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.

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.

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.

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.

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 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 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.

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 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 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.