Aims to establish a cloud-edge-device collaborative intelligent management and control system to enhance production process controllability, shorten construction cycles, and strengthen decision support capabilities.

Driven by production plans and guided by process flows, a three-level cloud-edge-device collaborative architecture is designed. By constructing a physical-information fusion environment in the ship block workshop, a "plan-resource-execution" linkage mechanism is established. An improved genetic algorithm (IGA) combined with simulated annealing is proposed for the dynamic scheduling model, alongside the development of a multi-source heterogeneous data fusion engine to achieve full-factor visual management and control.

After system implementation, the ship block construction cycle is reduced by 19.7% compared to traditional models, production anomaly response time is shortened by 75%, and the equipment load balancing index is optimized by 28%.

The proposed cloud-edge-device collaborative management and control model effectively resolves the dynamic matching dilemma between planning and execution in ship block workshops. The established "perception-analysis-decision-execution" closed-loop system provides a reusable implementation framework for intelligent ship manufacturing, promoting the digital transformation of the shipbuilding industry.

To systematically review the technological evolution of unmanned surface vehicles (USVs) and explore the path of their convergence with intelligent ships, aiming to overcome the performance bottlenecks of individual USVs regarding endurance, computing power, and communication.

It reviews the centennial evolution of USVs, tracing the transition from radio remote control to fully autonomous navigation, and from single-agent operation to swarm collaboration. It provides an in-depth analysis of four core technologies: environmental perception, decision planning, motion control, and communication links. On this basis, the study focuses on the convergence trend between USVs and large intelligent ships, analyzing the "mothership-drone" cross-domain collaborative operational mode and the cloud-based management system driven by digital twins.

It indicates that current USV technology is undergoing an intelligent transition from "perception-avoidance" to "cognition-gaming". Furthermore, the "mothership-drone" collaborative mode, by combining the platform advantages of large ships with the high maneuverability of USVs, effectively resolves the challenges of individual USV operations in complex deep-sea environments and the "last mile" maneuvering difficulties for large intelligent ships entering and leaving ports, thereby achieving complementary advantages.

Collaborative mode represents a mainstream paradigm for future maritime operations. However, continuous breakthroughs are still required in areas such as regulatory adaptability, communication network security, and green energy propulsion. The findings provide theoretical references for constructing a new integrated air-surface-underwater intelligent maritime equipment system.

Existing autonomous berthing technologies rely on precise mathematical ship models and mostly employ empirical formulas for modeling. However, in actual berthing scenarios, influenced by environmental factors and speed, these methods cannot accurately reflect the current ship maneuvering status in real-time, leading to limited berthing control accuracy. To address the aforementioned problems,

a ship autonomous berthing control method based on physics- informed neural networks (PINN) is proposed. The method constructs a real-time dataset using a sliding window and identifies ship maneuvering parameters in real-time through the physics-informed neural network. An adaptive controller based on gain scheduling is designed to dynamically adjust control gains using the identified parameters, realizing precise ship berthing.

Experimental results demonstrate that the PINN network can converge rapidly under dynamic conditions and accurately identify ship parameters, with a goodness of fit reaching 0.97. In berthing experiments, the method ensured that the terminal heading deviation and lateral error converged to a minimal range, achieving smooth and safe docking, with a heading error of 0.13°.

The method effectively resolves the failure of traditional control algorithms caused by model mismatch under unknown ship parameters and complex working conditions, offering a safe and interpretable adaptive berthing control solution.

To improve the accuracy and robustness of ship trajectory prediction,

an ABiM-Ship network that encodes historical trajectories using a bidirectional selective state space model is proposed. An attention mechanism to explicitly align trajectories with heading and speed is utilized. A two-stage end-to-end joint prediction is designed, first regressing future trajectories, heading, and speed, then refining them using residual correction. Huber loss is introduced to constrain physical errors and stabilize convergence.

The experimental results show that this network outperforms traditional mainstream baselines in terms of average prediction error over short, medium, and long distances, achieving high prediction accuracy. The representation method, two-stage structure, and Huber loss all contribute significantly to performance gains.

The research findings achieve explicit coupling and coarse-to-fine prediction for trajectories, heading, and speed while maintaining linear temporal complexity. They have good reproducibility and scalability, providing a generalizable technical path and engineering reference for intelligent navigation and collaborative scheduling in complex maritime areas with high traffic density.

To investigate the carbon fiber reinforced polymer (CFRP) hull lightweighting effect on the environmental impact,

Life cycle assessment on an 11 m CFRP high-speed vessel is conducted, focusing on atmospheric pollution indicators: global warming potential (GWP) and ozone depletion potential (ODP).

The results demonstrate that the fiber content adjustment-based lightweight design algorithm can achieve a 12.5% reduction of hull structure mass while maintaining structural safety by increasing fiber content from 40% (original case) to 55% (lightweight design case) approximately. However, due to the significantly higher environmental burden of carbon fiber production compared to resin, the manufacturing phase saw increases of 10.24% in GWP and 14.37% in ODP. Conversely, the operational phase benefited from reduced fuel consumption due to lightweighting, saving 323.98 t of fuel over 25 years, which decreased GWP and ODP by 4.13% and 4.19%, respectively.

The operational phase ultimately offset the negative environmental impacts of the manufacturing phase. Critical insights for green ship design and maritime industry decarbonization strategies is provided.

In order to formulate a reasonable control strategy for electrothermal de-icing of wind turbine blades,

an experimental approach utilizing electrothermal heating component prototypes has been employed to investigate the influence of factors such as ice thickness (5 mm and 20 mm), heating power (ranging from 400 W to 1 000 W), and ambient temperature on the ice-melting process within an environmental chamber set at temperatures between -20 ℃ and -5 ℃.

The results show that for each 1 ℃ decrease in ambient temperature, an additional approximately 40 W of power is required to sustain the same final temperature, revealing a linear coupling relationship between heating power and ambient temperature with respect to the final temperature of the heated surface. Furthermore, when the ice thickness is 5 mm, the duration of the gradual temperature rise phase during ice melting extends from 2.5 minutes to 10.0 min, and a heating power of 800 W or higher becomes necessary for effective ice melting when the ambient temperature falls below -15℃. As the ice thickness increases to 20 mm, the heat absorption by the ice layer itself increases by 3.2 times, leading to a proportional extension of the ice-melting time by 55%.

Therefore, in practical engineering applications, it is imperative to dynamically adjust the heating power based on real-time data on ambient temperature thresholds and ice thickness, while also optimizing the control logic by taking into account the critical conditions for ice shedding and the temperature abrupt change characteristics during the ice-melting stagnation phase.

Aiming at the problem of the impact of center body position on nozzle cavitation intensity and flow morphology,

based on the CFD-Fluent, the Mixture multiphase model is employed, the k-ε turbulence model, and the Schnerr-Sauer cavitation model, numerical simulations are performed for nozzle flow fields with varying center body positions. The validity and reliability of the methodology are confirmed through comparison with prior research results.

The nonlinear regulatory effect of the center body's axial position on the internal flow field and cavitation intensity within the nozzle is systematically quantified. It clearly identified the junction of the nozzle throat and diffuser section as the optimal position most prone to triggering cavitation effects. Cavitation intensity and mass transfer rate peaked when the center body is located at this position. Cavitation is absent on the center body when positioned inside the nozzle. When center body located outside the nozzle, the downstream extent of the cavitation zone changed minimally, and cavitation intensity gradually diminished as the center body moved further downstream. Furthermore, based on the large eddy simulation (LES) method, an in-depth analyze the complex unsteady flow structures and large-scale radial diffusion characteristics of the vapor phase downstream of the nozzle under the optimal cavitation position condition is further conducted. Significant unsteady features are observed at a location 10 nozzle diameters (10D) downstream, where the vapor phase distribution expanded radially to four times the nozzle diameter (4D).

The research clarifies the regulatory mechanism of center body position on cavitation intensity, providing a theoretical basis for optimizing cavitating nozzle design. The findings contribute to enhancing the efficiency of industrial processes reliant on cavitation, such as cleaning and fragmentation, and offer theoretical support for developing adjustable center body structures to enable real-time control of cavitation intensity.

To address the severe scouring challenges faced by offshore wind power infrastructure,

the scouring problem of the four-pile jacket foundation of offshore wind power under the action of ocean currents through numerical simulation. Using FLOW-3D software is studied, adopting the large eddy simulation (LES) turbulence model and the sediment transport model, the validity of the numerical model was verified through comparisons with experimental results, the scouring process of the four-pile jacket foundation under the action of a single steady flow is simulated. The development and changes in the scour pit morphology around the foundation over time are analyzed, and the effects of different flow velocities, incoming flow angles, and pile spacings on the scouring of the four-pile foundation are studied.

The results show that the group pile effect is central to the scouring characteristics of four-pile jacket foundations. The incident flow angle alters the shielding interactions among piles, leading to an asymmetric distribution of scour morphology. Pile spacing modulates the intensity of interference between adjacent piles; as the spacing increases, the group pile effect gradually weakens, and the scour pattern transitions from a unified, interconnected scour hole to relatively independent local scour holes. The maximum scour depth is primarily governed by flow velocity and exhibits only minor variation with changes in pile spacing.

The research findings provide a reference for the scouring of four-pile jacket foundations in offshore wind farms.

To overcome the limitations of the traditional random sample consensus (RANSAC) algorithm in cylindrical segmentation, a novel method is developed for segmenting point clouds of ring-ribbed shells by integrating structural features and statistical methods.

Initially, the model surface area feature is utilized to estimate the proportion of inliers, thereby enhancing the accuracy of initial parameters. Subsequently, principal component and radius constraints are introduced to enhance the accuracy of cylinder identification and reduce the number of iterations. Then, a weight function-based correction method is applied to mitigate outlier interference, thereby improving the accuracy of cylinder fitting. Finally, the DBSCAN algorithm clustered the point clouds of ring-ribs, and an improved RANSAC algorithm identified localized features, thus achieving precise measurement of component dimensions.

Experimental results show that the proposed method effectively addresses the intelligent recognition and dimensions measurement of components in various parts of the ring-ribs, significantly improving the recognition speed and accuracy of cylindrical shell and ring-ribs. The precision, recall, and overall accuracy of cylindrical shell reach 96.9%, 99.5% and 96.4% respectively, with a computational speed increase of approximately 4.6 times. The measurement error for ring-rib component dimensions is within 0.2%.

Compared with traditional methods, the proposed method offers significant advantages in the accuracy and computational efficiency of point cloud segmentation.

In order to study the potential application of the diesel fuel direct coal liquefaction diesel (DDCL) and polyoxymethylene dimethyl ethers (PODE) mixed fuel in marine diesel engines,

the volume of fluid (VOF) method is adopted to simulate and study the influence of different fuels, nozzle hole conical and the angle between the nozzle hole and the needle valve axis on the cavitation flow in the nozzle.

The results show that the cavitation intensity in the nozzle hole with a larger angle between the needle valve axis is greater, while there is no obvious cavitation in the nozzle hole with an angle less than 60°. The gradually converging nozzle hole can effectively suppress cavitation and has good flowability and low turbulence intensity. With the increase of PODE content, the density of the mixed fuel increases, the cavitation, turbulence intensity and flow loss in the nozzle hole decrease, and the effective flow area increases. The mass flow rate of DDCL is lower than that of petrochemical diesel. After blending the same volume of PODE, the mass flow rate of the former increases by 6.2% and is higher than that of petrochemical diesel.

The blended fuel of PODE-coal direct liquefaction diesel can reduce the internal flow loss of nozzle orifices, improve the flow performance of the orifices, and increase the mass flow rate.