Journal of Marine Science and Engineering

Aiming towards a better understanding of the flow field around a fully appended Joubert BB2 submarine model, and in order to complement the experimental investigations of the wake of the hydroplanes and sail, large eddy simulation (LES) with the dynamic Smagorinsky model was conducted. Three sets of grids with a maximum grid number of up to 228 million were designed to perform the LES simulation for the Joubert BB2 under 10° yaw conditions, with a freestream Reynolds number based on the local freestream velocity and a hull length of Re_L = 2.2 × 10⁷. Comparisons of the wake of the cruciform appendage were made with experiments to verify the computational accuracy and to examine the influence of the spatial resolution. A satisfactory result was more representative of the experiments with the improvement in grid spatial resolution. The evolution characteristics of three co-rotating vortices originating from the cruciform appendage under the most refined grid arrangement are further described in detail under straight-ahead and 10° yaw conditions. The comparison results show that, in the core-flow region, the resultant velocity, vorticity magnitude, and TKE were stronger and the wake was more complicated under 10° yaw conditions. Tip vortex tracking under 10° yaw conditions exhibited significant three-dimensional characteristics as the wake developed downstream. Full article

Marine energy development has been actively promoted in recent years. Analyzing wave data in the surrounding sea areas is crucial for improving wave power generator efficiency. This study collected unidirectional wave spectrum data from the northeast coastal area of Taiwan (e.g., Pengjia Islet, Fugui Cape, and NTOU). It aimed to analyze the main wave types (i.e., wind waves, swell, or mixed sea) and their proportions during the northeast and non-northeast monsoon periods. The results indicate that swell waves dominate at the NTOU buoy, while wind waves dominate at the other two stations. In addition, the analysis of wave spectra ratios revealed a low percentage of unimodal spectra (only about 0.5%), and bimodal and unformed spectra presented as predominant. Furthermore, this study also introduced a brand new JW-J wave spectrum, which combines the JW and JONSWAP spectra to describe low-frequency (swell) and high-frequency (wind wave) systems, showing superior performance compared to the Torsethaugen spectrum. Finally, this research analyzed the JW-J spectrum parameters from the Pengjia Islet and Fugui Cape stations and applied these results to the NTOU bouy for effectively describing wind/swell wave variations. Full article

The variability of the dissolved methane content in coastal zones is an important component of the biogeochemical cycle in the marine ecosystem. The objective of this study is to investigate the seasonal variability of dissolved methane distribution in the aerobic shallow coastal zone through the example of the small bay in the northeastern Black Sea. This study is based on the direct observations carried out during a long-term monitoring program conducted in the bay from 1999 to 2016. The seasonal and inter-annual variability of the dissolved methane pattern is considered under the climatic conditions as well as under the influence of extreme flood. The seasonal range of the dissolved methane content variability in the shallow part of the northeastern Black Sea is 1–2 orders of magnitude higher compared with the areas remote from the coast. The dissolved methane content in Golubaya Bay in summer is an order of magnitude higher than the winter values. In particular, local methane maxima located near the river and stream mouths and in the central bottom part of the bay have a well-shown seasonal cycle. The extreme flood conditions observed in July 2012 resulted in high methane concentrations 2 months after the flood event, when the surface concentrations of the dissolved CH₄ exceeded the equilibrium with the atmospheric values by a factor of 400. The obtained results provide a unique opportunity to estimate the scale of the biogeochemical processes in marine coastal environments under the influence of climatic and extreme conditions. Full article

The underactuated unmanned surface vessel (USV) has been identified as a promising solution for future maritime transport. However, the challenges of precise trajectory tracking and obstacle avoidance remain unresolved for USVs. To this end, this paper models the problem of path tracking through the first-order Nomoto model in the Serret–Frenet coordinate system. A novel risk model has been developed to depict the association between USVs and obstacles based on SFC. Combined with an artificial potential field that accounts for environmental obstacles, model predictive control (MPC) based on state space is employed to achieve the optimal control sequence. The stability of the designed controller is demonstrated by means of the Lyapunov method and zero-pole analysis. Through simulation, it has been demonstrated that the controller is asymptotically stable concerning track error deviation, heading angle deviation, and heading angle speed, and its good stability and robustness in the presence of multiple risks are verified. Full article

The identification of longitudinal bending moments is a critical component in the health monitoring of ship structures. This study examines the effect of the failure of measurement points on the accuracy of bending moment identification and presents a solution using an XGboost fitting method. The impact of failure point position and quantity on strain fitting accuracy and bending moment identification was investigated by performing a four-point bending experiment in typical failure scenarios. Further numerical analysis was conducted to identify potential sources of errors in the measurement process. Additionally, several XGBoost-based fitting schemes were tested under practical conditions to provide reliable fitting suggestions. The results indicated that the XGboost strain fitting method outperforms conventional methods for removing failed measurement points, resulting in improved accuracy of identification. When the most critical failure condition occurs (i.e., the deck plate measurement points and deck stiffener measurement points fail), the XGboost method can still estimate the strain at the failure points with acceptable accuracy. These results also hold in complex load scenarios. Moreover, in the practical measurement conditions, the arrangement of measuring points includes two sections that are sufficient to support the fitting of failed measurement points by using the XGboost method. The XGboost strain fitting method exhibits promising potential in strain fitting applications. Full article

Growing interest in finding the optimal route through the arctic ocean, and sea ice concentration is also emerging as a factor to be considered. In this paper, an algorithm to calculate the sea ice concentration was developed based on the images taken during the Arctic voyage of the Korean icebreaker ARAON in July 2019. A sea ice concentration calculation program was developed using the image processing functions in open-source image processing library, called OpenCV. To develop the algorithm, parameter studies were conducted on red, green, blue (RGB) color space and hue, saturation, value (HSV) color space, and k-means clustering. To verify the algorithm for sea ice concentration calculation, it was applied to images taken during Araon’s Arctic voyages. Lens curvature and view point were corrected through camera calibration. To improve the accuracy of sea ice concentration calculation, a binarization model based on random forest was proposed. A parameter study for training image numbers and tree numbers was conducted to establish the random forest model. The calculated sea ice concentrations by random forest and k-means clustering were compared and discussed. Full article

With the increasingly maturing technology of unmanned surface vehicles (USVs), their applications are becoming more and more widespread. In order to meet operational requirements in complex scenarios, the real-time interaction and linkage of a large amount of information is required between USVs, between USVs and mother ships, and between USVs and shore-based monitoring systems. Visual images are the main perceptual information gathered from USVs, and their efficient transmission and recognition directly affect the real-time performance of information exchange. However, poor maritime communication signals, strong channel interference, and low bandwidth pose great challenges to efficient image transmission. Traditional image transmission methods have difficulty meeting the real-time and image quality requirements of visual image transmissions from USVs. Therefore, this paper proposes an efficient method for visual image transmission from USVs based on semantic communication. A self-encoder network for semantic encoding which compresses the image into low-dimensional latent semantics through the encoding end, thereby preserving semantic information while greatly reducing the amount of data transmitted, is designed. On the other hand, a generative adversarial network is designed for semantic decoding. The decoding end decodes and reconstructs high-quality images from the semantic information transmitted through the channel, thereby improving the efficiency of image transmission. The experimental results show that the performance of the algorithm is significantly superior to traditional image transmission methods, achieving the best image quality while transmitting the minimum amount of data. Compared with the typical BPG algorithm, when the compression ratio of the proposed algorithm is 51.6% of that of the BPG algorithm, the PSNR and SSIM values are 7.6% and 5.7% higher than the BPG algorithm, respectively. And the average total time of the proposed algorithm is only 59.4% of that of the BPG algorithm. Full article

The Underwater Internet of Things (IoUT) shows significant future potential in enabling a smart ocean. Underwater sensor network (UWSN) is a major form of IoUT, but it faces the problem of reliable data collection. To address these issues, this paper considers the use of the autonomous underwater vehicles (AUV) as mobile collectors to build reliable collection systems, while the value of information (VoI) is used as the primary measure of information quality. This paper first builds a realistic model to characterize the behavior of sensor nodes and the AUV together with challenging environments. Then, improved deep reinforcement learning (DRL) is used to dynamically plan the AUV’s navigation route by jointly considering the location of nodes, the data value of nodes, and the status of the AUV to maximize the data collection efficiency of the AUV. The results of the simulation show the dynamic data collection scheme is superior to the traditional path planning scheme, which only considers the node location, and greatly improves the efficiency of AUV data collection. Full article

Centrifugal pumps are susceptible to various faults, particularly under challenging conditions such as high pressure. Swift and accurate fault diagnosis is crucial for enhancing the reliability and safety of mechanical equipment. However, monitoring data under fault conditions in centrifugal pumps are limited. This study employed an experimental approach to gather original monitoring data (vibration signal data) across various fault types. We introduce a multi−scale sensing Convolutional Neural Network (MS−1D−CNN) model for diagnosing faults in centrifugal pumps. The network structure is further optimized by examining the impact of various hyperparameters on its performance. Subsequently, the model’s efficacy in diagnosing centrifugal pump faults has been comprehensively validated using experimental data. The results demonstrate that, under both single and multiple operating conditions, the model not only reduces reliance on manual intervention but also improves the accuracy of fault diagnosis. Full article

To reduce the structural vibration of the duct structure in pump-jet propulsors (PJPs) and lower the induced vibration noise, this study learned from the “processor box” in an aero-engine and set a certain number of axial slots in the PJP. First, using finite element analysis, both dry and wet modes of the PJP ducts with and without an axial slot structure were simulated for analysis. Next, with the two-way fluid–solid coupling calculation method, the vibration performances of the PJP ducts with and without axial slots were contrasted and studied. The differences in calculation results under different duct structures were compared from three aspects—the vibration displacement, velocity, and acceleration of the duct and mesh nodes. According to the present results, after the addition of axial slots, the vibration displacement, the vibration velocity, and the vibration acceleration can be significantly reduced, especially in the back segment of the duct. Meanwhile, it can be concluded that it is quite important to select vibration acceleration for structural analysis in evaluating the PJP vibration. This study can provide a reference for further designs of low-noise PJPs. Full article

Renewable energy generation is increasingly important due to serious energy issues. A Doubly Salient Permanent Magnet Generator (DSPMG) can be an interesting candidate for tidal stream renewable energy systems. However, the special structure makes the system nonlinear and strongly coupled even after Park transformation and involves a larger torque ripple. Previous research mainly focused on model-based control for this machine, which is very sensitive to the parameters. Thus, to control the complex systems stably and accurately, two model-free control algorithms, Active Disturbance Rejection-Based Iterative Learning Control (ADRILC) and Active Disturbance Rejection Control–Iterative Learning Control (ADRC–ILC), are proposed for the current and speed control loops of a DSPMG-based Tidal Stream Turbine (TST), respectively. ADRC–ILC uses ADRC to deal with the external non-periodic speed ripple and adopts ILC to reduce the internal periodic speed ripple. ADRILC employs an iterative method to improve the ESO for the enhancement of the convergence rate of ILC. Considering the variable tidal speed, when the speed is above the rated value, Maximum Power Point Tracking (MPPT) must be changed to a power limitation strategy for limiting the generator power to the rated value and extending the system operating range. Thus, Optimal Tip Speed Ratio (OTSR)-based MPPT (for a low tidal current speed) and Leading Angle Flux-Weakening Control (LAFWC) (for a high tidal current speed) strategies are also proposed. According to the simulation results, the proposed ADRC–ILC + ADRILC has the lowest torque ripple, the highest control accuracy, as well as a good current tracking capability and strong robustness. At the rated speed, the proposed method reduces the torque ripple by more than 20% and the speed error by about 80% compared with PI control: the current difference is limited in 2A. The LAFWC proposed for an excessive tidal current speed is effective in conserving the electromagnetic power and increasing the generator speed. Full article

This article presents an in-depth study of CO₂ injection monitoring in the Sleipner Field, focusing on the Utsira Formation. The research leverages advanced time-lapse inversion techniques and 4D seismic data analysis to enhance the accuracy of volume estimations and provide a comprehensive understanding of the dynamic behavior of the injected CO₂ plume. The analysis encompasses cross correlation, time shift, predictability, and other key elements to yield robust insights into the reservoir’s response to CO₂ injection. Cross-correlation analysis results of 60% to 100% outside the injection zone and less than 50% within the injection zone reveal a distinct dissimilarity between the injection and non-injection zones, emphasizing phase, time, and frequency content changes due to CO₂ injection. Time shifts are meticulously calibrated globally on a trace-by-trace basis, to account for shallow statics and velocity changes, improving the overall alignment of seismic data. Predictability analysis results of 0 to 0.34 within the injection zone and 0.45 to 0.96 at the background further reinforce the findings, highlighting high predictability values in areas untouched by production and markedly lower values within the injection zone. These results provide a measure of the reliability of the seismic data and its ability to reflect the subtle changes occurring in the reservoir. Crucially, the time-lapse inversion process excels in capturing the evolving state of the CO₂ plume within the Utsira Formation. The seismic data reveals the migration and expansion of the plume over time and the dynamic nature of the reservoir’s response to CO₂ injection. Integrating various data facets reduces non-uniqueness in inversion results, allowing for more precise volume estimations. Full article

Floating offshore wind farm control via real-time turbine repositioning has a potential in significantly enhancing the wind farm efficiency. Although the wind farm power capture increase by moving platforms with aerodynamic force has been verified in a recent study, the investigation and mitigation of the fatigue damage caused by such aerodynamic force manipulated for turbine repositioning is still necessary. To respond to these needs, this paper presents fatigue load controller design for a semisubmersible floating offshore wind turbine, particularly when the turbine position is controlled by the nacelle yaw angle. At various turbine positions determined by nacelle yaw angles and average wind speeds, the designed controller manipulates three blade pitch angles individually and minimizes the fatigue load at the tower base. As the individual blade pitch controller, the linear quadratic regulator is optimized through surrogate optimization by simulating the turbine disturbed by various turbulent wind and irregular wave profiles, and then by searching for a minimum fatigue from these simulations. Fatigue load analysis with the optimized controller leads to the main contribution of this paper, that is, to demonstrate that turbine repositioning can be achieved while allowing for the inclusion of a fatigue reducing controller. In fact, when operating the FOWT with the position controller and fatigue load controller, the fatigue damage at the tower base is reduced by about 40% for different nacelle yaw angles. This result supports the feasibility of position-controlled wind turbines to optimize the wind farm efficiency, thereby drastically reducing the offshore wind energy cost. Full article

Similar to other industries, the maritime industry is also facing increasing restrictions on ships regarding pollution control. The research presented in this paper is aimed at studying the pros and cons of alternative fuels followed by a detailed analysis on hydrogen fuel cells (PEMFC) for a particular ship operating in Lisbon, Portugal. Dynamic forces acting on the ship have been studied for a year. Assessing various scenarios based on these results aids ship operators in making informed decisions regarding the future course of action for their existing vessels. These different cases are first: business as usual (diesel engine), second: replacing the diesel engine with a hydrogen hybrid system and, third: replacement of the ship with a new hydrogen hybrid ship. The study is based on the simulation of numerical equations and CFD simulation results. As the result, the second scenario is best suited in both aspects; namely, environmental and economic. Full article

It is of great importance to simultaneously stabilize output power and suppress platform motion and fatigue loads in floating offshore wind turbine control systems. In this paper, a novel composite blade pitch control scheme considering actuator fault is proposed based on an augmented linear quadratic regulator (LQR), a fuzzy proportional integral (PI) and an adaptive second-order sliding-mode observer. Collective pitch control was achieved via the fuzzy PI, while individual pitch control was based on the augmented LQR. In the case of actuator fault, an adaptive second-order sliding-mode observer was constructed to effectively eliminate the need for the upper bound of unknown fault derivatives and suppress the chattering effect. This paper conducted co-simulations based on FAST (Fatigue, Aerodynamics, Structures, and Turbulence) and MATLAB/Simulink to verify the effectiveness and superiority of the proposed scheme under different environmental conditions. It is shown that platform roll was reduced by approximately 54% compared to that under PI control. For the tower fore–aft moment, load reductions of 45% or more were achievable. The proposed scheme can greatly reduce the pitch and roll of the floating platform and loads in the windward direction of the wind turbine. Full article

Multi-vehicle collaborative mapping proves more efficient in constructing maps in unfamiliar underwater environments in comparison to single-vehicle methods. One of the pivotal hurdles of Simultaneous Localization and Mapping (SLAM) with multiple underwater vehicles is map registration. Due to the inadequate characteristics of the underwater grid maps, matching map features poses a challenge, and outliers between maps add to the complexity. We propose an algorithm to solve this problem. This approach employs the Gaussian Mixture Robust Branch and Bound (GMRBnB) algorithm with an interior point filtering technique. Feature point extraction, registration using the GMRBnB algorithm, inlier extraction based on density, and registration of the inlier are performed to obtain a more precise transformation matrix. The results of the simulation and experiments demonstrate that this technique heightens outlier tolerance and reinforces map registration accuracy. The proposed approach surpasses Iterative Closest Point (ICP) and Normal Distributions Transform (NDT) methods with respect to map registration quality. Full article

For the massive quantities and negative impacts of dredged mud slurry, its disposal and utilization have become one of the most noticeable issues in the world. In this paper, the flocculation-solidification-high pressure filtration combined method is proposed to effectively dispose of marine mud slurries. The advantages of this method are demonstrated herein in the following three aspects: dewatering performance, material savings, and the shear strength of the treated marine mud slurry. Then, the effects of the anionic polyacrylamide (APAM) dose, composite solidification agent dose, initial water content of marine mud slurries, and initial thickness of geo-bags on the mechanical properties of the marine mud slurry treated by the flocculation-solidification-high pressure filtration combined method are studied. Experimental results show that with increasing doses of APAM, the structures of mud slurries become more stable, and the optimal dose of APAM is determined as 0.16%. Moreover, the increase in the composite solidification agent dose and initial water content of the marine mud slurry, and the decrease in the initial thickness of geo-bags both contribute to the increase in the shear strength of the marine mud slurry treated by the flocculation-solidification-high pressure filtration combined method. Full article

Ocean-crossing ship structures continuously suffer from wave-induced loads when sailing at sea. The encountered wave loads cause significant variations in ship structural stresses, leading to accumulated fatigue damage. Where large inherent uncertainties still exist, it is now common to use spectral methods for direct fatigue calculation when evaluating ship fatigue. This paper investigates the use of a machine learning technique to establish a model for 2800TEU container vessel fatigue assessment. Measurement data from 3 years of cross-Atlantic sailing demonstrated and validated the machine learning model. In this investigation, the ship’s motions were used as inputs to build a machine learning model. The fatigue damage amounts predicted using a machine learning model were compared with those obtained from full-scale measurements and direct fatigue calculation. The pros and cons of the methods are compared in terms of their capability, robustness, and prediction accuracy. Full article

Nowadays, semantic segmentation is used increasingly often in exploration by underwater robots. For example, it is used in autonomous navigation so that the robot can recognise the elements of its environment during the mission to avoid collisions. Other applications include the search for archaeological artefacts, the inspection of underwater structures or in species monitoring. Therefore, it is necessary to improve the performance in these tasks as much as possible. To this end, we compare some methods for image quality improvement and data augmentation and test whether higher performance metrics can be achieved with both strategies. The experiments are performed with the SegNet implementation and the SUIM dataset with eight common underwater classes to compare the obtained results with the already known ones. The results obtained with both strategies show that they are beneficial and lead to better performance results by achieving a mean IoU of 56% and an increased overall accuracy of 81.8%. The result for the individual classes shows that there are five classes with an IoU value close to 60% and only one class with an IoU value less than 30%, which is a more reliable result and is easier to use in real contexts. Full article