J. Mar. Sci. Eng., Volume 11, Issue 8 (August 2023) – 156 articles

This is a new installment in the series of publications that describe the mathematical modeling of the Floating Hybrid Generator Systems Simulator (FHYGSYS) tool. This work presents an improved mathematical model of the turbines of the floating hybrid system—consisting of an “OC3-Hywind” wind turbine and two marine current turbines—presented by the authors in previous publications. In this third installment, the modeling of the three turbines of the floating hybrid system is described using the Blade Element Momentum (BEM) theory. This modeling allows one to replace the one based on the One-Dimensional theory used in previous installments. For the operation of modeling with BEM, it has been considered necessary to implement a continuous feedback control system. In this case, two PID (proportional–integral–derivative) controllers have been implemented in each of the turbines. The first controls the torque on the turbine generator and the second controls the collective pitch angle of the blades. The results obtained are presented and validated through a code-to-code comparison with simulations carried out with FASTv8 under the same conditions and with the operating results of marine current turbines that exist in the literature. This improvement in the mathematical model offers the possibility of implementing other types of controllers that allow for the testing of different strategies of the floating hybrid control system, with the aim of maximizing energy production while ensuring the structural stability of the floating hybrid system. Full article

This research investigates the application of photovoltaic (PV) systems on ship retrofits with the aim of reducing the emission of harmful gases. By using renewable energy resources, this research presents the potential for reducing greenhouse gas (GHG) emissions and improving energy efficiency in maritime operations, specifically within the Split coastal area. Overcoming the space restrictions on ships, an innovative design is presented to maximize the installation area for solar power. The research is conducted for several cases based on the IHOGA simulator, for all ship phases, and it aims to minimize fuel consumption by the diesel generators, thus emphasizing the use of renewable energy resources. A model with two operational modes is designed: Mode 1 allows surplus power to charge batteries or supply the port network, while Mode 2 covers power deficits from alternative sources. The implementation of renewables results in carbon dioxide (CO₂) and nitrogen oxide (NO_X) emission reductions. Furthermore, during the ship hotelling phase, the load is supplied entirely by batteries, resulting in zero emissions at the port. Full article

Underwater acoustic channels, influenced by time-varying, space-varying, frequency-varying, and multipath effects, pose significant interference challenges to underwater acoustic communication (UWAC) signals, especially in non-cooperative scenarios. The task of modulating and identifying distorted signals faces huge challenges. Although traditional modulation recognition methods can be useful in the radio field, they often prove inadequate in underwater environments. This paper introduces a modulation recognition system for recognizing UWAC signals based on higher-order cumulants and deep learning. The system achieves blind recognition of received UWAC signals even under non-cooperative conditions. Higher-order cumulants are employed due to their excellent noise resistance, enabling the differentiation of OFDM signals from PSK and FSK signals. Additionally, the high-order spectra differences among signals are utilized for the intra-class recognition of PSK and FSK signals. Both simulation and lake test results substantiate the effectiveness of the proposed method. Full article

Based on a series of physical model tests and numerical simulations, the bearing characteristics of a tripod-bucket foundation are investigated. It is found that with the decrease in aspect ratio (L/D), the rotation center of the foundation will decrease, and the displacement model change from rotation to uplift. Characteristics of earth pressure on the buckets from both finite element analysis and model tests are studied, which is used to explain the failure mechanism for tripod-bucket foundations with different L/D. A revised method is proposed to estimate the moment bearing capacity of the tripod bucket foundation under horizontal and moment load. This method is thought to be more convenient and applicable in the practice. Full article

The rapid expansion of offshore wind farms (OWFs) in European seas is accompanied by many challenges, including efficient and safe operation and maintenance, environmental protection, and biodiversity conservation. Effective decision-making for industry and environmental agencies relies on timely, multi-disciplinary marine data to assess the current state and predict the future state of the marine system. Due to high connectivity in space (land–estuarial–coastal sea), socioeconomic (multi-sectoral and cross-board), and environmental and ecological processes in sea areas containing OWFs, marine observations should be fit for purpose in relation to multiple OWF applications. This study represents an effort to map the major observation requirements (Part-I), identify observation gaps, and recommend solutions to fill those gaps (Part-II) in order to address multi-dimension challenges for the OWF industry. In Part-I, six targeted areas are selected, including OWF operation and maintenance, protection of submarine cables, wake and lee effects, transport and security, contamination, and ecological impact assessments. For each application area, key information products are identified, and integrated modeling–monitoring solutions for generating the information products are proposed based on current state-of-the-art methods. The observation requirements for these solutions, in terms of variables and spatial and temporal sampling needs, are therefore identified. Full article

Autonomous underwater vehicles (AUVs) are important in areas such as underwater scientific research and underwater resource collection. However, AUVs suffer from data portability and energy portability problems due to their physical size limitation. In this work, an acoustic guidance method for underwater docking is proposed to solve the problem of persistent underwater operation. A funnel docking station and an autonomous remotely operated vehicle (ARV) are used as the platform for designing the guidance algorithms. First, the underwater docking guidance is divided into three stages: a long-range approach stage, a mid-range adjustment stage and a short-range docking stage. Second, the relevant guidance strategy is designed for each stage to improve the docking performance. Third, a correction method based on an ultra-short baseline (USBL) system is proposed for the ARV’s estimate of the depth, relative position and orientation angle of the docking station. To verify the feasibility of the docking guidance method, in this work, tests were performed on a lake and in a shallow sea. The success rate of autonomous navigation docking on the lake was 4 out of 7. The success rate of acoustic guidance docking on the lake and in the shallow sea were 11 out of 14 and 6 out of 8, respectively. The experimental results show the effectiveness of the docking guidance method in lakes and shallow seas. Full article

The paper analyzes quasiperiodic upwellings and downwellings on the shelf and upper part of continental slope of the northeastern Black Sea. It is shown that these processes are related to changes in intensity and direction of alongshore current and the following geostrophic adjustment of the density field. The source of such changes is the meandering of the Black Sea Rim Current (RC). It leads to a quasiperiodic change in direction of the alongshore current, from northwestern (cyclonic RC meander) to southeastern (anticyclonic RC meander, or eddy). These cycles, or phases, have an average duration of about 10 days. During the northwestern phase, the permanent Black Sea pycnohalocline (hereafter pycnocline) and seasonal thermocline descend, their thickness increases, and so does the thickness of the upper mixed layer (UML). During the southeastern phase, both the pycnocline and seasonal thermocline ascend and become thinner, along with the UML, which also becomes thinner. In both phases, isopycnals in the pycnocline and isotherms in the thermocline demonstrate quasi-in-phase vertical oscillations, which have a good correlation with the speed and direction of the alongshore current. These correlations allow estimation of the magnitude of upwellings and downwellings in the shelf–slope area of the northeastern Black Sea using data series of current velocity profiles. Full article

Uncompensated motion errors can seriously affect the imaging quality of synthetic aperture sonars (SASs). In the existing line-by-line motion compensation (MOCO) algorithms for wide-beam multiple-receiver SAS systems, the approximate form of the range history error usually introduces a significant approximation error, and the residual two-dimensional (2D) range cell migration (RCM) caused by aperture-dependent motion errors is not corrected, resulting in the severe defocus of the image. In this paper, in the presence of translational and rotational errors in a multiple-receiver SAS system, the exact range history error concerning the five-degree-of-freedom (DOF) motion errors of the sway, heave, yaw, pitch, and roll under the non-stop-hop-stop case is derived. Based on this, a two-stage subaperture MOCO algorithm for wide-beam multiple-receiver SAS systems is proposed. We decompose the range history error into the beam-center term (BCT) and the residual spatial-variant term (RSVT) to compensate successively. In the first stage, the time delay and phase error caused by the BCT are compensated receiver-by-receiver through interpolation and phase multiplication in the azimuth-time domain. In the second stage, the data of a single pulse are regarded as a subaperture, and the RSVT is compensated in the subaperture range-Doppler (RD) domain. We divide the range into several blocks to correct RCM caused by the RSVT in the subaperture RD domain, and the phase error caused by the RSVT is compensated by phase multiplication. After compensation, the wide-beam RD algorithm is used for imaging. Simulated and real-data experiments verify the superiority and robustness of the proposed algorithm. Full article

Underwater acoustic target recognition methods based on time-frequency analysis have shortcomings, such as missing information on target characteristics and having a large computation volume, which leads to difficulties in improving the accuracy and immediacy of the target recognition system. In this paper, an underwater acoustic target recognition model based on a deep residual attention convolutional neural network called DRACNN is proposed, whose input is the time-domain signal of the underwater acoustic targets radiated noise. In this model, convolutional blocks with attention to the mechanisms are used to focus on and extract deep features of the target, and residual networks are used to improve the stability of the network training. On the full ShipsEar dataset, the recognition accuracy of the DRACNN model is 97.1%, which is 2.2% higher than the resnet-18 model with an approximately equal number of parameters as this model. With similar recognition accuracies, the DRACNN model parameters are 1/36th and 1/10th of the AResNet model and UTAR-Transformer model, respectively, and the floating-point operations are 1/292nd and 1/46th of the two models, respectively. Finally, the DRACNN model pre-trained on the ShipsEar dataset was migrated to the DeepShip dataset and achieved recognition accuracy of 89.2%. The experimental results illustrate that the DRACNN model has excellent generalization ability and is suitable for a micro-UATR system. Full article

Low-visibility maritime image enhancement is essential for maritime surveillance in extreme weathers. However, traditional methods merely optimize contrast while ignoring image features and color recovery, which leads to subpar enhancement outcomes. The majority of learning-based methods attempt to improve low-visibility images by only using local features extracted from convolutional layers, which significantly improves performance but still falls short of fully resolving these issues. Furthermore, the computational complexity is always sacrificed for larger receptive fields and better enhancement in CNN-based methods. In this paper, we propose a multiple-feature fusion-guided low-visibility enhancement network (MFF-Net) for real-time maritime surveillance, which extracts global and local features simultaneously to guide the reconstruction of the low-visibility image. The quantitative and visual experiments on both standard and maritime-related datasets demonstrate that our MFF-Net provides superior enhancement with noise reduction and color restoration, and has a fast computational speed. Furthermore, the object detection experiment indicates practical benefits for maritime surveillance. Full article

Gravity-aided navigation is an effective navigation method for underwater vehicles. However, the distribution of the gravity field may affect the measurement errors of gravity anomalies and the precision of gravity-aided navigation. In this paper, the upper and lower thresholds of the gravity field standard deviation are computed by the statistical properties of the local gravity field to classify each grid in the gravity map into different levels. A parallel multiple methods with adaptive decision making (PMMADM) for gravity-aided navigation is proposed which incorporates the gravity anomaly measurements, particle filtering, and maximum correlation method into the observation equation of the extended Kalman filter. The algorithm autonomously selects the observation variables in the filter by combining the gravity field standard deviation at the current position of the carrier with a decision tree. This approach can combine the characteristics of different gravity matching algorithms, reduce the impact of random noise in the measurements, and improve the positioning accuracy of gravity-aided navigation. Physical simulation experiments demonstrated that the proposed gravity matching algorithm achieves the high navigation accuracy and long-term stability in different gravity fields, and the mean value of positioning error is 620.72 m. Full article

The rapid development of convolutional neural networks has significant implications for automated underwater fishing operations. Among these, object detection algorithms based on underwater robots have become a hot topic in both academic and applied research. Due to the complexity of underwater imaging environments, many studies have employed large network structures to enhance the model’s detection accuracy. However, such models contain many parameters and consume substantial memory, making them less suitable for small devices with limited memory and computing capabilities. To address these issues, a YOLOv6-based lightweight underwater object detection model, YOLOv6-ESG, is proposed to detect seafood, such as echinus, holothurian, starfish, and scallop. First, a more lightweight backbone network is designed by rebuilding the EfficientNetv2 with a lightweight ODConv module to reduce the number of parameters and floating-point operations. Then, this study improves the neck layer using lightweight GSConv and VoVGSCSP modules to enhance the network’s ability to detect small objects. Meanwhile, to improve the detection accuracy of small underwater objects with poor image quality and low resolution, the SPD-Conv module is also integrated into the two parts of the model. Finally, the Adan optimizer is utilized to speed up model convergence and further improve detection accuracy. To address the issue of interference objects in the URPC2022 dataset, data cleaning has been conducted, followed by experiments on the cleaned dataset. The proposed model achieves 86.6% mAP while the detection speed (batch size = 1) reaches 50.66 FPS. Compared to YOLOv6, the proposed model not only maintains almost the same level of detection accuracy but also achieves faster detection speed. Moreover, the number of parameters and floating-point operations reaches the minimum levels, with reductions of 75.44% and 79.64%, respectively. These results indicate the feasibility of the proposed model in the application of underwater detection tasks. Full article

Sidewall roughness is a key factor influencing the shaft resistance of rock-socketed piles. Owing to the difficulties in onsite measuring and the inconsistency in quantitatively characterizing the roughness degree of sidewalls, existing approaches for estimating the shaft resistance of rock-socketed piles often cannot take this factor into account. Based on the measured surface curves of the 68 sockets in No. 6# and 7# group piles of the Chishi Bridge on the Ru-Chen Expressway in China, sidewall roughness is described by introducing the roughness factor (RF) based on the Horvath and Monash models, respectively, while a statistical analysis of the sidewall roughness in rock-socketed sections is also conducted. In addition, an analytical solution to the shaft resistance of rock-socketed piles with consideration of sidewall roughness and the relative settlement of the pile–rocks interface (∆s), is proposed and further compared with the field load tests. The results showed that: the RF obtained by the Horvath model is bigger than that obtained by the Monash model; the larger RF is, the bigger the mobilized shaft resistance; the analytical solution generally overestimates the mobilized shaft resistance of rock-socketed piles under the same ∆s, and the deviation is less than 15% if ∆s is larger than 3.00 mm. The Horvath model is recommended to quantitatively characterize the roughness degree of sidewalls for its good operability in practice. Full article

Upon the higher requirement on high-performance structures of large-scale supporting structures of offshore wind turbines, the systematic analysis on the compressive-flexural behavior and ultimate bearing capacity of tapered concrete-filled double skin steel tubular (CFDST) members designed by over-ranging parameters was performed. Investigating the entire-process mechanism, e.g., the moment–deformation response, stress development, interaction stress, and subassembly contribution, was based on the finite element (FE) analysis, where the moment–deformation curve can be distinguished by four characteristic points, and the transverse local buckling of outer tube partly weakens distribution height of interface pressure in compression zone compared to that in tension zone. Influences of material strengths and geometric parameters were examined by the parametric study, e.g., increasing tapered angle (ψ) slightly reduces the bearing capacity; the higher axial compression ratio (n) contributes a noteworthy action on the post-peak behavior and carrying capacity, e.g., the bearing capacities at n = 0.9 and n = 0.5 reduce by 64.14% and 18.44% compared to capacity at n = 0.1, respectively; influence of D_o/t_o ratio is more significant than D_i/t_i ratio. Subsequently, the modified cross-sectional stress integration (CSI) method was proposed to predict the moment–strain (M-ε) curves of tapered CFDST members; meanwhile, a confined concrete model with transverse confinement stress as an explicit parameter was modified, and influences of different confined concrete models on predicting M-ε curves were compared. Finally, design methods based on the modified CSI method and limit state method were proposed as a simplified calculation method to determine the correlative relationship of axial compressive strength and moment-resisting capacity (N-M curve). Full article

Underwater wireless sensor networks (UWSNs) are significantly different from terrestrial sensor networks in the following aspects: low bandwidth, high latency, variable topology, limited battery, low processing power and so on. These new features pose many challenges to the design of self-organizing routing protocol for UWSNs. This paper focuses on the application of Ad Hoc On-demand Distance Vector (AODV) routing protocol in UWSNs. In order to solve the problems of packet collision and excessive energy consumption associated with the flooding-based routing discovery method and the periodic hello packet routing maintenance mechanism of AODV, a routing discovery and maintenance method based on location and energy information is proposed, and it is referred to as the route-focusing AODV (RFAODV) routing protocol. In the RFAODV protocol, the routing discovery process is focused on a few nodes through forwarding area control and dynamic delay adjustment. In addition, feedback from a media access control layer and residual energy control are used for routing maintenance. We implement the RFAODV and evaluate its performance according to the sea trial data as parameters in the NS-2. The simulation results show that compared with the other protocols, RFAODV improves the routing discovery success ratio by at least 18%, increases the packet transmission ratio by at least 4%, reduces the protocol overhead by at least 15% and reduces the energy consumption by at least 5% under various simulation scenarios. RFAODV is suitable for large-scale, high-load and dynamic networks underwater wireless sensor networks. Full article

The environmental effects of ship propellers were not even close to fully examined before the current massive ships were introduced to sea trade. Larger ships, result in greater length, beam, draft and propulsion power. Of concern here is the under-keel clearance (UKC) and applied power, the most important parameters causing sea bottom sediment resuspension and, consequently, the transport and deposition of washed sediments. The problems are multifarious: shorelines could be contaminated with heavy metals, petroleum hydrocarbons and other organic chemicals, which are sometimes buried deep in the sediment bed. The effects of resuspension on marine life have been well documented by marine biologists. Further, a ship passing through a flow field may have a significant hydrodynamic effect on the shipping channel: waves generated by moving vessels can accelerate shoreline erosion; erosion around quay piles have a negative impact on sea flora. Waves can also affect other manoeuvring vessels or ships at berth. Available empirical models are applicable for a steady state condition, addressing velocity and, consequently, shears at the sea bottom for defined UKC and states of applied power. The idea here is to calculate material resuspension dynamically in the water column based on realistic manoeuvring conditions, which can be a matter of some complexity. During a manoeuvre, the pilot must bring the ship into or out of the harbour in the safest possible way, operating the telegraph, rudder, thrusters and possibly tugs, and also co-ordinating the work of the linesmen. The jet speed powering the vessel is not only a function of the speed of the propeller, but also of the present speed of the ship, which has an effect on the propeller’s constantly changing torque. Additionally, the bathymetry is constantly changing, and the streamlines hit not only the seabed, but also the bank and other structures of the harbour basin. The resuspended material remains in the column long after the ship has finished manoeuvring, moving slowly through the entire water column and being transported not only by the remaining streamlines of the ship but also by general currents. Realistic manoeuvring parameters can be obtained from real-time simulations with a real crew using state-of-the-art Full Mission Bridge Simulators (FMBS); eddies and the like contribute to the distribution and material resuspension and can be calculated by applying numerical modelling. In this paper, a container ship departure manoeuvre is simulated dynamically using Wartsila FMBS obtained data, which are postprocessed and coupled with the MIKE 3 FM hydrodynamic modelling application to which we add the precise port of Koper bathymetry to gain ship propeller spatial jet velocity distribution in specific time domains. Obtained jet velocity distribution is further coupled with the MIKE 3 MT particle tracking application to visualize total resuspended sediment transport patterns, etc. Container ships were selected to amplify the urgency of this phenomenon; they are the most intrusive in terms of resuspending and scouring the seabed given their powerful engines and larger propellers. Passenger ships could have been used, car carriers, or even tankers; but the fear among scientists is that the issue will not be taken seriously enough by certain stakeholders. Full article

In this paper, a variable damper is proposed to regulate the efficiency of a two-body wave energy converter (WEC) with mechanically driven power take-off (PTO). The variable damper introduces logic constraints into the WEC system, which can be translated into a mixed logical dynamical form with the dynamics of real-valued variables, the dynamics of logic variables, and their interactions. A hybrid model predictive control (MPC) method is used to determine the control inputs, which has the capacity to handle various constraints. The performance is assessed through simulations to evaluate the effectiveness of the proposed method. The achievable performance improvements of the proposed hybrid MPC are shown by means of comparative analysis with uncontrolled WEC devices. The results show that the proposed hybrid MPC has a high requirement on the lower bound of the variable damper and the maximum damping is used only at low relative velocities to achieve the optimum phase, like latching control. The hybrid MPC performs exceptionally well under wave conditions with a small significant wave height and long wave period, improving the power generation of the uncontrolled system up to 22.5%. And, the prediction error has a significant effect on hybrid MPC performance, especially for long prediction horizon. Full article

Efficiently predicting high-resolution and accurate flow fields through networked autonomous marine vehicles (AMVs) is crucial for diverse applications. Nonetheless, a research gap exists in the seamless integration of data-driven flow modeling, real-time data assimilation from flow sensing, and the optimization of AMVs’ sensing strategies, culminating in a closed-loop dynamic data-driven application system (DDDAS). This article presents a novel DDDAS that systematically integrates flow modeling, data assimilation, and adaptive flow sensing using networked AMVs. It features a hybrid data-driven flow model, uniting a neural network for trend prediction and a Gaussian process model for residual fitting. The neural network architecture is designed using knowledge extracted from historic flow data through tidal harmonic analysis, enhancing its capability in flow prediction. The Kriged ensemble transform Kalman filter is introduced to assimilate spatially correlated flow-sensing data from AMVs, enabling effective model learning and accurate spatiotemporal flow prediction, while forming the basis for optimizing AMVs’ flow-sensing paths. A receding horizon strategy is proposed to implement non-myopic optimal path planning, and a distributed strategy of implementing Monte Carlo tree search is proposed to solve the resulting large-scale tree searching-based optimization problem. Computer simulations, employing underwater gliders as sensing networks, demonstrate the effectiveness of the proposed DDDAS in predicting depth-averaged flow in nearshore ocean environments. Full article

The hydrocarbon source rocks of the marine carbonates of the Ordovician Majiagou Formation in the Ordos Basin are generally in the high-overmature stage and are, therefore, not suitable for hydrocarbon thermal simulation experiments. Their hydrocarbon generation potential and hydrocarbon generation characteristics are not clearly understood. Meanwhile, Nordic Cambrian carbonates are similar in lithology, parent material type, and sedimentary age, and are in the low evolution stage, which is suitable for hydrocarbon thermal simulation experiments. Therefore, in this study, we selected the Nordic carbonates for the gold tube thermal simulation experiment to analyze the content and geochemical characteristics of the thermal simulation products. The experimental results are also compared and analyzed with the characteristics of thermal simulation products of Pingliang Formation mud shale (contemporaneous with the Majiagou Formation) and Shanxi Formation coal (in the upper part of the Majiagou Formation), which are similar to the Majiagou Formation in the Ordos Basin. The results showed that the Nordic carbonate has different hydrocarbon production characteristics from the mud shale of the Pingliang Formation of the same parent material type, and although the hydrocarbon production yields of the two are not very different, the carbonate still produces methane at 600 °C. The hydrocarbon production yield of the Nordic carbonates is almost equivalent to that of type-II₂ kerogen, indicating that the hydrocarbon production yield is not related to lithology and only to the organic matter type; however, the Nordic carbonate can produce a large amount of H₂S. The alkane carbon isotope changes are mainly controlled by the degree of thermal evolution, showing gradual heaviness with increasing temperature. No carbon isotope sequence reversal occurred during the thermal simulation, and its distribution range is roughly the same as that of the alkane carbon isotope composition of the mud shale of the Pingliang Formation. The ethane carbon isotope composition is as heavy as −21.2‰ at the high-temperature stage, showing similar coal-type gas characteristics. The addition of calcium sulphate (CaSO₄) causes the TSR reaction to occur, which has a significant impact on the methane content under high maturity conditions, reducing its content by more than 50% at 600 °C. However, the addition of CaSO₄ increases the yield of heavy hydrocarbon gases, such as ethane, and promotes the production of C_6-14 hydrocarbons and C₁₄₊ hydrocarbons at high-temperature stages, and the addition of CaSO₄ substantially increases the yield of H₂, CO₂, and H₂S. The thermal simulation results have implications for the hydrocarbon formation mechanism of the early Paleozoic marine carbonate formation system in the stacked basins of the Ordos Basin and the Tarim Basin in China. Full article

The study of hydrodynamic characteristics of floating double-rotor horizontal axis tidal current turbines (FDHATTs) is of great significance for the development of tidal current energy. In this paper, the effect of roll motion on a FDHATT is investigated using the Computational Fluid Dynamics (CFD) method. The analysis was conducted in the CFD software STAR-CCM+ using the Reynolds-averaged Navier–Stokes method. The effects of different roll periods and tip speed ratios on the power coefficient and thrust coefficient of FDHATT were studied, and then the changes in the vorticity field and velocity field of the turbine wake were analyzed by two-dimensional cross-section and Q criterion. The study indicates that roll motion results in a maximum decrease of 30.76% in the average power coefficient and introduces fluctuations in the instantaneous load. Furthermore, roll motion significantly accelerates the recovery of wake velocity. Different combinations of roll periods and tip speed ratios lead to varying degrees of wake velocity recovery. In the optimal combination, at a position 12 times the rotor diameter downstream, roll motion can recover the wake velocity to 92% of the incoming flow velocity. This represents a 23% improvement compared to the case with no roll motion. Full article

Digital rock physics (DRP) has been widely used as an effective approach for estimating the permeability of porous media. However, conventional implementation of DRP requires the reconstruction of three-dimensional (3D) pore networks, which suffer from intensive memory and underlying uncertainties. Therefore, it is highly significant to develop an approach only based on two-dimensional (2D) cross-sections of parent samples without 3D reconstruction. In this study, we present a novel approach that combines the Kozeny–Carman equation with fractal theory to derive a bridge function that links 2D cross-sectional images and 3D pore structures of parent samples in flow equivalence. Using this bridge function, we predicted the physical properties of the parent samples, including the permeability, bulk porosity, tortuosity, and pore fractal dimension. To validate our model, we performed Lattice Boltzmann (LB) simulations on nine carbonate samples and compared the LB simulation results with our model’s predictions. We also compared our predicted results with available data on various porous materials, such as sandstone, glass beads, and carbonate, in the literature. Our findings demonstrate that without reconstructing 3D pore networks, our method provides a reliable estimation of sample permeability using 2D cross-sectional images. This approach not only simplifies the determination of sample permeability in heterogeneous porous media but also sheds new light on the inherent correlations between 2D cross-sectional information and 3D pore structures of parent samples. Moreover, the derived model may be conducible to a better understanding of flow in reservoirs during the extraction of unconventional onshore and offshore oil/gas. Full article

The structural evolution and sedimentary differentiation of the Sichuan Basin in China are complex, with intricate reservoir pore structures that significantly impact shale gas production. This study examines the complexity and heterogeneity of the microscopic pore structures in the deep marine shale reservoir in the Longmaxi Formation. Pore structure characterization techniques are used to compare deep and shallow–medium marine shales, and siliceous and silty shales. The results reveal the factors influencing pore structure and their impact on exploration and development. The key points are as follows: (1) The pore structure of deep siliceous shale is the most complex due to its diverse range of pore development patterns, pore types, and sizes. (2) The box dimension of full pore size is about 1.52 for deep marine shale and 1.46 for shallow–medium shale. Organic matter (OM) content, the degree of pore development, and inorganic mineral content all correlate positively with the complexity of the pore structure in deep marine shale, which affects the formation of high-quality reservoirs. (3) Lateral heterogeneity of pore structures shows strong regional variations in the study area. Heterogeneity is more pronounced in the deep marine shale than in the medium and shallow shale formations. OM mesopores significantly influence the overall heterogeneity of the shale pore system. The deep marine shale reservoir is situated in an area with strong regional variations. The pore structure of high-quality reservoirs is more complex than those of shallow–medium marine shales, displaying notable heterogeneity. Pore structures with fractal dimension values close to that of the shallow–medium formations (box dimensions within 1.5) offer promising targets for the exploration and development of deep marine shale gas. Full article

We propose a robust nonlinear trajectory tracking design by integrating a nonlinear model transformation, robust disturbance eliminator, and trajectory generator for unmanned surface vessels influenced by modeling uncertainties and ocean environmental disturbances. We designed this nonlinear control law to help control unmanned surface vessels following any designated sail trajectory constructed by the trajectory generator. With cubic spline interpolation, this trajectory generator can generate arbitrary smooth trajectories. Simulation results show that the proposed nonlinear robust control law has precise trajectory tracking performance and a robustness property for unmanned surface vessels under harsh ocean environmental disturbances and modeling uncertainties. Full article

Green energy is at the forefront of current policy, research, and engineering, but some of the potential fuels require either a lot of deeper research, or a lot of infrastructure before they can be implemented. In the case of hydrogen both are true. This report aims to analyse the potential of hydrogen as a future fuel source by performing a life-cycle assessment. Through this the well-to-tank phase of fuel production, and the usage phase of the system have been analysed. Models have also been created for traditional fuel systems to best compare results. The results show that hydrogen has great potential to convert marine transport to operating off green fuels when powered through low-carbon energy sources, which could reduce a huge percentage of the international community’s greenhouse gas emissions. Hydrogen produced through wind powered alkaline electrolysis produced emission data 5.25 g of CO₂ equivalent per MJ, compared to the 210 g per MJ produced by a medium efficiency diesel equivalent system, a result 40 times larger. However, with current infrastructure in most countries not utilising a great amount of green energy production, the effects of hydrogen usage could be more dangerous than current fuel sources, owing to the incredible energy requirements of hydrogen production, with even grid (UK) powered electrolysis producing an emission level of 284 g per MJ, which is an increase against standard diesel systems. From this the research concludes that without global infrastructure change, hydrogen will remain as a potential fuel rather than a common one. Full article

The permeable submerged breakwater has gained popularity in recent days due to its merits of reducing incident wave energy without negatively impacting the aesthetics of the ocean view and allowing for water exchange. However, the effect of porosity on wave nonlinearity and turbulence motion close to the water/structure interface is not well resolved in the literature. This paper presents an experimental and numerical study of regular wave propagation over a permeable submerged breakwater with a wide top width. The laboratory experiments were conducted in a wave flume and included 45 test cases. The numerical simulations were performed utilizing validated olaFoam. The results show that the nonlinearity of the waves on the permeable submerged breakwater is weak, which can effectively suppress and reduce the second harmonic waves. A large amount of turbulent kinetic energy exists at the interface between the permeable submerged breakwater and the water body, which helps to dissipate wave energy. For the wider permeable submerged breakwater in this paper, the wave dissipation capacity is greatest when the porosity is between 0.2 and 0.3, and as the length of the breakwater increases, the energy transmission coefficient decreases, and the energy dissipation coefficient increases. Better wave attenuation is achieved when the permeable submerged breakwater has a certain porosity and a large width. Full article

As the modern industry rapidly advances toward digitalization, networking, and intelligence, intelligent fault diagnosis technology has become a necessary measure to ensure the safe and stable operation of mechanical equipment and effectively avoid major disaster accidents and huge economic losses caused by mechanical equipment failure. As the “power heart” of hydraulic transmission systems, hydraulic piston pumps (HPPs) occupy an important position in aerospace, navigation, national defense, industry, and many other high-tech fields due to their high-rated pressure, compact structure, high efficiency, convenient flow regulation, and other advantages. Faults in HPPs can create serious hazards. In this paper, the research on fault recognition technology for HPPs is reviewed. Firstly, the existing fault diagnosis methods are described, and the typical fault types and mechanisms of HPPs are introduced. Then, the current research achievements regarding fault diagnosis in HPPs are summarized based on three aspects: the traditional intelligent fault diagnosis method, the modern intelligent fault diagnosis method, and the combined intelligent fault diagnosis method. Finally, the future development trend of fault identification methods for HPPs is discussed and summarized. This work provides a reference for developing intelligent, efficient, and accurate fault recognition methods for HPPs. Moreover, this review will help to increase the safety, stability, and reliability of HPPs and promote the implementation of hydraulic transmission technology in the era of intelligent operation and maintenance. Full article

The accurate forecast of algal blooms can provide helpful information for water resource management. However, the complex relationship between environmental variables and blooms makes the forecast challenging. In this study, we build a pipeline incorporating four commonly used machine learning models, Support Vector Regression (SVR), Random Forest Regression (RFR), Wavelet Analysis (WA)-Back Propagation Neural Network (BPNN) and WA-Long Short-Term Memory (LSTM), to predict chlorophyll-a in coastal waters. Two areas with distinct environmental features, the Neuse River Estuary, NC, USA—where machine learning models are applied for short-term algal bloom forecast at single stations for the first time—and the Scripps Pier, CA, USA, are selected. Applying the pipeline, we can easily switch from the NRE forecast to the Scripps Pier forecast with minimum model tuning. The pipeline successfully predicts the occurrence of algal blooms in both regions, with more robustness using WA-LSTM and WA-BPNN than SVR and RFR. The pipeline allows us to find the best results by trying different numbers of neuron hidden layers. The pipeline is easily adaptable to other coastal areas. Experience with the two study regions demonstrated that enrichment of the dataset by including dominant physical processes is necessary to improve chlorophyll prediction when applying it to other aquatic systems. Full article

To investigate the overtopping and slamming phenomena that occur in the interactions between waves and oscillating surge wave energy converters (OSWECs), a two-dimensional numerical wave flume was established using computational fluid dynamics (CFD) software Fluent by adding the momentum source terms into the original Navier–Stokes equation. Numerical convergence studies of the mesh sizes and time steps were firstly performed to ensure the sufficient accuracy of the numerical model. The variations in the wave heights along the wave propagation direction in the wave-generating area, working area, and wave-absorbing area were analyzed. The dynamics of the flap-type OSWEC were simulated using the overset mesh function embedded in Fluent. In addition, the numerical results were compared with the experimental data, and good agreements were achieved. External torque was applied to the hinge joint of the OSWEC to simulate the forces due to the power take-off (PTO) system, and the identified optimal PTO damping was compared with the numerical results based on the potential flow theory, which verified the correctness of the numerical PTO system. On this basis, nonlinear wave slamming by the sharp-eagle OSWEC was analyzed. The results show that under certain incident wave conditions, the sharp-eagle OSWEC can effectively reduce the maximum rotation angle and angular velocity compared with those of the flap-type OSWEC, and there is no overtopping that occurring for the sharp-eagle OSWEC. Furthermore, the sharp-eagle OSWEC performs better than the flap-type OSWEC. Full article

This paper presents the Simulation Management System for Operational Coastal Hydrodynamic Models, or SMS-Coastal, and its novel methodology designed to automate forecast simulations of coastal models. Its working principle features a generic framework that can be easily configured for other applications, and it was implemented with the Python programming language. The system consists of three main components: the Forcing Processor, Simulation Manager, and Data Converter, which perform operations such as the management of forecast runs and the download and conversion of external forcing data. The SMS-Coastal was tested on two model realisations using the MOHID System: SOMA, a model of the Algarve coast in Portugal, and BASIC, a model of the Cartagena Bay in Colombia. The tool proved to be generic enough to handle the different aspects of the models, being able to manage both forecast cycles. Full article

Monitoring the state of oceans and their evolution in space and time is of fundamental importance as they are severely impacted by climate change, showing an increase in temperature, acidity and stratification. The role of metrology in the marine sector is relevant for helping oceanographers consolidate measurement approaches already in place by introducing concepts like metrological traceability and measurement uncertainty. The aim of this paper is to present some examples of successful and potential applications of metrology in oceanographic research, with a focus on past and ongoing activities in the framework of joint research cooperation, which could be applied by oceanographers to consolidate the comparability of data acquired in different experimental conditions, and places and time for some essential ocean variables. Scientific cooperation in the framework of joint research projects is particularly useful for supporting measurement capabilities in marine research worldwide, and the technologies and methods developed so far represent a starting point for improvements in international monitoring networks. These techniques may be applied by laboratories and centres working in the marine sector. Applications and possible future developments will also be discussed in this paper. Full article