This paper proposes a risk assessment method combining event tree analysis and numerical simulation, to assess the risks of truck-to-ship bunkering operations for methanol-fueled ships and enhance the safety level of methanol bunkering. Risk factors for methanol bunkering operations were identified based on ship layout, bunkering system, and operational procedures. An event tree model for methanol leakage was established considering influencing factors such as ignition conditions, leakage source location, and fire protection system response. The occurrence probabilities of different scenarios were calculated using typical failure data. On this basis, the three-dimensional computational fluid dynamics (CFD) tool FLACS was used to simulate the distribution of flammable hazard zones and toxic hazard zones following methanol leakage. Individual risk values were calculated according to the acceptable individual risk criterion. The results show that the individual risk of truck-to-ship bunkering operations for methanol-fueled ships is at an unacceptable level. It is recommended that based on quantitative calculation results, the restricted area for bunkering operations be delineated along the vessel's length and breadth directions according to the flammability limits, and a warning zone be established with the methanol tanker as the center and the toxic concentration limit as the radius. The research findings can provide input for the development of bunkering operation manuals for methanol-fueled ships.

To enhance the autonomy of underwater operations in marine and ship engineering, this paper proposes a method for object grasping based on an MB-Grasp model. First, an RGB image of the target is captured by a binocular camera mounted on the operation platform, and a disparity depth map is generated using a semi-global stereo matching (SGM) algorithm. The RGB and depth images are then fed into the MB-Grasp network to estimate the target's position, category, and grasp angle. Subsequently, the target's pose in the robotic arm base coordinate system is derived through coordinate transformation. Path planning is performed using the rapidly-exploring random tree star (RRT*) algorithm, and inverse kinematics solves the joint angles for each path point. A smooth trajectory in joint space is generated via polynomial interpolation to drive the robotic arm to execute the grasp. An experimental platform simulating the underwater operation environment of a ship was constructed, and autonomous grasping of simulated cables and small components was successfully achieved. This study provides a feasible solution for autonomous underwater operations in ship underwater engineering.

This paper proposes a dynamic response optimization algorithm for boarding equipment on floating offshore wind turbine foundations, to address the problems of large control delay and low control accuracy in the dynamic response of offshore boarding equipment under harsh sea conditions. Capacitive acceleration sensors are deployed on the boarding equipment to monitor the displacement amplitude under multi-degree-of-freedom motion response. The water level allocation problem of the ballast tank is transformed into a mathematical optimization model with the objective of minimizing the displacement difference at each monitoring point, and is solved using the Harris Hawks optimizer (HHO) to obtain the optimal water level allocation scheme. An improved PID controller is adopted to accurately track the water level allocation scheme, and the water pumps dynamically adjust the inflow or outflow of the ballast tank to actively suppress the motion of the boarding equipment. Experimental results show that this method can significantly reduce the motion difference between each position point, decreasing the maximum displacement error from 1.33 m to 0.20 m. Under extreme sea conditions, the dynamic response delay is only 0.5 s and the overshoot is small (fluctuating around 0.3%), demonstrating that the algorithm can maintain the stability of the boarding equipment more quickly and accurately, effectively improving the safety of boarding operations.

Permanent magnet synchronous motors (PMSMs) have the advantages of high power factor, good dynamic characteristics, high safety, and high torque density, and are widely used in ship electric propulsion systems. High-performance control of PMSMs requires rotor position and speed information. Installing mechanical position sensors increases the cost of the drive system, adds control difficulty, and reduces power density. Therefore, research on sensorless control technology for PMSMs is of great practical significance. Proportional-integral (PI) parameters are key factors affecting the dynamic performance of PMSM sensorless control systems. Traditional PI parameter tuning methods include the critical gain method, the decay ratio method, trial and error method, and theoretical calculation method. These methods require specific conditions and are computationally intensive and time-consuming. On the basis of traditional PI parameter tuning, this paper introduces the grey wolf optimization (GWO) algorithm to simulate the PMSM sensorless control system based on the high-frequency injection method, compares the dynamic performance before and after optimization, and verifies the correctness of the theory. This study provides a new approach for PI parameter tuning of PMSM sensorless control using the high-frequency injection method.

This paper proposes a fault diagnosis method based on regularized threshold wavelet decomposition, Markov transition field (MTF), and SE-DenseNet model, to address the problem of low diagnostic accuracy of ship power gearboxes caused by variable noise and complex working conditions in their operating environment. First, the squeeze-and-excitation networks (SENet) module is integrated into densely connected convolutional networks (DenseNet) to improve the model's feature extraction capability for MTF images. Regularized threshold wavelet decomposition is then used to denoise the original data. The preprocessed one-dimensional time-series data is transformed into a two-dimensional MTF image, which is fed into the improved DenseNet model for fault diagnosis, with a classification output head achieving accurate identification of fault types. The model is experimentally validated using the gear fault dataset from Southeast University and the fault data collected by a self-built ship power gearbox fault analysis unit. On the Southeast University dataset, the accuracy reaches 98.8% and 96.6% under two different working conditions, respectively. On the self-built dataset, the accuracy reaches 97.43% and 98.14% under two working conditions, respectively. The results show that the proposed model can identify fault types with high accuracy. Compared with other algorithm models, it exhibits good accuracy and generalization under different working conditions, providing a reference for the fault diagnosis of ship power gearboxes.

The reliability of hydraulic pipelines in waterjet propulsion devices is crucial to the navigation safety of waterjet-propelled ships. To overcome the limitations of traditional methods in real-time detection, positioning accuracy, and environmental adaptability, this paper proposes a method for leak detection and localization of waterjet propulsion pipelines based on a backpropagation (BP) neural network and Amesim simulation. A hydraulic system simulation model of a certain type of waterjet propulsion device is built using Amesim software and system simulation signals under different pipeline leak locations are collected to form the dataset. A BP neural network model is then constructed, trained, and validated to achieve fault localization. The prediction results of the BP neural network model are in good agreement with experimental measurements, showing a strong linear relationship, which indicates that the BP neural network model has satisfactory predictive performance. This study can provide a technical reference for precise leak localization and condition assessment of hydraulic pipelines in complex ship cabin environments.

Ship propulsion shafting system is the core component for power transmission, and its whirling vibration characteristics directly affect navigational safety and passenger comfort. Traditional shafting design relies on finite element numerical simulations and a trial-and-error process involving multiple iterations, which suffers from complex modeling, time-consuming calculations, and long design cycles. Unlike the conventional "trial-and-error" paradigm, forward design is a performance-oriented methodology that directly derives key design parameters meeting specified requirements. To address the aforementioned challenges, this paper proposes a forward design method for shafting systems. First, a parametric finite element model of a typical propulsion shafting was established using ANSYS APDL. The accuracy of the finite element model and its calculations was validated by comparing results with literature data. Based on this, a dataset containing 1000 sets of design parameters and corresponding first three orders of whirling natural frequencies was generated in batches. Second, a deep neural network prediction model was constructed, enabling high-precision and rapid prediction from shafting design parameters to whirling natural frequencies. The coefficient of determination R² on the test set reached 0.981, with an average error of 1.41%. Finally, a forward design model comprising a prediction network cascaded with a generative network was developed to achieve the inverse mapping from target vibration frequencies to key design parameters. The first-order fundamental frequency error of the design results was less than 5.5%. The results demonstrate that the proposed method achieves high accuracy and can effectively shorten the shafting design cycle, contributing to the intelligent and efficient design of ship propulsion shafting.

Traditional ship design management, centered on drawings and documents, suffers from prominent issues such as discontinuous design processes, scattered data carriers and low collaboration efficiency. To advance the digital transformation of ship design management, this paper constructs a whole-process management system for ship design that takes 3D models as the sole authoritative data source and bill of materials (BOM) collaborative integration as the core. This system covers seven key application scenarios including project management, plan management, review and verification management, submission and return review management, shipyard-institute collaboration management, model data management and archive management. All scenarios feature interlinked data and closely connected processes, forming an integrated whole-process management system. Combined with practical implementation in multiple ship-type projects, the application value of this system has been verified. The new digital design management model proposed in this paper effectively addresses the problems of data silos, version inconsistency and poor collaboration in traditional design management, providing a reference for ship enterprises in the construction of digital design management systems and facilitating their intelligent and digital transformation.

This paper proposes a three-dimensional mapping system for in-hold environments oriented toward cleaning operations, to address the operational characteristics of bulk carrier holds—namely enclosed spaces, weak geometric features, and the continuous removal of bulk materials during cleaning, which leads to a gradual decrease in pile height and constantly changing surface morphology—aiming to achieve a stable and updatable 3D representation of the hold environment. A multi-LiDAR and inertial measurement unit (IMU) collaborative 3D mapping system is built on a hold-cleaning machine platform. Spatiotemporal consistency across sensors is ensured through time synchronization and extrinsic calibration. Motion compensation of LiDAR point clouds is performed using IMU preintegration, and pose estimation is carried out via a tightly coupled LiDAR and IMU odometry method. Combined with sliding-window local mapping and an incremental voxel-based map representation, the system enables real-time construction and continuous updating of the in-hold 3D map. Verification through multiple field experiments shows that the proposed system can robustly estimate the pose of the hold-cleaning machine under feature-sparse and highly dynamic operating conditions, while producing structurally complete and continuously updated 3D point cloud maps. The generated maps effectively capture key structures, including hold walls, the hold floor, and bulk material surfaces, thereby meeting the requirements for remote visualization and operational awareness. The proposed system is well suited to bulk carrier hold-cleaning scenarios and provides a reliable foundation for three-dimensional environmental perception in autonomous hold-cleaning operations.

This paper constructs a surrogate model for load inversion and response prediction of tubular joints based on sparse measurement data by integrating two-dimensional Chebyshev orthogonal polynomials and a radial basis function (RBF) neural network, to address the challenge of dynamic monitoring and structural response prediction for the service status of tubular joints in offshore jacket platforms and achieve accurate load identification and rapid response prediction. First, given the complexity of the surfaces at key tubular joints of the jacket platform, two-dimensional Chebyshev orthogonal polynomials are used to mathematically represent the distributed loads acting on these surfaces. Combined with truncated singular value decomposition (TSVD) and generalized cross-validation (GCV), high-precision and stable load identification is achieved from pre-set sparse measurement response data. Then, based on the inversion results, a high-quality training sample set consisting of 100 working conditions, covering spatial coordinates and load characteristic coefficients, is generated. Each sample uses spatial node coordinates and load coefficients as input features, with the corresponding equivalent stress as output. Finally, the RBF surrogate model is constructed and trained, with the coefficient of determination ( R²) used to evaluate model performance. The results show that the model achieves an R² of 0.913 78 on the validation set, enabling high-precision and high-generalization prediction of the stress field at key tubular joints of the jacket platform, providing effective technical support for real-time condition assessment and fatigue damage accumulation prediction of the jacket platform.

To address the technical bottlenecks such as large data volume and limited transmission bandwidth encountered in constructing a high-fidelity digital twin of a liquefied natural gas (LNG) carrier, this paper proposes a ship-to-shore data transmission system that integrates reliable transmission and efficient compression. First, the system uses standardized interfaces to achieve multi-source data acquisition, and through a data hierarchical transmission strategy, extracts the value of the data. On this basis, the GZip algorithm is introduced to efficiently compress key data such as those from the liquid cargo tank system. Second, based on the data transmission architecture of the Message Queuing Telemetry Transport (MQTT) protocol, a quality of service (QoS) level confirmation mechanism is introduced to ensure effective data transmission, and an intelligent network disconnection recovery strategy is proposed to enable local caching at the ship end and ensure complete data transmission. Finally, real ship application verification shows that the proposed system achieves a compression rate of over 93% while ensuring 100% complete data transmission, and significantly reduces the end-to-end transmission delay under limited bandwidth. This study comprehensively improves the effectiveness of data transmission for the digital twin of LNG carriers from three dimensions—transmission efficiency, reliability, and real-time performance—and verifies the effectiveness of the proposed transmission strategy and modules, laying the foundation for the engineering application of high-value ship digital twin systems.

This paper proposes an intelligent optimization method based on Gaussian process regression (GPR) surrogate model and particle swarm optimization (PSO) algorithm for the heat dissipation optimization of finned liquid cooling plates. First, a sample dataset of fin angles, temperatures, and pressures is constructed through COMSOL simulations, and the GPR surrogate model is trained to establish a high-precision response surface. On this basis, the PSO algorithm is used to perform global optimization within the feasible range of fin angles, with the optimization objectives of simultaneously reducing the bottom wall temperature of the liquid cooling plate and the internal flow pressure. This method can efficiently determine the optimal fin angle. The entire process uses only coarse mesh data to train the model, and the average relative errors of the peak temperature and pressure are 0.07% and 5.36%, respectively. At a flow rate of 500 mL/min, the peak temperature of the liquid cooling plate after fin angle optimization is reduced by approximately 8.94 K. This method significantly reduces the dependence of the optimization process on computing resources and provides an effective approach for the rapid design optimization of finned liquid cooling plates.

This paper presents a review of the application of peridynamics in the structural analysis of large composite wind turbine blades, with emphasis on damage problems such as crack propagation, interlaminar delamination, adhesive debonding, sandwich-interface failure, and fatigue accumulation. Peridynamics describes interactions between material points through a nonlocal integral formulation, thereby avoiding the dependence of conventional continuum-based methods on the continuity assumption of the displacement field. This feature provides a new theoretical route for analyzing crack growth and structural damage in composite blades. In view of the damage analysis requirements of wind turbine blades under complex service conditions, the fundamental concepts of peridynamics are summarized, and recent modeling advances are reviewed for typical blade components, including composite laminates, thick transition regions, sandwich structures, web regions, and adhesive joints. Its applicability and application potential under fluid-structure coupling, thermo-hygro-electrical environments, impact loading, and fatigue damage are further discussed. Existing studies indicate that peridynamics is well suited to analyzing crack propagation, delamination, and debonding mechanisms in local high-risk regions of blades. However, its engineering application to full-scale blades is still constrained by computational efficiency, parameter calibration, constitutive modeling, and experimental validation. Future work should focus on combining global finite element models with local peridynamic models, thereby supporting structural integrity assessment of large composite wind turbine blades.

Data preprocessing is a core step in knowledge graph construction, consisting of two main stages: data collection and information extraction. This paper systematically reviews mainstream data preprocessing methods based on rules and lexicons, statistical machine learning, and deep learning, and thoroughly analyzes their technical principles and application limitations in entity recognition and relation extraction. Existing methods rely heavily on manual rules and suffer from weak semantic generalization, making it difficult to achieve cross-domain knowledge transfer. To address these issues, this paper explores a novel paradigm of “semantic-driven and automated extraction” based on large language models. By generating deep semantic embeddings through pre-trained large language models and combining vector similarity computation, it enables unsupervised and context-aware information extraction, driving the intelligent transformation of knowledge graph construction. The current approach is still in the exploratory stage, facing challenges such as high computational cost and low interpretability. Future research should focus on lightweight model design, multimodal semantic alignment, and domain knowledge integration to improve the efficiency of knowledge graph construction and model interpretability.

The gasification process is the core of the marine fuel gas supply system, whose simulation research is critical for ensuring the rational design of the gasification system and the safe operation of the fuel gas supply system.The optimization of gasification scheme was carried out based on the high- and low-pressure fuel gas supply system of a large LNG-powered vessel. A method for simulating the gasification process using Aspen HYSYS software under the maximum operating condition was proposed, to verify the rationality of the gasification scheme. And, the gasification process under various typical operating conditions was then simulated and validated. The influence of LNG methane concentration and inlet pressure on the design of the gasification scheme was explored. It was found that the heat duty of the vaporizer increased with LNG methane concentration and decreased with inlet pressure. This provides a theoretical basis for the optimal design of the gasification process in marine fuel gas supply systems and contributes to the safe and stable operation of dual-fuel power systems.

Water-lubricated bearings are critical components in marine propulsion systems, directly influencing vessel operational safety and reliability. This study investigates the mechanism of water film pressure distribution under different slat profiles by establishing a fluid-structure interaction (FSI) analysis model of the bearing. Systematic numerical calculations were carried out for various slat types and groove arrangements. The effects of flat, convex, and concave slats on bearing load capacity and wedge-shaped water film pressure distribution were compared. The results indicate that concave slats generate a larger positive pressure zone in the circumferential direction and exhibit more continuous water film pressure distribution. In the studied 24-slat bearing, the difference in maximum water film pressure between the slat-down and groove-down arrangements was only 1%. In contrast, the local groove configuration increased maximum water film pressure by 17.6% compared to the other two arrangements, also demonstrating superior axial pressure distribution. In conclusion, the combination of concave slats and a local groove design significantly enhances the load-carrying capacity and operational reliability of large-scale water-lubricated bearings.

This study investigates the slamming load characteristics on the ladder of the self-propelled cutter suction dredger Tianjing under head-sea conditions. The vessel’s motion responses are computed based on potential flow theory, and the slamming pressures on the ladder are analyzed using the Von Kármán and Wagner impact theories, as well as the GJB design code method. Results show that the GJB method is more suitable for evaluating hull slamming loads. Ladder slamming predominantly occurs near the waterline, and the impact loads follow a three-parameter Weibull distribution. Increase in ship size significantly reduces motion responses, slamming frequency, and pressure intensity. However, short-period waves tend to intensify slamming risks. Based on the three-parameter Weibull model and extreme value theory, the design extreme values of ladder slamming pressure are predicted, providing a reference for the structural design of next-generation large self-propelled cutter suction dredgers.

To improve the efficiency and accuracy of 3D hull modeling, address data fragmentation issues when exporting hull models from different software, reduce the data volume of hull surfaces, and better support hydrodynamic performance calculations and experimental model fabrication, this paper proposes a hull modeling method based on offset points and lines plan using CATIA V6. A technical system is established for repairing defective hull models, reconstructing surfaces, and verifying quality. A series of EKL programs are developed for rapid hull modeling. CATIA V6 offers powerful logical and professional capabilities, making it well suited for modeling marine engineering series models. The development and summarization of hull modeling methods based on CATIA V6 are of positive significance for the overall design of ships.

Additional resistance induced by shallow water effects is a critical factor influencing fuel economy during ship design and operation, especially in shallow sea areas such as the Baltic Sea. To address fuel efficiency challenges in such regions, this paper proposes a speed optimization model that accounts for shallow water resistance. The model adopts a multi-objective genetic algorithm integrated with a hypervolume indicator to optimize both total fuel consumption and voyage time. Three operational strategies are investigated: minimizing overall fuel consumption, applying shallow water resistance constraints, and maintaining constant optimal engine power. The effectiveness of each strategy is assessed on a typical Baltic route. Results show that the proposed optimization strategy can reduce fuel consumption by approximately 3.6% without compromising voyage schedules. Moreover, the additional fuel consumption attributed to shallow water effects can be limited to 2.5%. The proposed approach demonstrates strong effectiveness and engineering applicability, providing theoretical support and optimization guidance for green ship design and intelligent speed control.

Ice-induced vibration is a significant risk factor for the operational safety and structural stability of offshore oil platforms. If the hazards posed by sea ice, currents, and other environmental loads are not properly assessed, offshore platforms may suffer catastrophic consequences such as collapse, oil spills, and casualties. Due to the uncertainty of ice load and ice-induced vibration, traditional numerical analysis and statistical prediction methods struggle to simultaneously meet the requirements for high-precision early warning and timeliness in ice-induced vibration risk assessment. Therefore, based on the measured winter sea environment data of the JZ20-2MUQ platform in the Bohai Sea, this paper uses the interpretive structural model (ISM) and the expectation maximization (EM) algorithm to construct a Bayesian network model. Quantitative analysis indicates that the main risk factors affecting the vibration of the offshore oil platform in the Bayesian network are ice thickness, ice velocity, ice direction, wind speed, and tidal flow velocity and direction. Furthermore, the accuracy and validity of the model are verified. The average accuracy of the model is over 0.75, and the accuracy of the target node is above 0.9. Verification results confirm that the model has high accuracy and can provide reliable data support for risk prediction.

When navigating in coastal areas, a ship may collide with a coastal bank, resulting in grounding. This issue involves interactions among the wave, the ship, and the coastal bank, with the key aspect being the contact between the ship and the bank. To solve this problem, a smoothed particle hydrodynamics (SPH) method is employed to simulate wave motion, wave-ship interaction, and wave-bank interaction. A discrete element method (DEM) is used to handle the interaction between the ship and the bank. Accordingly, a coupled SPH-DEM approach is established to study ship grounding. Numerical analysis is conducted for a bank with a 1:10 gentle slope under three wave periods, focusing on ship motion responses of pitch, surge, heave, and contact forces between the ship and the bank. The results show that under short-wave-period conditions, the ship remains grounded on the bank with small motion responses. Under the long-wave-period condition, the ship lifts off the bank and floats freely, with significantly increased motion responses. Regarding the contact forces between the ship and the bank, the vertical components are significantly larger than the horizontal components. In the early stage of grounding, the vertical components of the contact forces reach their peak values, accounting for up to 65.52% of the ship's gravity for all test cases. In the stable stage, the vertical components account for 32.84% and 41.89% of the ship's gravity for the two short-wave-period cases, and decrease to 0% for the long-wave-period case as the vessel is no longer grounded.

The cargo containment system of a liquid hydrogen carrier operates at extremely low temperatures, and the hull structures are also exposed to such low temperatures, resulting in a significant temperature gradient. This temperature gradient poses a threat to the safety of both the cargo containment system and the hull structure. This study conducts a temperature field analysis of the hull structure and cargo containment system of a 40 000 m³ liquid hydrogen carrier. The computational fluid dynamics (CFD) method is adopted, considering thermal conduction, thermal convection, and thermal radiation, as well as the thermal coupling effect of the hull structure. A partial cargo hold model is adopted for analysis, which properly reflects the structural arrangement of the cargo hold area and the cargo containment system. The temperature distribution in the cargo holds is analyzed based on the IGC (International Code for the Construction and Equipment of Ships Carrying Liquefied Gases in Bulk) and USCG (United States Coast Guard) load cases. The results provide a reference for the design of similar vessels.

To determine whether the hydrogen release rate of a solid-state hydrogen storage tank can meet the power response requirements of a hydrogen-powered inland river vessel, a simulation analysis was conducted to study its operational characteristics. A 3 000-ton inland container ship was selected as the research object. A whole-ship multi-system integrated power simulation model and a solid-state hydrogen tank model were developed in MATLAB/Simulink. Two calculation conditions were designed: calculating the minimum lithium battery capacity configuration and analyzing the dynamic characteristics of the solid-state hydrogen tank. The results show that, while satisfying the target speed (with a mean squared error of less than 5% between feedback and target speeds), the calculation of minimum lithium battery capacity must account for both the depth of discharge and the limitation imposed by the maximum charging current on the rated capacity. When the lithium battery is configured to minimum capacity, the hydrogen storage mass fraction, temperature, and pressure of the solid-state hydrogen tank remain within the design limits throughout the entire voyage. Additionally, the heat output of the fuel cell meets the thermal demand during hydrogen release from the solid-state hydrogen tank. These results indicate that the hydrogen release rate of the solid-state hydrogen tank can satisfy the propulsion power response requirements of the target vessel. Finally, suggestions for practical application are provided: under low-load conditions, the temperature and flow rate of the circulating heating water for the solid-state hydrogen tank must be dynamically adjusted according to hydrogen consumption demand; otherwise, the tank pressure and temperature may exceed allowable limits. To avoid a sharp pressure drop during the initial hydrogen release stage, a buffer tank should be configured and operated in conjunction with the solid-state hydrogen tank in practical applications.

Since the beginning of the 21st century, with the rapid development of the new-generation information technology and high-end manufacturing industries, major shipping and shipbuilding nations have engaged in research and demonstration projects on intelligent ships, and have accelerated their strategic positioning in this field in recent years. This paper analyzes the water environment and navigation characteristics of inland waterways, reviews research progress on key technologies for intelligent inland ships—including intelligent navigation, cybersecurity, intelligent devices and systems, testing, verification, and evaluation—as well as the development of classification standards for intelligence levels of inland ships. It also presents typical engineering cases of intelligent inland ships and discusses future development trends. At present, the overall development of intelligent ships is still in its early stages. The domestic shipbuilding and shipping industry should align with China's development strategy of “new quality productive forces” to strengthen the domestic industrial chain and consolidate the industry's leading position in the new economic development paradigm.

Alternative design and arrangements (AD & A) represent both a distinctive feature and a challenge in cruise ship design, and constitute one of the core technologies that must be urgently mastered to achieve independent design of cruise ships in China. Four typical application scenarios of AD & A for cruise ship fire safety, namely super-large main vertical zones, lifts without independent machine rooms, non-steel structural materials, and super-large fire doors, are first analyzed. The process of AD & A for fire safety is then summarized and refined, providing a concise overview of the main tasks involved in the four stages: preparation, preliminary qualitative analysis, quantitative analysis, and final scheme implementation. Finally, the key technologies and research progress in AD & A for cruise ship fire safety are elaborated in detail from three aspects: fire hazard identification, life safety performance criteria, and rapid approval of AD & A schemes. Meanwhile, several suggestions on the foundational works related to AD & A are offered.

Large cruise ships, as integrated energy systems combining propulsion and hotel services, are characterized by complex load structures, diverse operating conditions, and stringent emission constraints. With increasingly stringent decarbonization policies in international shipping and the transition of China’s cruise industry from import-based operation to independent design and construction, the issues of cruise operational optimization and energy management have evolved from standalone energy-saving measures into complex engineering problems involving multi-energy system coordination and full life-cycle optimization. This paper systematically reviews research on cruise operational optimization and energy management, summarizes the architecture and load characteristics of cruise energy systems, analyzes the current status of optimization methods and energy management strategies, and further discusses development trends such as multi-energy integration, low-carbon fuels, and data-driven approaches. The study provides a reference for improving energy efficiency, reducing carbon emissions, and promoting green maritime transport.

To improve the noise reduction accuracy of reciprocating friction vibration signals of piston ring friction pairs in marine diesel engines, this paper proposes a collaborative noise reduction method that integrates complementary ensemble empirical mode decomposition (CEEMD) and an adaptive correlation coefficient screening mechanism. Simulated vibration signals were obtained using the BRUKER UMT friction and wear testing machine to construct an experimental dataset. The original signals were subjected to multi-scale decomposition using CEEMD, and effective intrinsic modal components were screened using an adaptive correlation coefficient threshold. The dominant noise component was removed to achieve signal reconstruction. MATLAB software was applied to implement the noise reduction processing. Three types of indicators—signal-to-noise ratio (SNR), normalized cross-correlation coefficient (NCC), and mean square error (MSE)—were used for quantitative evaluation. Multi-scale permutation entropy (MPE) theory was also innovatively introduced to verify the dynamic characteristics of the denoised signal. The experimental results show that the CEEMD adaptive correlation coefficient screening method has significantly improved noise reduction performance compared to other methods. The correlation coefficients between the multi-scale permutation entropy image of the stripped noise signal and the overall laboratory noise signal image are all above 0.8, thus proving the accuracy of effective signal denoising.

Economic globalization has driven the development of China’s manufacturing industry. The rise of major clean energy projects such as offshore wind power has increased the demand for maritime transportation of super-large structures. In view of the problems of poor universality, cumbersome procedures, and high cost faced by the current lashing tools for super-heavy marine modules such as offshore wind power jackets during sea transport, this paper designs a new gantry-type lashing tooling and systematically evaluates its strength and deformation through finite element analysis. The results show that when transporting a typical wind power jacket module, the maximum stress and deformation of the tooling are 236.62 MPa and 2.15 mm, respectively; when transporting an offshore pile module, the values are 110.85 MPa and 1.80 mm, respectively. Both are below the allowable stress of Q355B steel (273.08 MPa). The tooling has reliable strength, strong versatility, and high reusability. It can significantly shorten the lashing preparation cycle for major components such as wind power jackets, reduce the comprehensive transportation cost, and provide a feasible solution for cost reduction and efficiency improvement in the maritime transport of major components within the offshore wind power industry chain.

The unsteady forces generated when a pump-jet propulsor operates behind a submarine hull are a major excitation source for hull vibration and radiated noise. Accurate prediction of these forces is therefore central to the design of low-noise propulsors. This study presents a numerical framework that couples the prediction of tonal forces via unsteady Reynolds-averaged Navier-Stokes (URANS) equations with the estimation of broadband forces using large eddy simulation (LES). The framework is applied to a model-scale pump-jet installed behind the SUBOFF hull. Numerical simulations characterize the unsteady forces on the rotor and stator blades, yielding their respective tonal and broadband force spectra. The results indicate that the tonal force and moment fluctuations occur predominantly at the first and the second blade passing frequency (BPF), while the broadband force and moment spectra exhibit a distinct hump around the first BPF.

To investigate the distribution and characteristics of slamming loads on a self-propelled jack-up wind turbine installation vessel (WTIV) during transit and preloading conditions, a three-dimensional hydrodynamic numerical tank model was established based on Computational Fluid Dynamics (CFD). The generation mechanism, time-history curves, and spatial distribution of slamming loads under operational sea states and two typical air gap conditions were obtained and analyzed. The results show that under transit (free-sailing) conditions, the peak slamming load increases with wave height and wave steepness, primarily depending on the water-entry velocity at the slamming point and the vessel's hull flare. During preloading operations, a trapped air cushion beneath the hull is significantly compressed and struggles to escape, leading to higher slamming loads at smaller air gaps. In the same region, slamming loads under beam seas are 6% to 14% higher than those under head seas, while also inducing a lateral horizontal load of approximately 300 t. The characteristics of slamming pressure obtained in this study provide critical references for the structural safety of similar vessels and safe operations during preloading conditions.