Search Results (183)

Marine sources contribute approximately 2% of global energy-related CO₂ emissions, with the shipping industry accounting for 87% of this total, making it the fifth-largest emitter globally. Environmental regulations by the International Maritime Organization (IMO), such as the MARPOL (International Convention for the Prevention of Pollution from Ships) treaty, have driven the exploration of alternative green energy solutions, including nuclear-powered ships. These ships offer advantages like long operational periods without refueling and increased cargo space, with around 200 reactors already in use on naval vessels worldwide. Among advanced reactor concepts, the molten salt reactor (MSR) is particularly suited for marine applications due to its inherent safety features, compact design, high energy density, and potential to mitigate nuclear waste and proliferation concerns. However, MSR systems face significant challenges, including tritium production, corrosion issues, and complex behavior of volatile fission products. Understanding the impact of marine-induced motion on the thermal–hydraulic behavior of MSRs is crucial, as it can lead to transient design basis accident scenarios. Furthermore, the adoption of MSR technology in the shipping industry requires overcoming regulatory hurdles and achieving global consensus on safety and environmental standards. This review assesses the current progress, challenges, and technological readiness of MSRs for marine applications, highlighting future research directions. The overall technology readiness level (TRL) of MSRs is currently at 3. Achieving TRL 6 is essential for progress, with individual components needing TRLs of 4–8 for a demonstration reactor. Community Readiness Levels (CRLs) must also be addressed, focusing on public acceptance, safety, sustainability, and alignment with decarbonization goals. Full article

With the development of the marine oil industry, leakage accidents are one of the most serious problems threatening maritime and national security. The spilt crude oil can float and sink in the water column, posing a serious long-term threat to the marine environment. High-frequency sonar detection is currently the most efficient method for identifying sunken oil. However, due to the complicated environment of the deep seabed and the interference of the sunken oil signals with homogeneous information, sonar detection data are usually difficult to interpret, resulting in low efficiency and a high failure rate. Previous works have focused on features designed by experts according to the detection environments and the identification of sunken oil targets regardless of the feature extraction step. To automatically identify sunken oil targets without a prior knowledge of the complex seabed conditions during the image acquisition process for sonar detection, a systematic framework is contrived for identifying sunken oil targets that combines image enhancement with a convolutional neural network (CNN) classifier for the final decision on sunken oil targets examined in this work. Case studies are conducted using datasets obtained from a sunken oil release experiment in an outdoor water basin. The experimental results show that (i) the method can effectively distinguish between the sunken oil target, the background, and the interference target; (ii) it achieved an identification accuracy of 83.33%, outperforming feature-based recognition systems, including SVM; and (iii) it provides important information about sunken oil such as the location of the leak, which is useful for decision-making in emergency response to oil spills at sea. This line of research offers a more robust and, above all, more objective option for the difficult task of automatically identifying sunken oils under complex seabed conditions. Full article

Vietnam’s seaport system currently includes 298 ports with 588 wharves (a total length of approximately 92,275 m), which is vital in developing Vietnam’s marine economy. The piled pier, a type of wharf structure, is widely used and accounts for up to 90%, while the remaining 10% is made up of other types of wharf structures, such as gravity and sheet pile quay walls. Most wharves have been operating for over 10 years and some for even more than 50 years. Noticeably, wharves are highly vulnerable and degrade rapidly due to many factors, especially heavy load impacts and severe environmental conditions. Additionally, wharves have a higher risk of deterioration than other inland infrastructure, such as buildings and bridges. Consequently, determining a wharf’s health is an important task in maintaining normal working conditions, extending its lifecycle, and avoiding other severe damage that could lead to dangers to the safety of vehicles, facilities, and humans. Moreover, regulated quality inspections usually include only simple inspections, e.g., displacement, settlement, geometric height, and tilt; the visual inspection and determination of dimensions by simple length-measuring equipment; concrete strength testing by ultrasonic and rebound hammers; and the experimental identification of the chloride ion concentration, chloride diffusion coefficient, corrosion activity of rebar in concrete, and steel thickness. These testing methods often give local results depending on the number of test samples. Therefore, advanced diagnostic techniques for assessing the technical condition of piled piers need to be studied. The impact vibration test (IVT) is a powerful non-destructive evaluation method that indicates the overall health of structures, e.g., underground and foundation structures, according to official standards. Hence, the IVT is expected to help engineers detect the potential deterioration of overall structures. It is fundamental that, if a structure is degraded, its natural frequency will be affected. A structure’s health index and technical condition are determined based on this change. However, the IVT does not seem to be widely applied to piled piers, with no published standard; hence, controversial issues related to accuracy and reliability still remain. This motivates the present study to recommend an adjusted factor (equal to 1.16) for the health index (classified in official standards for other structures) through numerical and experimental approaches before officially applying the IVT method to piled piers. The current work focuses on the health index using the design natural frequency, which is more practical in common cases where previous historical data and the standard natural frequency are unavailable. This study also examines a huge number of influencing factors and situations through theoretical analysis, experience, and field experiments to propose an adjusted indicator. The results are achieved with several assumptions of damages, such as the degradation of materials and local damages to structural components. With the proposed adjusted indicator, the overall health of piled piers can be assessed quickly and accurately by IVT inspections in cases of incidents, accidents due to collisions, cargo falls during loading and unloading, or subsidence and erosion due to natural disasters, storms, and floods. Full article

Due to the detrimental impact of fog on image quality, dehazing maritime images is essential for applications such as safe maritime navigation, surveillance, environmental monitoring, and marine research. Traditional dehazing techniques, which are dependent on presupposed conditions, often fail to perform effectively, particularly when processing sky regions within marine fog images in which these conditions are not met. This study proposes an adaptive sky area segmentation dark channel prior to the marine image dehazing method. This study effectively addresses challenges associated with traditional marine image dehazing methods, improving dehazing results affected by bright targets in the sky area and mitigating the grayish appearance caused by the dark channel. This study uses the grayscale value of the region boundary’s grayscale discontinuity characteristics, takes the grayscale value with the least number of discontinuity areas in the grayscale histogram as a segmentation threshold adapted to the characteristics of the sea fog image to segment bright areas such as the sky, and then uses grayscale gradients to identify grayscale differences in different bright areas, accurately distinguishing boundaries between sky and non-sky areas. By comparing the area parameters, non-sky blocks are filled; this adaptively eliminates interference from other bright non-sky areas and accurately locks the sky area. Furthermore, this study proposes an enhanced dark channel prior approach that optimizes transmittance locally within sky areas and globally across the image. This is achieved using a transmittance optimization algorithm combined with guided filtering technology. The atmospheric light estimation is refined through iterative adjustments, ensuring consistency in brightness between the dehazed and original images. The image reconstruction employs calculated atmospheric light and transmittance values through an atmospheric scattering model. Finally, the use of gamma-correction technology ensures that images more accurately replicate natural colors and brightness levels. Experimental outcomes demonstrate substantial improvements in the contrast, color saturation, and visual clarity of marine fog images. Additionally, a set of foggy marine image data sets is developed for monitoring purposes. Compared with traditional dark channel prior dehazing techniques, this new approach significantly improves fog removal. This advancement enhances the clarity of images obtained from maritime equipment and effectively mitigates the risk of maritime transportation accidents. Full article

Maritime UAV path planning is a key link in realizing the intelligence of maritime emergency transportation, providing key support for fast and flexible maritime accident disposal and emergency material supply. However, most of the current UAV path planning methods are designed for land environments and lack the ability to cope with complex marine environments. In order to achieve effective path planning for UAV in marine environments, this paper proposes a Directional Drive-Rotation Invariant Quadratic Interpolation White Shark Optimization algorithm (DD-RQIWSO). First, the directional guidance of speed is realized through a directional update strategy based on the fitness value ordering, which improves the speed of individuals approaching the optimal solution. Second, a rotation-invariant update mechanism based on hyperspheres is added to overcome the tracking pause phenomenon in WSO. In addition, the quadratic interpolation strategy is added to enhance the utilization of local information by the algorithm. Then, a wind simulation environment based on the Lamb–Oseen vortex model was constructed to better simulate the real scenario. Finally, DD-RQIWSO was subjected to a series of tests in 2D and 3D scenarios, respectively. The results show that DD-RQIWSO is able to realize path planning under wind environments more accurately and stably. Full article

The development of offshore wind farms is an important step toward increasing the share of green energy in Poland’s energy mix, offering promising prospects for the energy industry. However, in addition to numerous benefits, such investments also carry potential risks for the marine environment, including the risk of spills of hazardous substances such as gear oils, hydraulic oils, and lubricants. This paper analyses the potential impact of oil spills from offshore wind farms on the marine ecosystems of the Baltic Sea, taking into account hydrometeorological factors, particularly protected areas (such as Natura 2000 sites) and the intensity of ship traffic in the area of the planned farms. Simulations of spill scenarios are also presented to assess the potential extent of pollution and its impact on the environment. This paper emphasises the importance of advanced monitoring and safety systems in minimising the risk of accidents and responding quickly to possible incidents. The development of offshore wind farms in Poland presents itself as a key element in a sustainable energy development strategy, combining advanced technology with environmental concerns. Full article

Lifeboats are life-saving equipment used when it is necessary to abandon a ship or, in some ships, for man-overboard situations (to collect persons from water). Every seafarer onboard a ship has a task related to lifeboat operation in an emergency. In order to master and practise the assigned tasks, be ready to react at any moment, and efficiently use life-saving equipment and appliances, seafarers on ships perform drills at prescribed intervals. Effective drill performance is of paramount importance, as it improves safety and enables crew members to practise lifeboat operations. However, although their primary role is life-saving, lifeboat drills have resulted in numerous accidents, causing injuries and fatalities, besides equipment damage. Therefore, it is necessary to prevent such unwanted events and discover their root causes. As the human factor is considered a significant cause of marine accidents, this paper aims to quantify human error probability (HEP) during lifeboat drills. In addition, because lifeboat drill accident data are scarce, this study adopted the Success Likelihood Index Method (SLIM) for human reliability analysis (HRA). Based on expert judgments, the tasks with the highest probability of human error and factors significantly influencing human performance during lifeboat drills are identified. According to the study results, the recovery of the lifeboat is the most hazardous phase with the highest HEP. In addition, the BN-SLIM is adopted to estimate the probability of human error during the recovery of the lifeboat. The task with the largest HEP is confirming the release lever is properly rested and hooks locked (HEP = 4.5%). Furthermore, the design and condition of equipment and Crew Competence are identified as the most important Performance-Shaping Factors (PSFs) that affect crew members’ performance. The BN-SLIM model was verified utilising a sensitivity analysis and validated by analysing real-life lifeboat drill accidents that occurred during lifeboat recovery. The results confirmed that the model could be used to analyse lifeboat accidents and for proactive preventive measures because most influencing factors are recognised, and acting on them can significantly reduce the HEP of the overall task, improve lifeboat safety, and save lives at sea. Full article

Accidents in marine operations are occurring consistently despite government safety initiatives and efforts to lower the number of accidents and the ensuing human casualties. Since each accident type has a different frequency and casualty rate, identifying risky accident types is important to determine the priority for taking necessary risk reduction actions. Usually, a risk is calculated using two criteria, i.e., the frequency and fatality of an accident. However, the accident statistics show that for the last 5 years from 2018 to 2022, the injury rate is more than three times the death rate in maritime accidents in Korean waters. Considering the importance of injury, unlike other previous studies, we perform a risk analysis with three criteria, i.e., frequency, death, and injury to complement the conventional risk calculation methods, which can help decision-makers allocate the limited resources to the riskiest types of accidents in order of priority. In doing so, we employed an integrated ordinal priority approach (OPA) and grey relational analysis (GRA) method to assign proper weight to each criterion and rank eight accident types. We categorized the accidents types into three different groups where safety accidents and collisions were ranked as the most dangerous types. The combined OPA and GRA technique has been effectively applied to other risky industries, as well as the maritime industry. Additionally, the proposed method is suitable for multi-criteria models when each criterion has a different importance. Finally, the method can be integrated into the framework of the risk ranking process to enhance the analysis results. Full article

Oil spillages on a sea’s or an ocean’s surface are a threat to marine and coastal ecosystems. They are mainly caused by ship accidents, illegal discharge of oil from ships during cleaning and oil seepage from natural reservoirs. Synthetic-Aperture Radar (SAR) has proved to be a useful tool for analyzing oil spills, because it operates in all-day, all-weather conditions. An oil spill can typically be seen as a dark stretch in SAR images and can often be detected through visual inspection. The major challenge is to differentiate oil spills from look-alikes, i.e., low-wind areas, algae blooms and grease ice, etc., that have a dark signature similar to that of an oil spill. It has been noted over time that oil spill events in Pakistan’s territorial waters often remain undetected until the oil reaches the coastal regions or it is located by concerned authorities during patrolling. A formal remote sensing-based operational framework for oil spills detection in Pakistan’s Exclusive Economic Zone (EEZ) in the Arabian Sea is urgently needed. In this paper, we report the use of an encoder–decoder-based convolutional neural network trained on an annotated dataset comprising selected oil spill events verified by the European Maritime Safety Agency (EMSA). The dataset encompasses multiple classes, viz., sea surface, oil spill, look-alikes, ships and land. We processed Sentinel-1 acquisitions over the EEZ from January 2017 to December 2023, and we thereby prepared a repository of SAR images for the aforementioned duration. This repository contained images that had been vetted by SAR experts, to trace and confirm oil spills. We tested the repository using the trained model, and, to our surprise, we detected 92 previously unreported oil spill events within those seven years. In 2020, our model detected 26 oil spills in the EEZ, which corresponds to the highest number of spills detected in a single year; whereas in 2023, our model detected 10 oil spill events. In terms of the total surface area covered by the spills, the worst year was 2021, with a cumulative 395 sq. km covered in oil or an oil-like substance. On the whole, these are alarming figures. Full article

Ship pollution accidents can cause serious harm to marine ecosystems and economic development. This study proposes a ship pollution accident analysis method based on a knowledge graph to solve the problem that complex accident information is challenging to present clearly. Based on the information of 411 ship pollution accidents along the coast of China, the Word2vec’s word vector models, BERT–BiLSTM–CRF model and BiLSTM–CRF model, were applied to extract entities and relations, and the Neo4j graph database was used for knowledge graph data storage and visualization. Furthermore, the case information retrieval and cause correlation of ship pollution accidents were analyzed by a knowledge graph. This method established 3928 valid entities and 5793 valid relationships, and the extraction accuracy of the entities and relationships was 79.45% and 82.47%, respectively. In addition, through visualization and Cypher language queries, we can clearly understand the logical relationship between accidents and causes and quickly retrieve relevant information. Using the centrality algorithm, we can analyze the degree of influence between accident causes and put forward targeted measures based on the relevant causes, which will help improve accident prevention and emergency response capabilities and strengthen marine environmental protection. Full article

In the midst of a rapidly evolving economic landscape, the global demand for oil is steadily escalating. This increased demand has fueled marine extraction and maritime transportation of oil, resulting in a consequential and uneven surge in maritime oil spills. Characterized by their abrupt onset, rapid pollution dissemination, prolonged harm, and challenges in short-term containment, oil spill accidents pose significant economic and environmental threats. Consequently, it is imperative to adopt effective and reliable methods for timely detection of oil spills to minimize the damage inflicted by such incidents. Leveraging the YOLO deep learning network, this paper introduces a methodology for the automated detection of oil spill targets. The experimental data pre-processing incorporated denoise, grayscale modification, and contrast boost. Subsequently, realistic radar oil spill images were employed as extensive training samples in the YOLOv8 network model. The trained detection model demonstrated rapid and precise identification of valid oil spill regions. Ultimately, the oil films within the identified spill regions were extracted utilizing the simulated annealing particle swarm optimization (SA-PSO) algorithm. The proposed method for offshore oil spill survey presented here can offer immediate and valid data support for regular patrols and emergency reaction efforts. Full article

Marine diesel engines are the preferred power equipment for ships and are the most important component among the numerous electromechanical devices on board. Accidents involving these engines can potentially cause immeasurable damage to the vessel, making fault detection in marine diesel engines crucial. This design enables the detection and reporting of faults in marine diesel engines at the earliest possible time through the computation of convolutional neural networks, which is of great significance for ensuring the safe navigation of ships. For this functionality, the Xilinx ZYNQ-7000 XC7Z010 is selected as the main control chip, and the LoRa wireless network is used as the transmission module. The FreeRTOS embedded operating system is ported, with sensor data collection completed on the PS side of the ZYNQ chip and algorithm acceleration calculations on the PL side. Data are then transmitted to the host computer via the LoRa module paired with a custom protocol. Experimental test results show that the program provides stable data transmission, with each module of the algorithm generally accelerating by more than 95% and an accuracy rate of 92.86%. Additionally, the host computer can display the received data in real time. The custom protocol’s header also allows for precise judgments about the completeness and origin of messages, facilitating the expansion of other SOC’s message uplink and the host computer’s message downlink. Full article

A fire in a marine energy storage system (ESS) has a high risk because of the special situation of the sea compared with land systems. To mitigate serious damage in the event of a fire in marine ESSs, initial suppression of the fire is extremely important. In this study, a unit module-based fire extinguishing system was constructed for the initial suppression of an ESS fire, and a unit module fire suppression test was conducted. In addition, multiple modules were constructed to evaluate the impact of unit module fire suppression on adjacent modules. Novec 1230 and F-500, which are adaptable to ESS fire control, were used as extinguishing agents. The fire suppression test results showed that both extinguishing agents could effectively suppress the ESS fire in the initial stage using the proposed fire extinguishing system. The results of this study will contribute to the development of maritime safety protocols and practical measures for reinforcing preparation for ESS-related fire accidents. Full article

To ensure the safe protection of marine engine systems, it is necessary to explore the hot surface ignition (HSI) characteristics of marine diesel in ship environments. However, an accurate model describing these complex characteristics is still not available. In this work, a new experimental method is proposed in order to enhance prediction performance by integrating testing data of the characteristics of HSI of marine diesel. The sensitivity of HSI is determined by various factors such as surface parameters, flow state, and the ship’s environment. According to variations in the HSI status of marine diesel in an engine room, the HSI probability is distributed in three phases. It is essential to determine whether the presence of marine diesel or surrounding items can intensify the risk of an initial fire beginning in the engine room. A vapor plume model was developed to describe the relationship between HSI height and initial specific buoyancy flux in vertical space. Further, field distribution revealed significant variation in the increase in temperature between 200 and 300 mm of vertical height, indicating a region of initial HSI. In addition, increasing surface temperature did not result in a significant change in ignition delay time. After reaching a temperature of 773 K, the ignition delay time remained around 0.48 s, regardless of how much the hot surface temperature increased. This study reveals the HSI evolution of marine diesel in a ship engine room and develops data-based predictive models for evaluating the safety of HSI parameters during initial accident assessments. The results show that the goodness of fit of the predictive models reached above 0.964. On the basis of the predicted results, the HSI characteristics of marine diesel in engine rooms could be gleaned by actively determining the parameters of risk. Full article