Welded joints can be divided into different material zones, with considerable variation of material properties around the weld toe. The material inhomogeneity influences the local stress and strain of welded joints under monotonic and cyclic loading. This study aims to examine the local stress and strain characteristics of welded joints considering material inhomogeneity. Numerical models with various material zones were developed, and material properties were determined based on hardness. Smooth specimen models were used to analyze stress and strain distributions excluding notch effects. A detailed inhomogeneous model of a welded joint was established based on extensive microhardness measurements around the weld toe and the Kriging interpolation method. Additionally, a homogeneous model and a simplified inhomogeneous model, based on limited measured data, were generated and compared with the detailed inhomogeneous model. Fatigue life was estimated using the Smith, Watson, and Topper method based on the obtained stress and strain. For smooth specimen models, stress concentration occurs at a location where the strain is not significant, and fatigue cracks were most likely to initiate from the base metal. Results from the two simplified models showed deviations from those of the detailed inhomogeneous model, and the limitations of these simplified models are discussed.

This article provides a method by which the scour depth and scour width below pipelines, and the scour depth around single vertical piles as well as the time scales of scour for both structures due to bichromatic and bidirectional waves are calculated. The scour and time scale formulae summarized by Sumer and Freds?e (2002) as well as the bottom shear stress formulae under bichromatic and bidirectional waves by Myrhaug et al. (2023) are used. Results for unidirectional bichromatic waves and symmetrically bidirectional monochromatic waves are provided, showing qualitative agreement with what is expected physically. Qualitative comparisons are made with the data from Schendel et al.’s (2020) small scale laboratory tests on scour around a monopile induced by directionally spread waves. Applications to related cases for pipelines are also suggested. In order to conclude regarding the validity of the method for pipelines and vertical piles, it is required to compare with data in its validity range.

The current study examines damage detection in fluid-conveying pipes supported on a Pasternak foundation. This study proposes a novel method that uses the matching pursuit (MP) algorithm for damage detection. The governing equations of motion for the pipe are derived using Hamilton’s principle. The finite element method, combined with the Galerkin approach, is employed to obtain the mass, damping, and stiffness matrices. To identify damage locations through pipe mode-shape decomposition, an index called the “matching pursuit residual” is introduced as a novel contribution of this study. The proposed method facilitates damage detection at various levels and locations under different boundary conditions. The findings demonstrate that the MP residual damage index can accurately localize damage in the pipes. Furthermore, the results of the numerical and experimental tests showcase the efficiency of the proposed method, highlighting that the MP signal approximation algorithm effectively detects damage in structures.

Through active manipulation of wavelengths, a structure exposed to a water-wave field can achieve a target hydrodynamic performance. Based on the form invariance of the governing equation for shallow water waves, wavelength modulators have been proposed using the space transformation method, which enables wavelength manipulation by distributing an anisotropic medium that incorporates water depth and gravitational acceleration within the modulation space. First, annular wavelength modulators were designed using the space transformation method to reduce or amplify the wavelength of shallow water waves. The control method of wavelength scaling ratios was investigated. In addition to plane waves, the wavelength modulator was applied to manipulate the wavelength of cylindrical waves. Furthermore, the interactions between a vertical cylinder and modulated water waves were studied. Results indicate that the wavelength can be arbitrarily reduced or amplified by adjusting the dimensional parameters of the modulator. Additionally, the modulator is effective for plane waves and cylindrical waves. This wavelength modulator can enable the structure to achieve the desired scattering characteristics at the target wavelength.

This study investigates the effect of nacelle motions on the rotor performance and drivetrain dynamics of floating offshore wind turbines (FOWTs) through fully coupled aero–hydro–elastic–servo–mooring simulations. Using the National Renewable Energy Laboratory 5 MW monopile-supported offshore wind turbine and the OC4 DeepCwind semisubmersible wind turbine as case studies, the research addresses the complex dynamic responses resulting from the interaction among wind, waves, and turbine structures. Detailed multi-body dynamics models of wind turbines, including drivetrain components, are created within the SIMPACK framework. Meanwhile, the mooring system is modeled using a lumped-mass method. Various operational conditions are simulated through five wind–wave load cases. Results demonstrate that nacelle motions significantly influence rotor speed, thrust, torque, and power output, as well as the dynamic loads on drivetrain components. These findings highlight the need for advanced simulation techniques for the design and optimization of FOWTs to ensure reliable performance and longevity.

Marine current energy conversion with turbines is a growing field of interest owing to its high energy density and predictability. For wind energy, three-bladed horizontal-axis turbines are the most common because of their high power capture. Forces on blades are considerably higher in marine currents, presenting challenges to turbine design. Current research focuses on blade optimization and the selection of reliable transmission systems, and data from experiments conducted in natural environments are lacking. This paper focuses on a five-bladed vertical axis marine current turbine with a direct drive generator especially designed for low rotational speed and presents data from real-world experiments and 3D simulation models. The paper specifically investigates the influence of blade pitch angle on power capture. Experiments have been conducted at 1.42 m/s with a turbine in a river for blade pitch angles of 0° and +3° (the angle is defined as the leading edge of the blade rotating outward, perpendicular to, and opposite of the turbine axis). Two numerical 3D models, namely a vortex model and an actuator line model, have been used to simulate the turbine under the same conditions (1.42 m/s and 0°, +3°). The experimental and simulation results show that a 0° pitch angle gives a higher power capture power than a +3° pitch angle. In addition, simulation models were used to simulate the performance for an extended range at pitch angles of -3° to +3°, a fixed tip-speed ratio, and a step size of 1°. The simulations show that +1° gives the highest power coefficient and increases the average power capture by up to 0.6%. The performance of vertical axis marine current turbines can be improved by increasing the pitch angle to 1° in the positive direction. By contrast, a negative pitch angle can increase the average power capture of wind turbines.

Water depth significantly affects ship resistance, which, in turn, influences fuel consumption. Furthermore, the urgent need to reduce carbon emissions for environmental sustainability highlights the importance of applying drag reduction methods to shallow-water vehicles. To effectively employ these methods, the initial step entails an in-depth investigation of how shallow water impacts the resistance and flow dynamics of a mini-bulk carrier. This study extensively analyzes the hydrodynamic characteristics of mini-bulk carriers, focusing on the impact of shallow water on resistance and flow dynamics utilizing a combination of experimental tests and numerical analyses. This study emphasizes the interaction between the hull and the shallow seabed. This study also highlights increased frictional drag and significant residual resistance by analyzing the total resistance at various speeds in shallow waters. The results of five key factors influencing resistance in shallow waters, namely, boundary layer thickness, shear stress, velocity and pressure, turbulence, and waves, are discussed. A decrease in water depth accelerates the flow under the hull, increasing shear stress and resistance. The accelerated flow reduces the gap between the hull and the shallow seabed, elevating water pressure and increasing sinkage and resistance. Heightened turbulence in shallow water intensifies Reynolds stress, augmenting friction and viscous resistance.

The coupling effect between ship motion and liquid sloshing in a beam sea is investigated, with a focus on the influence of liquid types, namely, water and liquefied natural gas (LNG), on the coupling dynamics. A hybrid numerical model, combining a potential flow model and computational fluid dynamics methods, is employed to simulate these interactions. Numerical validation is performed using experimental data from water sloshing tests. Comparisons between water and LNG reveal that liquid type has minimal effects on the ship’s roll motion response in a beam sea, provided the total liquid masses are the same. Regarding sloshing impact pressure, although differences between LNG and water results are minor, substituting LNG with water in physical experiments is shown to yield reliable results.

Accurate and robust detection of wax appearance (a medium- to high-molecular-weight component of crude oil) is crucial for the efficient operation of hydrocarbon transportation. The wax appearance temperature (WAT) is the lowest temperature at which the wax begins to form. When crude oil cools to its WAT, wax crystals precipitate, forming deposits on pipelines as the solubility limit is reached. Therefore, WAT is a crucial quality assurance parameter, especially when dealing with modern fuel oil blends. In this study, we use machine learning via MATLAB’s Bioinformatics Toolbox to predict the WAT of marine fuel samples by correlating near-infrared spectral data with laboratory-measured values. The dataset provided by Intertek PLC—a total quality assurance provider of inspection, testing, and certification services—includes industrial data that is imbalanced, with a higher proportion of high-WAT samples compared to low-WAT samples. The objective is to predict marine fuel oil blends with unusually high WAT values (>35℃) without relying on time-consuming and irregular laboratory-based measurements. The results demonstrate that the developed model, based on the one-class support vector machine (OCSVM) algorithm, achieved a Recall of 96, accurately predicting 96% of fuel samples with WAT >35℃. For standard binary classification, the Recall was 85.7. The trained OCSVM model is expected to facilitate rapid and well-informed decision-making for logistics and storage when choosing fuel oils.

Dynamic positioning systems (DPS) on marine vessels exhibit actuator redundancy, with more actuators than degrees of freedom. A control allocation unit is employed to address this redundancy. Practical systems often feature time-varying elements in the effectiveness matrix due to factors such as changing operating conditions, nonlinearity, and disturbances. Additionally, not all thrusters require engagement at each step to counteract disturbances and maintain position. Control efforts can be generated by selecting some thrusters based on their instant effectiveness, while others can remain on standby. Therefore, introducing a control allocation method that calculates the effectiveness matrix online and selects the most efficient thrusters could be effective. This paper introduces a fault-tolerant control allocation strategy for DPS with a varying effectiveness matrix. Specifically, the investigation focuses on a case study featuring eight azimuth thrusters used on a drilling rig. At each time step, the effective matrix is calculated online, followed by the selection of the four most effective thrusters based on the actuator effectiveness index, with the four serving as backups in case of a fault. The proposed strategy has been validated through simulation results, demonstrating advantages such as robustness against changes in the effectiveness matrix and reduced energy usage by the thrusters.

As a vessel navigates at high speeds in waves, considerable pitching motion can result in the discomfort of passengers. In this study is proposed a ride control system consisting of dual T-foils to generate a larger righting moment than a common single T-foil system. One T-foil is mounted at the bow, and the other at the stern. Accordingly, different control strategies for dual T-foils were proposed To verify the stratigies, a model experiment was conducted in the Towing Tank, Dalian Unievrsity of Technology. The optimal control signal was determined by comparing the pitch responses, heave responses, bow accelerations, and stern accelerations of a vessel in regular waves. In addition, the control strategy for the best motion-reduction effect was investigated. The optimized dual T-foil system provides a 34% reduction in pitch motion.

The rudder mechanism of the X-rudder autonomous underwater cehicle (AUV) is relatively complex, and fault diagnosis capability is an important guarantee for its task execution in complex underwater environments. However, traditional fault diagnosis methods currently rely on prior knowledge and expert experience, and lack accuracy. In order to improve the autonomy and accuracy of fault diagnosis methods, and overcome the shortcomings of traditional algorithms, this paper proposes an X-steering AUV fault diagnosis model based on the deep reinforcement learning deep Q network (DQN) algorithm, which can learn the relationship between state data and fault types, map raw residual data to corresponding fault patterns, and achieve end-to-end mapping. In addition, to solve the problem of few X-steering fault sample data, Dropout technology is introduced during the model training phase to improve the performance of the DQN algorithm. Experimental results show that the proposed model has improved the convergence speed and comprehensive performance indicators compared to the unimproved DQN algorithm, with precision, recall, F_1-score, and accuracy reaching up to 100%, 98.07%, 99.02%, and 98.50% respectively, and the model’s accuracy is higher than other machine learning algorithms like back propagation, support vector machine.

To address low detection accuracy in near-coastal vessel target detection under complex conditions, a novel near-coastal vessel detection model based on an improved YOLOv7 architecture is proposed in this paper. The attention mechanism Coordinate Attention is used to improve channel attention weight and enhance a network’s ability to extract small target features. In the enhanced feature extraction network, the lightweight convolution algorithm Grouped Spatial Convolution is used to replace MPConv to reduce model calculation costs. EIoU Loss is used to replace the regression frame loss function in YOLOv7 to reduce the probability of missed and false detection. The performance of the improved model was verified using an enhanced dataset obtained through rainy and foggy weather simulation. Experiments were conducted on the datasets before and after the enhancement. The improved model achieved a mean average precision (mAP) of 97.45% on the original dataset, and the number of parameters was reduced by 2%. On the enhanced dataset, the mAP of the improved model reached 88.08%. Compared with seven target detection models, such as Faster R-CNN, YOLOv3, YOLOv4, YOLOv5, YOLOv7, YOLOv8-n, and YOLOv8-s, the improved model can effectively reduce the missed and false detection rates and improve target detection accuracy. The improved model not only accurately detects vessels in complex weather environments but also outperforms other methods on original and enhanced SeaShip datasets. This finding shows that the improved model can achieve near-coastal vessel target detection in multiple environments, laying the foundation for vessel path planning and automatic obstacle avoidance.

This study introduces an enhanced adaptive fractional-order nonsingular terminal sliding mode controller (AFONTSMC) tailored for stabilizing a fully submerged hydrofoil craft (FSHC) under external disturbances, model uncertainties, and actuator saturation. A novel nonlinear disturbance observer modified by fractional-order calculus is proposed for flexible and less conservative estimation of lumped disturbances. An enhanced adaptive fractional-order nonsingular sliding mode scheme augmented by disturbance estimation is also introduced to improve disturbance rejection. This controller design only necessitates surpassing the estimation error rather than adhering strictly to the disturbance upper bound. Additionally, an adaptive fast-reaching law with a hyperbolic tangent function is incorporated to enhance the responsiveness and convergence rates of the controller, thereby reducing chattering. Furthermore, an auxiliary actuator compensator is developed to address saturation effects. The resultant closed system of the FSHC with the designed controller is globally asymptotically stable.

Near-field underwater explosions can cause substantial damage to offshore ship structures, presenting considerable risks to their integrity. This study focused on rapidly predicting girder structure deformation in ship hulls subjected to near-field explosions from small equivalent-weight spherical charges underwater. The Runge–Kutta discontinuous Galerkin method (RKDG) was employed to calculate the explosive load generated by the spherical charge. This load was then applied to the nonlinear finite element solver software, ABAQUS, to determine the maximum deformation of the ship hull girder structure under the impulse load. By comparing the results with experimental data, the accuracy of the proposed model was validated, confirming that the RKDG finite element coupling calculation effectively simulates the response characteristics of spherical charges in near-field explosion scenarios. Subsequently, two machine learning algorithms driven by data, namely extreme gradient boosting (XGBoost) and random forest (RF), were employed to dynamically predict the maximum girder structure deformation in ship hulls. The analysis demonstrated that both models successfully predicted the maximum deformation. The root mean square error for the XGBoost model (27.67) was lower than that of the RF model (50.31). The XGBoost model also fitted 96% of the training data, compared to 94% for the RF model. Moreover, the relative error of the XGBoost model (6.25%) was lower than that of the RF model (10.38%). Overall, XGBoost is highly suitable for predicting girder structure deformation in ship hulls subjected to underwater explosions.

Frequent flood disasters caused by climate change may lead to tremendous economic and human losses along inland waterways. Emergency response and rescue vessels (ERRVs) play an essential role in minimizing losses and protecting lives and property. However, the path planning of ERRVs has mainly depended on expert experiences instead of rational decision making. This paper proposes an improved artificial potential field (APF) algorithm to optimize the shortest path for ERRVs in the rescue process. To verify the feasibility of the proposed model, eight tests were carried out in two water areas of the Yangtze River. The results showed that the improved APF algorithm was efficient with fewer iterations and that the response time of path planning was reduced to around eight seconds. The improved APF algorithm performed better in the ERRV’s goal achievement, compared with the traditional algorithm. The path planning method for ERRVs proposed in this paper has theoretical and practical value in flood relief. It can be applied in the emergency management of ERRVs to accelerate flood management efficiency and improve capacity to prevent, mitigate, and relieve flood disasters.