To review the current state of research on autonomous navigation decision-making and control technologies for intelligent unmanned surface vehicles, and to clarify the technical bottlenecks and development trends under scenarios of varying complexity,

a systematic investigation is conducted into the development history of key technologies for unmanned surface vehicles both domestically and internationally. It review addresses the differing technical requirements between low-to-medium complexity and high-complexity application scenarios, covering path planning, line-of-sight guidance, autonomous collision avoidance, automatic docking and undocking, multi-agent cooperative control, and autonomous recovery. It evaluates existing technological shortcomings and provides recommendations for future development.

Analysis indicates that autonomous navigation technology for open waters has matured and is gradually being implemented in engineering applications. However, core technologies for complex waters and complex missions still face developmental bottlenecks.

Looking further ahead, we propose establishing a standardized simulation and real-vessel testing evaluation system tailored to real-world scenarios. It will accelerate the rapid iteration and implementation of key technologies, thereby supporting the advancement of autonomous navigation decision-making and control technologies for unmanned surface vehicles in China.

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.

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.

To enhance the real-time computational capability of diesel generator set simulation models under dynamic conditions such as sudden load changes, and to address the issues of computational complexity and insufficient dynamic response timeliness in traditional mechanistic models during ship deployment,

a physics-mechanism-inspired multilayer perceptron (MLP) data-driven modeling method is proposed. By constructing a dual-hidden-layer network topology mapped to the electromagnetic-electromechanical transient process of generators, the approach achieves coordinated rapid calculation of the DC bus voltage and current of diesel generator sets.

The model effectively captures the nonlinear dynamic characteristics of diesel generator sets, improving computational efficiency while maintaining the accuracy of mechanistic models.

The research providing rapid-deployable technical support for real-time situational awareness and intelligent management of ship power systems.

To optimize the propulsion efficiency of trailing suction hopper dredgers (TSHD) in two typical operating conditions: low-speed operation and self high-speed navigation,

The ducted pitch propeller and the ducted pitch propeller are designed based on the graph method, and the performance difference of the two cases is compared. A multi-objective optimization platform is established, utilizing the Reynolds-Averaged Navier-Stokes (RANS) method and the non-dominated sorting genetic algorithmⅡ (NSGA-Ⅱ) to conduct an optimization study of the fixed-pitch ducted propeller that balances both operating conditions.

The results show that the pitch ratios of the ducted propellers obtained based on the graph method are very close for both operating conditions, allowing a compromise propeller design to achieve good efficiency in both conditions. Furthermore, compared to the ducted fixed-pitch propeller, the ducted controllable-pitch propeller has higher requirements for the disk area ratio, and under dredging conditions, the fixed-pitch propeller exhibits higher efficiency. Through optimization, the two optimal ducted propeller designs obtained show efficiency improvements of 5.65% and 5.59% under dredging condition, and increases of 7.70% and 8.09% under high-speed navigation condition, respectively.

It provides assistance and reference for subsequent research.

To effectively reduce fatigue damage, a reasonable dynamic cable design is required.

An optimization model based on an improved hybrid particle swarm optimization algorithm is established. It employ MATLAB to develop a genetic-chaotic particle swarm dynamic factor optimization algorithm and utilize the Orcaflex software for the overall design and optimization of platform dynamic cables. The optimization problem of deepwater dynamic cables is treated as the objective function, with parameters such as cable length, buoyancy block and counterweight block positions, and spacing as optimization variables. Building upon the foundation of the standard particle swarm algorithm and integrating genetic algorithms, it effectively prevent dynamic cable optimization parameters from falling into local optima. Chaotic initialization of initial particles is applied to ensure a uniform distribution in high-dimensional solution spaces. Dynamic inertia weight factors and learning factors are introduced to balance global and local search capabilities during optimization. Adhering to the Pareto principle, It formulate an objective function to facilitate multi-objective constrained optimization. The improved optimization algorithm shows better performance in terms of convergence, accuracy, and convergence speed.

It quickly and effectively balances the relationship between the maximum axial tension and the minimum bending radius of the cable and pipe, achieves the optimal design.

It provides strong support and guidance for practical engineering applications.

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.

To address the challenge of coordinated optimization between collision avoidance planning and motion constraints in the scenario where ships navigate close to dynamic surface targets, a hierarchical path planning method integrating the improved A^* algorithm, rapid reverse search iterative planning (RRSIP), and Hybrid A^* fine-grained planning, aiming to achieve efficient and kinematically compliant dynamic target tracking planning is proposed.

An improved A^* algorithm based on dynamic programming and oriented bounding box obstacle detection is used to plan a global reference path, reducing path length and redundant waypoints. For the dynamic target point, the RRSIP method is proposed, which reuses node information from prior searches via reverse search to quickly iterate and predict the approach point, avoiding global replanning and improving efficiency. Hybrid A^* algorithm is introduced near turning points for local refined planning, quickly generating a feasible path that satisfies ship kinematics and approach heading constraints.

Compared with other typical algorithms, the path length of the improved A^* algorithm proposed is reduced by an average of 4.17% and 1.79% respectively. The RRSIP method reduces the iterative planning time by at least 33.9% compared with the FR method. While ensuring path feasibility, the local Hybrid A^* planning reduces the time consumption by at least 72.1% compared with the global application.

The proposed method can effectively solve the problems of real-time performance and kinematic feasibility in dynamic target tracking, and significantly improve the autonomous tracking capability of ships in scenarios such as tugboat escort and maritime police law enforcement.

Aiming at the problem of unstable bus voltage output caused by Marine condition disturbance switching during the operation of ship direct current (DC) microgrid photovoltaic power generation units,

a photovoltaic power generation control strategy based on double integral sliding mode controller is proposed, including establishing an engineering model of photovoltaic cells and adopting a new exponential approach law and hyperbolic tangent switching function.

Simulation verification shows that the controller shortens the startup time to 0.002 s, reduces the overshoot to 2%, and effectively eliminates the steady-state error. Compared with traditional proportional-integral (PI) control, the startup time is reduced by 67% and the ability to resist load disturbances is enhanced by 41.2%.

The strategy significantly enhances the dynamic response speed and robustness, and is applicable to the dynamic working conditions of ships, addressing the shortcomings of traditional control methods under sudden light changes and load disturbances.

To quantitatively analyse the impact of digital transformation on enhancing the performance of shipbuilding enterprises and to understand its underlying mechanisms,

the research employs principal component analysis and regression models to assess the influence of digital transformation on corporate performance, based on its digital transformation level data from 2011 to 2023.

It reveals that for every one standardised unit increase in an enterprise's digital transformation level, its total output value grows by 30.4%. The combined contribution from the four application systems-design, manufacturing, management, and supply chain-remains relatively balanced. The research indicates that digital transformation is an indispensable pathway for the development of the shipbuilding industry. The key to successfully achieving digital transformation lies in enabling data sharing across design, procurement, manufacturing, and management systems. Crucially, realising data sharing hinges on establishing an enterprise data standards system and developing an enterprise data model that describes organisational behaviour, characteristics, status, and performance.

The proposed comprehensive measurement method for digital transformation levels, based on the depth of core system application, provides a reference for manufacturing enterprises to evaluate transformation effectiveness and optimise resource allocation.