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.

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 enable accurate assessment and online diagnosis of the DC-link capacitor health state in neutral point clamped (NPC) three-level inverters,

DC-side parameter identification with a focus on capacitor parameter degradation characteristics is investigated. To address the low accuracy and strong specificity of traditional capacitor-parameter identification methods, this paper proposes a digital-twin-based approach for DC-link capacitor identification in NPC three-level inverters. The method first analyzes the capacitor operating characteristics to determine the identification parameters, then builds a mathematical model and constructs a digital-twin NPC inverter using a fourth-order Runge-Kutta solver. A particle swarm optimization algorithm is employed to continuously update and refine the identification parameters until the digital twin matches the real system outputs.

Simulation studies verify the effectiveness of the proposed capacitor-health identification method under various conditions and confirm the consistency between the digital-twin model and the physical system. Results show that the method accurately identifies capacitor values, equivalent series resistance, and related load parameters, with identification errors generally within 5%.

The research results provide a reference for the parameter identification of DC-side capacitors in inverters.

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.

In order to apply the dynamic inclinometers based on low-cost micro electro mechanical systems (MEMS) inertial measurement units to ships conviniently, it is necessary to overcome the significant impact of the ship sway and surge motion in moored condition, which are periodic acceleration changes of several seconds to tens of seconds, on inclinometer measurements.

The algorithm for inclinometers in the situation is studied. Gyroscope measurements are used for attitude quaternion update. Then the horizontal accelerometer measurement values without the effects of roll and pitch are obtained. On this basis, the error analysis and impact analysis of sway and surge is carried out. The horizontal accelerometer measurement values are put through a low-pass filter with zero-phase-delay. Then Kalman filtering is performed using the low frequency component of horizontal accelerometer measurement values as the observation of the Kalman filter, and horizontal attitude errors and angular velocity measurement errors as state variables. Attitude closed-loop correction is conducted to make the MEMS dynamic inclinometer keeping the expected accuracy in a long time when the ship is moored.

An experiment is conducted using a certain type of MEMS dynamic inclinometer to validate the algorithm of reducing the effect of sway and surge. The measurement accuracy reached 0.2°(1σ) with an acceleration amplitude of 0.8g in the experiment,

verifying the effectiveness of the algorithm. Key words: low pass filter; Kalman filter; dynamic inclinometer; sway and surge

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 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.

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 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.