The rapid global expansion of offshore wind energy (OWE) has established it as a critical component of the renewable energy transition; however, this development concurrently introduces significant underwater noise pollution into marine ecosystems. This paper provides a comprehensive review of the acoustic footprint of OWE across its entire lifecycle, rigorously distinguishing between the high-intensity, acute impulsive noise generated during pile-driving construction and the chronic, low-frequency continuous noise associated with decades-long turbine operation. We critically evaluate the engineering capabilities and limitations of current underwater acoustic monitoring architectures, including buoy-based real-time monitoring nodes, cabled high-bandwidth systems (e.g., cabled hydrophone arrays with DAQ/DSP and fiber-optic distributed acoustic sensing, DAS), and autonomous seabed archival recorders (PAM deployment). Furthermore, documented biological impacts are synthesized across diverse taxa, ranging from auditory masking and threshold shifts in marine mammals to the often-overlooked sensitivity of invertebrates and fish to particle motion—a key metric frequently missing from standard pressure-based assessments. Our analysis identifies a fundamental gap in current governance paradigms, which disproportionately prioritize the mitigation of short-term acute impacts while neglecting the cumulative ecological risks of long-term operational noise. This review synthesizes recent evidence on chronic operational noise and outlines a conceptual pathway from event-based compliance monitoring toward long-term, adaptive soundscape management. We propose the implementation of integrated, adaptive acoustic monitoring networks capable of quantifying cumulative noise exposure and informing real-time mitigation strategies. Such a paradigm shift is essential for optimizing mitigation technologies and ensuring the sustainable coexistence of marine renewable energy development and marine biodiversity.
3D point clouds are a crucial data format for accurately capturing geometric information in large-scale industrial environments such as shipyards. Deep learning-based object detection technology using 3D point clouds enables automated production management and process optimization. However, the large volume characteristic of 3D point clouds remains a challenge due to the resources and time required for data processing and dataset construction. The large volume of 3D point clouds leads to excessive computational costs, storage demands, and time consumption during dataset construction and training. Therefore, it is necessary to appropriately reduce the dataset size for efficient utilization while ensuring object detection performance. This necessitates a study on dataset downsampling strategies that maintain optimal density and detection accuracy. In this study, an experimental dataset similar to the S3DIS (Stanford Large-Scale 3D Indoor Spaces) dataset was constructed. The density of the 3D point clouds was adjusted in five levels by reducing points per unit area by 20% increments. These datasets were applied to a deep learning architecture to analyze object detection accuracy. Subsequently, the findings were applied to a shipyard dataset to streamline large volume point clouds and evaluate detection performance, thereby assessing their practical applicability. The results demonstrated that reducing the experimental dataset density to approximately 20% still maintained object detection accuracy of around 95% IoU for key objects. This indicates that lightweight datasets can reduce processing resources and costs while preserving detection performance. Additionally, applying the approach to real shipyard datasets revealed that object detection was feasible with reduced data (approximately 4.6% of the raw data). This study provides a practical framework for constructing efficient deep learning models for object detection by downsampling datasets in large-scale industrial environments like shipyards. It is expected to contribute to the establishment of automated data management systems for production management and process efficiency enhancement. Further analysis is required to evaluate performance at extreme low densities (below 20%). Moreover, while this study employed simple downsampling techniques, future work should explore the performance of various downsampling methods to optimize detection accuracy.
ObjectiveIn order to explore the characteristics of optimization design schemes of titanium alloy double layer stiffened cylindrical shell structures under different length-to-diameter ratios and calculation pressures, a mathematical model for the lightweight optimization of such structures is established.MethodThe main control program of the genetic algorithm is established in MATLAB, and the ultimate bearing capacity is calculated and checked by finite element software ANSYS. The differences of optimization schemes between titanium alloy single layer stiffened cylindrical shells and titanium alloy double layer stiffened cylindrical shells under different length-to-diameter ratios and different calculation pressures are then compared and analyzed. ResultsThere are two critical calculation pressures in the optimization design of titanium alloy stiffened cylindrical shells, and the optimization design is divided into three types: the stability constraint type of ultimate bearing capacity constraint control optimization design, the strength constraint type of strength constraint control optimization design, and the joint constraint type of strength and ultimate bearing capacity constraint joint control optimization design. The larger the length-to-diameter ratio, the greater the critical calculation pressure. Under the same calculation pressure and length-to-diameter ratio, the weight of the double layer shell optimization scheme is lighter than that of the single layer shell, and the critical calculation pressure of the double layer shell optimization design is smaller than that of the single layer shell under the same length-to-diameter ratio.ConclusionThe results of this study can provide useful references for the optimization design of titanium alloy double layer stiffened cylindrical shell structures.
ObjectivesAiming at the problems that the symptom parameters from monitoring signals in a single analysis domain fail to fully characterize the operating state of the monitored object, and the model parameters of the Extreme Learning Machine (ELM) network are difficult to achieve the optimization, a fault diagnosis method for ship motor bearings is proposed, based on multi-domain information fusion and an improved ELM. Methods First, a multi-domain feature parameter set was constructed from the vibration signals of ship motor bearings in the time domain, frequency domain and time-frequency domain. This set served as the input to the fault diagnosis model. The sparrow search algorithm was then used to optimize the model parameters of the ELM network by determining the optimal weights and thresholds, thus enhancing the fault diagnosis accuracy of ELM model. Finally, the fault states of motor bearings were identified using experimental data from a self-built test bench and open-source experimental datasets. Results Experimental data verification based on the marine motor test bench demonstrated that the fault diagnosis model using multi-domain feature parameter sets, achieved a recognition accuracy of 100% on both the training and test sets. Verification with open-source experimental data showed that the recognition accuracy on the test set for the improved ELM model was 90.5%, which is 12.7% higher than that of the original ELM model. Additionally, the recognition accuracies on both training and test sets were higher than those of other diagnostic models. Conclusions This study has improved the input symptom parameter set and the diagnosis model. The proposed method can effectively identify the fault states of motor bearings and demonstrates good model stability, providing a valuable reference for fault diagnosis of ship motor bearings.
Mykel Fernandes de Sousa, Cláudio Dybas da Natividade, Marçal Rosas Florentino Lima Filho
et al.
Coral reefs are very important ecosystems for the planet, offering ecological and socio-economic benefits. However, they are under threat due to anthropogenic factors and environmental changes. This study assesses the feasibility of weathered Portland cement concrete as a material for marine artificial reefs by comparing its physicochemical and mechanical properties with those of natural coral skeletons from the coast of Paraíba, Brazil. Analyses included microstructural and physical characterization, compressive strength and ultrasonic pulse velocity tests, as well as pH monitoring. The results indicated that weathered concrete exhibits mineralogical similarity to corals, with the presence of carbonate phases and portlandite absent due to advanced carbonation. The compressive strength of the concrete (27.6 MPa) was significantly higher than that of the coral samples (1–6 MPa), while the porosity of the corals (34–41%) exceeded that of the concrete (14%). The alkaline nature of the concrete (pH 9.7) remained stable. Although differences in physical and mechanical properties are evident, the values are within the ranges reported for cementitious materials in marine applications. Mineralogical similarities between coral skeletons and concrete support its potential as a functional analog in artificial reefs, while adjustments in geometry and porosity are suggested to enhance ecological compatibility.
Sponges have always been filter feeders, in contrast to all the other filter-feeding invertebrate groups for which this feeding mode is a secondary adaptation. This study calls attention to this aspect, which explains why sponges are tolerant to hypoxia, but probably not more tolerant than the other filter-feeding invertebrates. The measurement of respiration rates at decreasing oxygen concentrations along with an estimation of the oxygen extraction efficiency in the marine demosponge <i>Halichondria panicea</i> have been used to understand why sponges are tolerant to low oxygen concentrations. It was found that the respiration rate was constant down to about 1.5 mL O<sub>2</sub> L<sup>−1</sup>, which shows that the extraction efficiency increases with a decreasing oxygen concentration. It is argued that the relationship between the filtration rate and oxygen consumption in filter feeders is controlled by the resistance to the diffusion of oxygen across the boundary layer between the feeding current and the tissues of the body. A high tolerance to hypoxia is a consequence of the adaptation to filter feeding, and sponges do not have a special capacity to overcome hypoxic events.
Unmanned surface vehicles (USVs) have garnered significant attention across various application fields. A sufficiently accurate kinetic model is essential for achieving high-performance navigation and control of USVs. However, time-varying unobservable internal states and external disturbances pose challenges in accurately modeling the USV’s kinetics, and existing methods face difficulties in accurately estimating unknown time-varying disturbances online while ensuring precise mechanism modeling. To address this issue, a novel grey-box modeling method based on incremental learning and mechanisms (GBM-ILM) is proposed. Its union structure combines the advantages of both incremental learning networks and physical mechanisms for estimating the USV’s full kinetics. Depending on the linear parameter-varying (LPV) mechanism, it not only adheres to physical laws but also calculates the unstructured model errors. An incremental learning network is implemented to continuously refine model errors, by accounting for the USV’s time-varying characteristics and iteratively updating the network parameters and structures to adapt to different USV states and environmental disturbances. To validate this method, we developed the ‘Salmon’ USV and conducted identification experiments in a lake. Compared to tests of other state-of-the-art methods, our method has better adaptability, with 46.34%, 14.86%, and 6.87% accuracy improvements when estimating the USV’s forward, turning, and sideslip dynamic model, respectively.
Diana María Quintana-Saavedra, Rafael Ricardo Torres-Parra, Richard Guzmán-Martínez
et al.
This paper proposes a comprehensive methodology for the management of submerged cultural heritage sites despite their worldwide location. The methodology is applied to four colonial shipwrecks located in Cartagena de Indias Bay (Colombia), two of them in the Inner Bay and two in the Bocachica sector. Five criteria are used and scored from 1 (indicating a low risk for the wreck) to 5 (high risk). The sum of the scores obtained at each criterion ranges from 5 to 25, and when the value obtained is higher than 15, management action is required. Five criteria were analyzed; (i) The historical criterion is based on the antiquity of the wreck. The ones investigated in this paper are associated with the Battle of Cartagena de Indias (A.D. 1741), having been submerged for ~280 years (all wrecks obtained a score of 3); (ii) The geographical criterion concerns the depth at which the wreck is located, which determines its accessibility. In Cartagena Bay, wrecks are situated at a water depth between 15.6 and 29.7 m (all wrecks were scored 4); (iii) The shipwreck condition criterion indicates the level of preservation, including organic and inorganic material, distinguishing among wooden hulls, ballast stones, and cannons. Obtained scores were 4 and 3 for the wrecks, respectively, located in the Inner Bay and in the Bocachica sector. (iv) The oceanographic criterion, linked to chemical and biological conditions of the water column, influences wreck conservation. All wrecks investigated scored 5. (v) The socioeconomic criterion indicates the multiple maritime and cultural activities presently taking place that might affect the wreck. In Cartagena Bay, all wrecks were scored 4. According to the total score obtained (20—Inner Bay and 19—Bocachica sector), guidelines for shipwreck conservation of cultural heritage in Cartagena Bay are proposed.
A CO2 boiled off gas (CO2 BOG) reliquefaction system using liquid ammonia cold energy is designed to solve the problems of fuel cold energy waste and the large power consumption of the compressor in the process of CO2 BOG reliquefaction on an ammonia-powered CO2 carrier. Aspen HYSYS is used to simulate the calculation, and it is found that the system has lower power consumption than the existing reliquefaction method. The temperature of the heat exchanger heater-1 heat flow outlet node (node C-4) is optimised, and it is found that, with the increase of the node C-4 temperature, the power consumption of the compressor gradually increases, and the liquefaction fraction of CO2 BOG gradually decreases. Under 85% conditions, when the ambient temperature is 0°C and the temperature of node C-4 is -9°C, the liquid fraction of CO2 BOG reaches the maximum, which is 74.46%, and the power of Compressor-1 is the minimum, which is 40.90 kW. According to this, the optimum temperature of node C-4 under various working conditions is determined. The exergy efficiency model is established, in an 85% ship working condition with the ambient temperature of 40°C, and the exergy efficiency of the system is the maximum, reaching 59.58%. Therefore, the CO2 BOG reliquefaction system proposed in this study could realise effective utilisation of liquid ammonia cold energy.
A major ocean response to tropical cyclone (TC) wind is the mixing of warm sea-surface water with cool subsurface water, which decreases the sea-surface temperature (SST). The decreased SST (δT) under the TC (rather than the cooled water in the wake after the storm has passed) modifies the storm’s intensity and is of interest to TC intensity studies. Here, the author shows that δT (non-dimensionalized by some reference temperature) is linearly related to Ψ, a dimensionless (nonlinear) function of TC and ocean parameters: the TC maximum wind, radius, and translation speed, as well as the ocean’s 26 °C and 20 °C isothermal depths (Z<sub>26</sub> and Z<sub>20</sub>). The Ψ can be estimated from observations. The modelled δT is validated against sea-surface cooling observed by satellites, δT<sub>o</sub>, for typhoons during the May–December 2015 period in the western North Pacific. The result yields a best-fit, linear relation between δT<sub>o</sub> and Ψ that explains ~60% of the observed variance: r<sup>2</sup> ≈ 0.6 (99% confidence). Tests show that the cube of the TC maximum wind and the ocean’s Z<sub>26</sub> account for 46% and 7%, respectively, of the observed variance, indicating their predominant influence on TC-induced cooling. Contributions from other parameters are less but not negligible.
A wreck salvage scheme is designed for the integral salvage of “Yangtze River Estuary II” ancient ship, and the dynamic response of the critical state of salvage is investigated via numerical simulation. Comprehensive studies including the motion response characteristics of the multi-body coupling system and the tension of the cable under different sea conditions are conducted. The results show that the instability of the salvage system increases under the transverse sea condition, and the operation risk is obviously higher than other waves of the same wave level conditions. In addition, the tension responses of lifting cables and mooring cables are higher in the off-bottom stage than that of the submerged part of the wreck. The numerical results demonstrate the feasibility of the salvage scheme, providing reference for decision-making on sea conditions to avoid the risk of salvage operation.
Engineering (General). Civil engineering (General), Chemical engineering
Norhafiz Hanafi, Meng-Hsien Chen, Ying Giat Seah
et al.
Given the identification of the new species <i>Johnius taiwanesis</i>, the <i>Johnius</i> genus in Taiwanese water is here reviewed through a collection of field samples, museum specimens, and a review of the Taiwanese scientific literature. Seven valid <i>Johnius</i> species were successfully identified and distinguished based on gill raker length, tip of upper jaw to mouth hinge length, tip of lower jaw to mouth hinge length, and length of second spine of anal fin. Our phylogenetic tree based on cytochrome oxidase subunit I (COI) showed the existence of high interspecific genetic diversity within the genus <i>Johnius</i> forming a monophyletic group. The <i>Johnius</i> species in Taiwan are mainly distributed in a latitude ranging from Xingda (22.4° N) to Hsinchu (24.8° N) with <i>J. taiwanensis</i>, <i>J. distinctus</i>, and <i>J. belangerii</i> representing the most abundant species caught throughout the year. <i>Johnius amblycephalus</i> and <i>J. borneensis</i> were only caught in the summer, while <i>J. trewavasae</i> was rarely caught. In conclusion, we provide a dichotomous key for the genus <i>Johnius</i> in Taiwan waters.
For the prediction of landslide-generated waves, previous studies have developed numerous empirical equations to express the maximums of wave characteristics as functions of slide parameters upon impact. In this study, we built the temporal relationship between the wave characteristics and slide features. We gave specific insights into impulse waves generated by snow avalanches and mimicked them using a buoyant material called <i>Carbopol</i> whose density is close to that of water. Using the particle image velocimetry (PIV) technique, the slide’s temporal velocity field and thickness, as well as the temporal free water surface fluctuation, were determined experimentally. Using a statistical method denoted as <i>panel data analysis</i>, we quantified the temporal wave amplitude from the time series data of the thickness and depth-averaged velocity of the sliding mass at the shoreline. Then, the slide’s temporal thickness and velocity at the shoreline were estimated from the parameters of the stationary slide at the initial position, based on the viscoplastic theory. Combining the panel data analysis and the viscoplastic theory, the temporal wave amplitudes were estimated from the initial slide parameters. In the end, we validated the proposed theoretical–statistical combined predictive method with the support of experimental data.
Ocean research and development has entered the era of all-sea-depth technology. A sediment mechanical property survey system with all-sea depth capability is very important in seabed resource exploration and research. This study used the in-situ test system of sediment mechanical properties as the object of study and designed an intelligent measurement system for all-sea-depth static touch exploration test, cross-board shear test, full-flow penetration test, and in-situ sampling function, which combines the characteristics of difficult communication, multiple uncertainties, small range of changes in shear strength of deep-sea sediments, and the influence of sea pressure on mechanical properties, according to the specifications and conventions of deep-sea sediment soil mechanical tests. The implementation of the system relies on the ARM+FPGA structure on the hardware and the state machine design mode of the software and satisfies the requirements of the all-sea-depth test on responsiveness and fault tolerance. A series of sample soils and sea experiments have demonstrated that the system can work at all-sea depths and accurately test the mechanical properties of sediments.
There is a need to study the evolutionary laws of the risks in the navigation environments of complex marine areas. This can promote shipping safety using an early-warning system. The present study determines shipping flows and meteorological conditions in a marine area on the basis of meteorological and automatic identification system (AIS) data. It also determines the uncertainty evolution law of the navigation environment’s influencing factors. Moreover, a navigation risk evolution system for ships in complex marine areas was developed. A case study was carried out in a coastal area of China on the basis of the determined evolutionary laws. Evolution in the navigational environment risk within the case study area was analyzed. The results showed that the hydrometeorology wind factor has the greatest impact on the risk of ship collisions. This work was not only able to show advances in navigational collision environmental evolution laws but also provides a theoretical reference for the evaluation and early warning of risks in shipping environments.
ObjectivesIn order to improve the working performance of gas turbines, this study examines the aerodynamic performance of variable geometry turbine clamshell guide vanes.MethodsTaking a typical variable geometry turbine guide vane as a prototype, a clamshell turbine guide vane model with a fixed pressure side and rotating suction side is constructed. Numerical simulation based on the shear-stress transport (SST) model is then carried out to analyze the parameter variation laws of the clamshell turbine guide vane and ordinary variable geometry turbine guide vane under different working conditions. ResultsAs the calculation results show, when the rotation angle of the guide vane varies from +3° to –3°, the aerodynamic performance of the clamshell turbine guide vane has certain advantages, a larger flow rate and a smaller total pressure loss coefficient. When the guide vane rotation angle exceeds +3°, its flow rate increase will decrease significantly and even stop or negatively increase. When the rotation angle of the suction surface gradually increases from a negative angle to 0°, the total pressure loss coefficient will gradually decrease. When the rotation angle is greater than 0°, the total pressure loss coefficient will increase with the rotation angle.ConclusionsThe adoption of clamshell turbine guide vanes can improve the aerodynamic performance of turbines to a certain extent.
This paper considers the general ocean circulation (GOC) within the thermodynamical closure of our climate theory, which aims to deduce the generic climate state from first principles. The preceding papers of this theory have reduced planetary fluids to warm/cold masses and determined their bulk properties, which provide prior constraints for the derivation of the upper-bound circulation when the potential vorticity (PV) is homogenized in moving masses. In a companion paper on the general atmosphere circulation (GAC), this upper bound is seen to reproduce the observed prevailing wind, therefore forsaking discordant explanations of the easterly trade winds and the polar jet stream. In this paper on the ocean, we again show that this upper bound may replicate broad features of the observed circulation, including a western-intensified subtropical gyre and a counter-rotating tropical gyre feeding the equatorial undercurrent. Since PV homogenization has short-circuited the wind curl, the Sverdrup dynamics does not need to be the sole progenitor of the western intensification, as commonly perceived. Together with GAC, we posit that PV homogenization provides a unifying dynamical principle of the large-scale planetary circulation, which may be interpreted as the maximum macroscopic motion extractable by microscopic stirring, within the confines of thermal differentiation.
Panayiotis Theodoropoulos, Christos C. Spandonidis, Nikos Themelis
et al.
Adverse conditions within specific offshore environments magnify the challenges faced by a vessel’s energy-efficiency optimization in the Industry 4.0 era. As the data rate and volume increase, the analysis of big data using analytical techniques might not be efficient, or might even be infeasible in some cases. The purpose of this study is the development of deep-learning models that can be utilized to predict the propulsion power of a vessel. Two models are discriminated: (1) a feed-forward neural network (FFNN) and (2) a recurrent neural network (RNN). Predictions provided by these models were compared with values measured onboard. Comparisons between the two types of networks were also performed. Emphasis was placed on the different data pre-processing phases, as well as on the optimal configuration decision process for each of the developed deep-learning models. Factors and parameters that played a significant role in the outcome, such as the number of layers in the neural network, were also evaluated.