To effectively monitor the total longitudinal bending moment of icebreakers, an improved shape function method and a measurement point layout strategy were introduced to enhance the accuracy of hull bending moment identification. First, through numerical analysis, hull section of an icebreaker was taken as the research object, and the solution strategy applicable to the shape function method and its regularization parameter selection strategy were investigated. Subsequently, to address the complex stress distribution of icebreakers, a measurement point layout strategy was proposed and validated. The results show that both the L-curve criterion and the hard threshold selection strategy can improve the accuracy of the improved shape function method. The proposed measurement point layout strategy can effectively improve the stability of bending moment identification. The bending moment identification scheme established in this study provides an improved approach for bending moment identification of icebreakers.

The bi-moment is a key factor in calculating the warping normal stress induced by restrained torsion in container ships. At present, bi-moment evaluation is mainly carried out using the transfer matrix method or one-dimensional finite element methods based on thin-walled structural mechanics, which involves relatively complex computational procedures. In this study, the CCS software for combined bending moment and longitudinal strength assessment of large-opening ships was employed to calculate the bi-moment for twelve container ships. The longitudinal distributions of torque and bi-moment were systematically analyzed for single-island and double-island configurations. Distinct bi-moment distribution characteristics were identified between the two ship types, revealing that the superstructure (i.e., island layout, referred to as "island" hereafter) significantly influences the bi-moment distribution. With torque and the bending-torsion characteristic coefficient selected as governing parameters, regression analyses were conducted to establish longitudinal bi-moment distribution formulas for single-island and double-island container ships, yielding two sets of concise regression formulas. These formulas can be applied to the global bending-torsion strength assessment and the torsional ultimate strength evaluation incorporating warping stress in the preliminary design stage of container ships.

To address the time-domain inversion problem of vibration responses of a simply supported rectangular plate, this paper first expresses the time-domain dynamic load at each time instant as a series of two-dimensional Legendre orthogonal polynomials, thereby transforming the complex load field into an identification problem of a finite number of coefficients. The vibration response of the thin plate is then decomposed into a linear superposition of modal displacements, and a time-domain transfer relationship between modal forces and modal displacements is established based on the modal superposition principle, thereby achieving a coupled mapping between the displacement response at any spatial point and the dynamic load at each time instant. On this basis, an iterative regularization method based on the least squares QR (LSQR) algorithm is adopted to solve the inversion problem, effectively overcoming the numerical instability caused by the ill-conditioned nature of the system. In this paper, spherical and planar distributed dynamic loads are separately inverted. The spatial and temporal distribution characteristics of the obtained dynamic loads are highly consistent with those of the original input loads, verifying the effectiveness of the proposed method. By introducing the signal-to-noise ratio (SNR) parameter, the spherical dynamic load is inverted under Gaussian white noise with an SNR of 30 dB, and the method is found to have good noise resistance and identification accuracy. In summary, the combination of two-dimensional Legendre orthogonal polynomials and the iterative regularization method has high feasibility and engineering application potential in time-domain dynamic load identification.

Sensitivity analysis and optimization of design parameters were carried out for the key region of hopper knuckle connections in a very large crude carrier (VLCC), aiming to seek the optimal layout of hopper knuckle structures that meets the requirements of the common structural rules. A workflow for yield and fatigue assessment at hopper knuckle connections was first established. The effects of hopper knuckle plate angle, arc radius at the knuckle, and plate thickness on the stress level and fatigue life at the knuckle were studied using Latin hypercube design and orthogonal arrays. On this basis, an intelligent optimization algorithm was adopted to optimize the rounded lower knuckle connection, and the optimal design scheme was obtained. The sensitivity analysis results show that for the rounded lower knuckle connection, the thickness of the inner bottom plate is the most critical factor affecting the stress level, and the arc radius is the most critical factor affecting the fatigue life. The above analysis approach and optimization strategy can provide an empirical reference for the structural optimization design of hopper knuckle connections in similar vessels.

Driven by the dual carbon goals of carbon peaking and carbon neutrality, the international shipping industry has placed higher demands on ship energy efficiency and full-scale ship performance evaluation methods, making the revision and development of relevant international standards a current research focus. This paper systematically reviews the progress of two key standards in the field of ship performance evaluation. First, it focuses on the publication and main revisions of the new version of ISO 15016:2025, Specifications for the assessment of speed and power performance by analysis of speed trial data, analyzing its core technical updates in wind and wave correction, shallow water correction, and water temperature and density correction. Second, it traces the ongoing development of the ISO 25817 standards, Evaluation of fuel consumption and propulsion performance in actual seas, which proposes a standardized method for evaluating ship performance and fuel consumption under real-world sailing conditions. By integrating onboard monitoring data with ship design data, it establishes a validation framework for ship propulsion performance covering both calm water and actual sea conditions, and specifies technical elements such as measurement requirements, data filtering and correction, and quality assessment. The analysis shows that ISO 15016:2025 introduces more refined correction methods and encourages the use of advanced sensors, which is expected to further improve the accuracy and comparability of speed trial data. ISO 25817 is dedicated to constructing an objective and verifiable system for evaluating ship energy efficiency, providing a technical pathway to achieve transparent benchmarking between design performance and operational performance. The coordinated advancement of these two standards signifies a paradigm shift in ship performance research from “experience” to “measurement” and from “delivery” to “operation”. It is recommended that future research be carried out in areas such as precise sensing of real-sea environments, standardized processing of ship monitoring data, and digital twins for full-scale ship performance.

To address the issue of ship-to-bank collision probability for large container ships under the influence of wind loads and bank effects in port channels, this paper proposes a prediction approach based on maneuverability analysis and Monte Carlo simulation. Taking a 10,000 TEU container ship navigating in the Yangshan Port channel as the research scenario, probability distribution models for ship route deviation and wind conditions are constructed using AIS (Automatic Identification System) data and meteorological records. Numerical simulation is conducted using the MMG (Maneuvering Modeling Group) model, accounting for stochastic factors such as ship speed, container stacking configuration, wind speed, wind direction, and ship-to-bank distance. Through maneuvering motion simulations, the relationship between the ship-to-bank collision probability and ship speed, as well as stacking pattern, is evaluated. Results show that maintaining 40% of the service speed significantly reduces the collision probability. The aerodynamic characteristics of stacking patterns have an important influence on the stopping and collision avoidance process. The comb-shaped stacking pattern has a higher probability of control loss but a lower final collision probability, while the empty aft deck stacking pattern shows the opposite effect. This research provides a quantitative analytical approach for identifying the environmental factors that affect the navigation safety of ships entering and leaving ports, as well as for supporting ship maneuvering decision-making.

In 2020, the IMO issued Interim Guidelines on the Second-Generation Intact Stability Criteria for Ships. To support the implementation of these criteria in the shipbuilding industry, ship design software requires time domain numerical methods of parametric roll with varying levels of accuracy and computational efficiency. First, a time-domain parametric roll mathematical model coupling heave, roll, and pitch in three degrees of freedom is developed based on two-dimensional and three-dimensional hydrodynamic methods. Second, in the two-dimensional hydrodynamic method, time-domain heave and pitch motions are obtained using a frequency-to-time domain conversion method, while in the three-dimensional hydrodynamic method, the three-dimensional time-domain mixed-source method is employed to directly solve for heave and pitch motions in the time domain. Subsequently, the Froude-Krylov moments and hydrostatic moments in the roll direction are solved along the instantaneous wet surface. Finally, using the international standard C11 container ship model, the effects of heave and pitch motions obtained from frequency-domain solutions, time-domain average wet surface solutions, and time-domain instantaneous wet surface solutions on parametric roll are compared, and the results are compared with model test data. It is verified that the three-degree-of-freedom mathematical models based on two- and three-dimensional hydrodynamic methods can quantitatively predict the time-domain motion of parametric roll in head waves.

High-speed displacement vessels can reduce resistance and increase speed by installing a reasonable bulbous bow. This paper takes the bulbous bow of a high-speed vessel as the research object. Without changing the total length of the vessel, this paper aims to optimize the hull resistance near the design speed by designing a new drag-reducing bulbous bow. First, based on the original bow lines, the lines of the bulbous bow are adjusted by altering the local characteristics of the bulbous bow. Then, using numerical calculation methods, numerical simulations of the bow wave-making, hull resistance, and hull attitude are carried out to compare and determine the optimal form of the new drag-reducing bulbous bow. Finally, based on the analysis and comparison of multiple schemes, the design parameters of such bulbous bows are summarized to provide a reference for the bulbous bow line design of high-speed displacement vessels.

To address the endurance challenge faced by autonomous underwater vehicles (AUVs) in deep-sea and offshore operations, this paper proposes an offshore mobile energy platform that uses a small waterplane area twin hull (SWATH) as its platform and integrates dual vertical-axis wind turbines (VAWTs). To evaluate the resistance characteristics and design the propulsion system of the platform, the calm-water resistance of the SWATH and the aerodynamic resistance of the VAWT are calculated respectively using computational fluid dynamics (CFD). On this basis, a propeller is designed and its open water performance is predicted. The results show that the designed propeller achieves a propulsion efficiency of over 60% at medium and high advance speeds, meeting the propulsion requirements under medium- and high-speed navigation conditions. At low advance speeds, the propeller can also provide sufficient thrust to meet the propulsion requirement of the platform at a maximum speed of 4 kn. This study performs resistance calculations and propeller design for the platform, confirming its hydrodynamic feasibility as AUV cluster charging equipment and providing data support and design references for the development of deep-sea and offshore energy supply equipment.

The extraction and analysis of ship flow field characteristics have long been a critical research direction in hull form design and ship condition analysis. However, data obtained through numerical simulation methods often remain underutilized, and traditional methods suffer from limited feature representation capabilities. This paper proposes a deep learning-based approach for analyzing the characteristics of pressure time-series data on ship surfaces. A clustering autoencoder model is constructed to perform both feature dimensionality reduction and feature classification. Using the spectral curves of pressure time-series data from propeller blade surfaces as training data, the model extracts low-dimensional features for subsequent classification. This process effectively visualizes the spatial distribution of flow field pressure time-series characteristics and accurately identifies high-frequency measurement points. The findings validate the utility of deep learning in the analysis of ship flow field characteristics and provide a methodological framework for understanding flow field characteristics induced by propeller excitation.

The interaction between waves and offshore structures is one of the central issues in marine engineering. Traditional physical model tests are limited by cost and scale effects; therefore, the development of high-precision numerical methods is crucial for elucidating the mechanisms of fluid-structure interaction. In this study, a three-dimensional numerical wave tank was constructed based on the moving particle semi-implicit (MPS) method, combined with graphics processing unit (GPU) parallel acceleration technology, enabling high-fidelity simulation of nonlinear regular waves. Through tests of damping and wave-attenuation layer parameters, the optimal damping coefficient was determined, effectively suppressing end reflections. Numerical results indicate that the tank can stably generate preset waves with good convergence; for dimensionless wave numbers less than 2, the deviation of the average wave height from the theoretical value is less than 4%, though model performance declines beyond this range. Further simulations of the floating structure's motion response showed that the time-history curves of the caisson's motion agreed well with experimental data, verifying the model's feasibility for wave-structure interaction problems. This study provides a reliable numerical method and theoretical reference for predicting the motion of floating structures under complex wave conditions.

The time-reversal method, as a wave signal processing technique, has shown great potential in the field of wave reconstruction in recent years. However, existing studies have mostly focused on using the time-reversal method to reconstruct wave trains under specific water depth conditions, without considering the influence of water depth variations on time-reversal wave train reconstruction. In this study, a high-precision two-dimensional numerical wave tank is established based on the open-source smoothed particle hydrodynamics (SPH) model DualSPHysics. Through numerical experiments of time-reversal wave train reconstruction under different water depth conditions, the influence of water depth variation on the evolution of the wave elevation and the time-frequency characteristics of the target wave train is analyzed in detail. This study not only verifies the reliability of the DualSPHysics model in simulating strongly nonlinear wave problems but also provides important data support for the engineering application of the time-reversal technique under varying water depth conditions, which is of significant importance for improving the safety assessment capability of ships and offshore structures subjected to strongly nonlinear waves.

In-situ mechanical testing of Arctic sea ice is of great significance for ensuring the safety of ice navigation and supporting the design of polar ships. During the 15th Chinese National Arctic Research Expedition, systematic in-situ sea ice mechanical tests were carried out in the high-latitude region of the central Arctic Ocean (84°57′N-89°49′N). A total of 63 in-situ borehole strength tests, one full-thickness in-situ cantilever beam flexural strength test, and 43 small-scale three-point bending strength tests were completed. Among them, the borehole strength test and the in-situ cantilever beam test were both conducted by China for the first time in the polar region. Preliminary analysis shows that the borehole strength increases with latitude, rising from 3.9 MPa at 84°57′N to 14.2 MPa at 89°49′N. The flexural strength tests reveal a certain size effect: the full-thickness flexural strength obtained from the cantilever beam test is 226 kPa, while the arithmetic mean of the small-scale three-point bending strength is 384 kPa. This paper provides valuable field data for understanding the mechanical properties of late-summer Arctic sea ice and can support the design of China’s polar engineering equipment and the assessment of navigation safety of ships in ice-covered waters.

The effect of moving slamming load represents a long-standing issue in the design of marine structures against slamming, which has not yet been fully accounted for. Extensive full-scale measurements and model tests indicate that the peak slamming pressure region can move rapidly along the structural surface, exhibiting distinct load migration characteristics. However, current structural design methods and classification society rules usually simplify slamming loads as a uniformly distributed pressure, without fully accounting for the effect of load migration on the structural dynamic response. Focusing on moving slamming loads of hull structures, this paper briefly reviews advances in relevant experimental observations, dynamic response analyses, and engineering design methods. First, the formation and engineering background of moving slamming loads are introduced. Typical research findings obtained from full-scale measurements, model tests, and hydroelastic tests are summarized, and the influence of load migration on the response characteristics of grillage structures is analyzed. Subsequently, the fundamental concept and derivation of equivalent design pressure are elaborated. The equivalent design pressure method for moving slamming loads based on the dynamic-static equivalence principle and its analytical models are highlighted. Finally, the applicability, key influencing factors, and engineering application prospects of this method are discussed. Studies show that considering the load migration effect is of great significance for reasonably evaluating the slamming response of hull structures and developing design methods that balance physical rationality and engineering practicality.

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.