenient installation. In China’s coastal areas, clay soil is widely distributed, and most of the seabed has layered clay. However, the bearing capacity of bucket foundations in layered soil is significantly different from that in homogeneous soil. Currently, there is relatively little research on the bearing capacity of bucket foundations in layered clay. Therefore, the finite element analysis method is adopted to establish a bearing capacity calculation method of bucket foundations in double-layer clay. The axial failure mechanisms and ultimate bearing capacity of bucket foundations in double-layer clay are deeply discussed, and the corresponding ultimate bearing capacity calculation method is given based on the numerical analysis results. The combined bearing capacity of bucket foundations in double-layer clay is fully analyzed, and the evolution method of V-H, V-M, H-M, and V-H-M failure envelopes is given.
The deep digitization of power system business faces three major challenges: computational resources are prone to crashes, business response is slow, and platform maintenance is unsustainable. To address these issues, this paper proposes a domain-specific cloud Business Operating System (BOS) for new power systems. BOS establishes a unified management paradigm for four core digital objects—Containers, Tasks, Programs, and Data—through their standardized definition and indexed organization. Building upon this foundation, it implements three dedicated plugins to enable synergistic task-container co-scheduling, plug-and-play program integration, and optimized data access. This paper elaborates on BOS’s architecture and its rationale as an operating system, detailing the key technologies for object management. Case studies on a real-world regional power grid demonstrate that BOS effectively ensures the efficient execution of large-scale computational tasks, supports the agile integration of domain-specific models and algorithms, achieves seamless and efficient data connectivity across business chains, thereby providing a robust foundation for next-generation power system digitization.
Organic solar cells (OSCs) are attracting attention as a possible replacement for traditional photovoltaics because they are low-cost, lightweight, and have adjustable optoelectronic features. The commercialization of single-junction OSCs still faces challenges in achieving high power conversion efficiency (PCE) and operating stability. Recent developments in photonic crystals, plasmonics, nanophotonics, and metamaterials have significantly addressed these issues, especially in single-junction systems. This paper reviews the latest advancements in charge transport engineering, nanophotonic light-trapping methods, and nanostructured interfaces specifically designed for single-junction OSCs. It also highlights recent record-breaking efficiencies that exceed 20% PCE. We discussed integrating plasmonic nanoparticles, optical microcavities, nanostructured electrodes, and improved photonic materials to increase light absorption, exciton dissociation, and charge collection within the specific limitations of single-junction devices. Furthermore, we stress the important role of computational modeling and recent experimental breakthroughs in enhancing optical and electrical performance. Rather than treating optical and electrical processes independently, this review emphasizes the synergistic role of photonic enhancement strategies in simultaneously improving light trapping and charge transport, highlighting how nanophotonic designs influence carrier generation, recombination, and extraction in single-junction OSCs.
This study investigates the need for the adoption of modern handloom tools, including jacquard and warping drums, and evaluates their impact on income generation, production efficiency, market reach, and women’s empowerment in rural areas of Udalguri District, Assam. A purposive sampling method was used to survey 50 households in total. The findings reveal that the jacquard and warping drums significantly reduced the time required for weaving, mitigating weather dependence and improving productivity. Consequently, beneficiaries reported increased income, leading to independent entrepreneurship. The marketing strategies employed included direct market linkage through Civil Society Organizations (CSOs), participation, and connection with buyers to expand market access. Types of products included Silk and Cotton, and most of the products were sold in local markets. Training initiatives have been conducted to enhance product quality and design diversity. Weavers, who previously worked with limited designs, have now adopted innovative patterns to boost product demand. The study underscores the pivotal role of CSOs in hand-holding support, development of marketing linkage, tracking systems, and development of community resource persons (CRPs) through cluster-based training programs. The modern handloom tools play a transformative role in enhancing productivity, income, and market access, while simultaneously empowering women and strengthening rural economies.
In photovoltaic (PV) systems, precise wiring connections are critical to ensuring safe operation. Thus, effective reverse polarity protection is the first line of defense against polarity reversal caused by wiring errors. This paper systematically reviews existing methods for protecting PV systems against reverse polarity. First, the operating principles of PV side reverse polarity protection techniques are analyzed, along with their advantages and limitations. Additionally, DC-bus side protection methods are examined, and the effectiveness of different approaches is evaluated. Overall, this review provides researchers with the latest advances in reverse polarity protection for PV systems.
Offshore wind power is a key resource for achieving low-carbon transition in power systems with high penetration of renewable energy and power electronics, and it plays an increasingly important role in the development of modern power systems worldwide. The current research work focuses on aggregation-based development and operation technologies, grid-connected operation methods, and optimal scheduling strategies for offshore wind power, aiming to achieve the stable and healthy development of the offshore wind power industry. This paper reviews the characteristics of offshore wind energy systems and the integrated utilization technology for grid-connected operation. First, the aggregation features and system characteristics of new energy systems with large-scale offshore wind power are examined. Then, the system reviews key technologies for large-scale offshore wind power grid integration based on VSC-HVDC technology and analyzes the source-load characteristics of new energy systems incorporating offshore wind power. Finally, the development trends of offshore wind energy systems and integrated utilization technologies for grid-connected operation, as well as the technical fields that require further research in the future, are prospectively discussed.
In the operation management of hydropower stations, uneven scheduling often leads to issues such as resource wastage and unequal energy distribution; big data technology offers a new approach for optimizing the scheduling of hydropower stations in the information era. Taking the X Hydropower Station Group as a case study, this paper explores data acquisition, cleaning, clustering analysis, and the formulation of seasonal scheduling strategies to enhance the efficient utilization of hydropower resources and ensure the stable operation of the power grid. K-means clustering analysis is applied to explore typical output curves of cascaded hydropower stations, revealing the relationships between water levels, inflow rates, and load rates. Furthermore, a grey prediction model is developed to forecast future load rates, providing robust data support for short-term operational scheduling plans. The research not only improves monitoring and decision-support capabilities but also enhances the adaptability and response speed to seasonal changes, ensuring the stability and reliability of the power supply.
Driven by global energy transition goals, the large-scale development of offshore wind power imposes rigid requirements for professionalism, standardization, and timeliness on feasibility study reports (FSR). Traditional manual compilation and existing automated methods fail to meet these requirements due to interdisciplinary complexity, poor process controllability, and insufficient domain adaptation. To address these challenges, this paper proposes a configurable and interpretable offshore wind FSR generation system built on a three-tier framework that encompasses “data support, process orchestration, and quality assurance”. The system integrates a YAML-based workflow architecture, multi-level prompt engineering, and a comprehensive evaluation system. Notably, the introduced “Cyclic Aggregation Mode” enables the iterative generation and logical summarization of multi-subproject data, effectively distinguishing this system from traditional linear text generation models. Experimental results demonstrate that the proposed “Retrieval-Augmented Generation (RAG) + Large-scale Language Model (LLM) + Workflow” system outperforms baseline models with key metrics including semantic consistency (0.6592), information coverage (0.3908), structural compliance (0.5123), and an overall score (0.5965). Ablation studies validate the independent contributions of the RAG and Workflow components, thereby establishing the “RAG + LLM + Workflow” paradigm for intelligent professional document generation. This work addresses core challenges related to controllability, accuracy, and interpretability in high-stakes decision-making scenarios while providing a reusable technical pathway for the automated feasibility demonstration of offshore wind power projects.
Optimizing aerodynamic performance with low loads is a core objective in high-power wind turbine blade design. This study develops a blade aerodynamic optimization design platform based on the performance of a wind turbine. By applying automated design principles, the platform rapidly iterates to obtain blade profiles that meet turbine development requirements, significantly improving design efficiency and reliability. Key findings include That Optimizing chord length and relative thickness distributions substantially contribute to enhancing power generation while reducing load levels. Relative thickness and twist angle distributions are critical parameters influencing stall characteristics during blade operation. Superior aerodynamic performance notably increases annual rated power generation hours but simultaneously elevates blade thrust and root loads. Among the evaluated designs meeting turbine specifications, the #436 blade achieves a maximum power coefficient of 0.4679 while maintaining low ultimate and fatigue loads. Furthermore, when paired with the wind turbine, its rated wind speed reaches 10.9 m/s, and its annual rated power generation hours under various inflow wind speed conditions all meet the turbine system’s development requirements. Consequently, the #436 blade demonstrates exceptional system compatibility, making the 8.5 MW turbine equipped with this blade highly competitive in the market.
This study forecasts the power conversion efficiency (PCE) of organic solar cells using data from experiments with donors and non-fullerene acceptor materials. We built a dataset that includes both numerical and categorical features by using standard scaling and one-hot encoding. We developed and compared several machine learning (ML) models, including multilayer perceptron, random forest, XGBoost, multiple linear regression, and partial least squares. The modified XGBoost model performed best, achieving a root mean squared error (RMSE) of 0.564, a mean absolute error (MAE) of 0.446, and a coefficient of determination (R2) of 0.980 on the test set. We also assessed the model’s ability to generalize and its reliability by examining learning curve trends, calibration curve analysis, and residual distribution. Plots of feature correlation and permutation importance showed that ionization potential and electron affinity were key predictors. The results demonstrate that with proper tuning, gradient boosting methods can provide highly accurate and easy-to-understand predictions of organic solar cell efficiency. This work establishes a repeatable machine learning process to quickly screen and thoughtfully design high-efficiency photovoltaic materials.