Search Results (74)

In the wake of the continuous development of the international strategic petroleum reserve system, the tonnage and quantity of oil tankers have been increasing. This trend has driven the expansion of offshore oil exploration and transportation, resulting in frequent incidents of ship oil spills. Catastrophic impacts have been exerted on the marine environment by these accidents, posing a serious threat to economic development and ecological security. Therefore, there is an urgent need for efficient and reliable methods to detect oil spills in a timely manner and minimize potential losses as much as possible. In response to this challenge, a marine radar oil film segmentation method based on feature fusion and the artificial bee colony (ABC) algorithm is proposed in this study. Initially, the raw experimental data are preprocessed to obtain denoised radar images. Subsequently, grayscale adjustment and local contrast enhancement operations are carried out on the denoised images. Next, the gray level co-occurrence matrix (GLCM) features and Tamura features are extracted from the locally contrast-enhanced images. Then, the generalized least squares (GLS) method is employed to fuse the extracted texture features, yielding a new feature fusion map. Afterwards, the optimal processing threshold is determined to obtain effective wave regions by using the bimodal graph direct method. Finally, the ABC algorithm is utilized to segment the oil films. This method can provide data support for oil spill detection in marine radar images. Full article

Hydrogen is a potential key component of a carbon-neutral energy carrier and an input to marine industrial processes. This study examines the consequences of coupled hydrogen release and marine environmental factors during floating photovoltaic hydrogen production (FPHP) system failures. A validated three-dimensional numerical model of FPHP comprehensively characterizes hydrogen leakage dynamics under varied rupture diameters (25, 50, 100 mm), transient release duration, dispersion patterns, and wind intensity effects (0–20 m/s sea-level velocities) on hydrogen–air vapor clouds. FLACS-generated data establish the concentration–dispersion distance relationship, with numerical validation confirming predictive accuracy for hydrogen storage tank failures. The results indicate that the wind velocity and rupture size significantly influence the explosion risk; 100 mm ruptures elevate the explosion risk, producing vapor clouds that are 40–65% larger than 25 mm and 50 mm cases. Meanwhile, increased wind velocities (>10 m/s) accelerate hydrogen dilution, reducing the high-concentration cloud volume by 70–84%. Hydrogen jet orientation governs the spatial overpressure distribution in unconfined spaces, leading to considerable shockwave consequence variability. Photovoltaic modules and inverters of FPHP demonstrate maximum vulnerability to overpressure effects; these key findings can be used in the design of offshore platform safety. This study reveals fundamental accident characteristics for FPHP reliability assessment and provides critical insights for safety reinforcement strategies in maritime hydrogen applications. Full article

The frequent occurrence of collision accidents between merchant and fishing vessels in China’s offshore waters not only threatens human lives and property, but also disrupts shipping and fishing activities and may cause marine environmental pollution. To effectively reduce such accidents and increase maritime safety in Chinese coastal waters, this study integrates association rules with complex networks to develop a directed weighted network of causal factors. Grounded theory and the Human Factors Analysis and Classification System (HFACS) are applied to identify and categorize causal factors from 152 collision accident investigation reports. Potential causal relationships are mined using the association rule, which is then applied to construct the causal network. Finally, the topological characteristics of the network are analyzed. The results reveal that serious negligence in lookout, failure to assess collision risks properly, and failure to adopt a safe speed significantly impact collision accidents. These findings highlight the necessity of implementing targeted preventive measures to address critical factors. This study provides valuable insights for maritime stakeholders to develop effective strategies. Full article

As critical components in offshore wind turbine (OWT) systems, pitch bearings require exceptional fatigue resistance to ensure the extended operational lifespan and structural reliability demanded by marine environments. Failure of these bearings due to rolling contact fatigue (RCF) can severely affect the economic efficiency of offshore wind turbines and potentially lead to safety accidents involving both humans and machinery. A simulation model for pitch bearings used in a 3 MW OWT is established to study the RCF behavior under operational conditions based on continuum damage mechanics. Both the elastic and plastic damage are considered in the damage process through a Python script. A user subroutine UMAT is programmed to depict the gradual deterioration of mechanical properties. The results indicate that the fatigue damage of the raceway exhibits significant nonlinear characteristics, with elastic damage playing a predominant role in determining its fatigue life under operational conditions. Full article

In busy offshore waters with high vessel density and intersecting shipping lanes, the risk of collisions and accidents is significantly increased. To address the problem of insufficient feature extraction capability of traditional recurrent neural networks (RNNs) in ship trajectory prediction in busy nearshore areas, this paper proposes a hybrid model based on Particle Swarm Optimization (PSO), Convolutional Neural Networks (CNN), Residual Networks, Attention Mechanism, and Gated Recurrent Units (GRU), named PSO-CNN-RGRU-Attention, for ship trajectory prediction. This study utilizes real Automatic Identification System (AIS) data and applies the PSO algorithm to optimize the model and determine the optimal parameters, using a sliding window method for input and output prediction. The effectiveness and practicality of the model have been fully verified. Experimental results show that, compared to the PSO-CNN-GRU model, the proposed model improves the longitude by 7.8%, 3.4%, and 1.7% in terms of Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE), respectively, and improves the latitude by 48.3%, 62.9%, and 39.2%, respectively. This has significantly contributed to enhancing the safety of ship navigation in the Bohai Strait. Full article

Offshore wind turbines are prone to structural damage over time due to environmental factors, which increases operational costs and the risk of accidents. Early detection of structural damage through monitoring systems can help reduce maintenance costs. However, under complex external conditions and varying structural parameters, existing methods struggle to accurately and quickly detect damage. Understanding the factors that influence structural health is critical for effective long-term monitoring, as these factors directly affect the accuracy and timeliness of damage identification. This study comprehensively analyzed 5 MW offshore wind turbine measurement data, including constructing a digital twin model, establishing a surrogate model, and performing a sensitivity analysis. For monopile-based turbines, sensors in x and y directions were installed at four heights on the pile foundation and tower. Via Bayesian optimization, the finite element model’s structural parameters were updated to align its modal parameters with sensor data analysis results. The update efficiencies of different objective functions and the impacts of neural network hyperparameters on the surrogate model were examined. The sensitivity of the turbine’s structural parameters to modal parameters was studied. The results showed that the modal flexibility matrix is more effective in iteration. A 128-neuron, double-hidden-layer neural network balanced computational efficiency and accuracy well in the surrogate model for modal analysis. Flange damage and soil degradation near the pile mainly impacted the turbine’s health. Full article

Marine nuclear power plants (MNPPs) represent items of forward-looking high-end engineering equipment combining nuclear power and ocean engineering, with unique advantages and broad application prospects. When a nuclear accident occurs, it causes considerable economic losses and casualties. The traditional accident analysis of nuclear power plants only considers the failure of a single system or component, without considering the coupling between the system and the operator, the environment, and other factors. In this study, the cause mechanism of nuclear accidents in MNPPs is analyzed from the perspective of a social technology system. The causal analysis model is constructed by using the internal core causal analysis (e.g., technical control) and external stimulation causal analysis (e.g., social intervention) of accidents, after which the mechanism of the coupled evolution of each influencing factor is analyzed. A Bayesian network inference model is used to quantify the coupling relationship between the factors that affect the deterioration of nuclear accidents. The results show that the main influencing factors are pump failure, valve failure, insufficient response time, poor psychological state, unfavorable sea conditions, unfavorable offshore operating environments, communication failure, inappropriate organizational procedures, inadequate research and design institutions, inadequate regulatory agencies, and inadequate policies. These 12 factors have a high degree of causality and are the main factors influencing the deterioration of the small break loss of coolant accident (SBLOCA). In addition, the causal chain that is most likely to influence the development of SBLOCA into a severe accident is obtained. This provides a theoretical basis for preventing the occurrence of marine nuclear power accidents. Full article

This article offers a concise overview of the best practices for safety in offshore oil and gas operations, focusing on the risks associated with various types of equipment, particularly on the risk of fire. It identifies specific machinery and systems that could pose hazards, assesses their potential impact on safety, and explores conditions that may lead to accidents. Some of the largest accidents were analyzed for their associations with fire hazards and specific equipment. Two primary regulatory approaches to offshore safety are examined: the prescriptive approach in the United States (US) and the goal-oriented approach in Europe. The prescriptive approach mandates strict compliance with specific regulations, while in the goal-oriented approach a failure to adhere to recognized best practices can result in legal accountability for negligence, especially concerning human life and environmental protection. This article also reviews achievements in safety through the efforts of regulatory authorities, industry collaborations, technical standards, and risk assessments, with particular attention given to the status of Mobile Offshore Drilling Units (MODUs). Contrary to common belief, the most frequent types of accidents are not those involving a fire/explosion caused by the failure of the Blowout Preventer (BOP) after a well problem has already started. Following analysis, it can be concluded that the most frequent type of accident typically occurs without fire and is due to material fatigue. This can result in the collapse of the facility, capsizing of the platform, and loss of buoyancy of mobile units, particularly in bad weather or during towing operations. It cannot be concluded that accidents can be more efficiently prevented under a specific type of safety regime, whether prescriptive or goal-oriented. Full article

The deployment of offshore wind turbines (WTs) has emerged as a pivotal strategy in the transition to renewable energy, offering significant potential for clean electricity generation. However, these structures’ operation and maintenance (O&M) present unique challenges due to their remote locations and harsh marine environments. For these reasons, it is fundamental to promote the development of autonomous solutions to monitor the health condition of the construction parts, preventing structural damage and accidents. This paper explores the application of Unmanned Aerial Vehicles (UAVs) in the inspection and maintenance of offshore wind turbines, introducing a new strategy for autonomous wind turbine inspection and a simulation environment for testing and training autonomous inspection techniques under a more realistic offshore scenario. Instead of relying on visual information to detect the WT parts during the inspection, this method proposes a three-dimensional (3D) light detection and ranging (LiDAR) method that estimates the wind turbine pose (position, orientation, and blade configuration) and autonomously controls the UAV for a close inspection maneuver. The first tests were carried out mainly in a simulation framework, combining different WT poses, including different orientations, blade positions, and wind turbine movements, and finally, a mixed reality test, where a real vehicle performed a full inspection of a virtual wind turbine. Full article

The oil and gas industry is a high-risk industry because of the work, meaning that employees in this industry to be very prone to work accidents. Employees who work in isolated locations such as offshore platforms in the long term can experience mental and emotional fatigue, resulting in burnout. Therefore, this research was conducted with the aim of analyzing and identifying the influence of mental workload and burnout on safety behavior with different roster systems of 2 weeks and 3 weeks in the oil and gas industry. The researchers used several approaches to analyzing mental workload, burnout, and safety behavior using the PLS-SEM method with SMART PLS software. Based on the results obtained from the comparative analysis of the roster systems, it was concluded that the 2-week roster system is better than the 3-week one. This research can also provide information to companies regarding the level of mental workload, burnout, and their relationship with safety behavior and recommendations programs for companies to minimize the risk of accidents by paying attention to the mental health aspects of their employees. Full article

To mitigate accidents in submarine cable construction within the rapidly expanding offshore wind power sector, this study employed the analytic hierarchy process (AHP) and risk matrix method (LS) to assess the risks associated with identified factors. Based on project research and expert consultations, five primary and twenty-two secondary risk factors were identified. AHP was utilized to rank the primary risk factors by severity, probability, and detection difficulty, with the highest risk being the environmental impact, followed by third-party destruction and worker error. LS was applied to rank the secondary risk factors by likelihood and severity, with the highest risks being complex submarine topography, low underwater visibility, and fishing operations. The study proposes risk reduction measures based on these evaluations and offers methodological guidance for improving construction safety in similar enterprises. Full article

In the process of natural gas hydrate extraction, especially in offshore hydrate extraction, the multiphase flow inside the wellbore is complex and prone to flow difficulties caused by reservoir sand production, leading to pipeline blockage accidents, posing a threat to the safety of hydrate extraction. This paper presents experimental research on the migration characteristics of micrometer-sized sand particles entering the wellbore, detailing the influence of key parameters such as sand particle size, sand ratio, wellbore deviation angle, fluid velocity, and fluid viscosity on the sand bed height. It establishes a predictive model for the deposition height of micrometer-sized sand particles. The model’s predicted results align well with experimental findings, and under the experimental conditions of this study, the model’s average prediction error for the sand bed height is 12.47%, indicating that the proposed model demonstrates a high level of accuracy in predicting the bed height. The research results can serve as a practical basis and engineering guidance for reducing the risk of natural gas hydrate and sand blockages, determining reasonable extraction procedures, and ensuring the safety of wellbore flow. Full article

This research explores how participants in an innovation ecosystem, operating without a focal firm, can collaboratively envision and create societal value beyond their individual business goals. Using participatory action research, the investigation focuses on two cases within the offshore wind energy sector, involving four complementary enterprises and nine enterprises that are both complementary and competitive. The findings suggest that ecosystem participants can collectively pursue opportunities for sustainable value creation that surpass the interests and goals of individual firms. This shift towards a future-oriented, ecosystem-wide perspective was driven by the focus on ecosystem-level value propositions and the dynamic organizing of heterogeneous knowledge, individual behaviors, and organizational behaviors, enabling successful future-oriented sensemaking. The research process highlights practices that led to significant innovation outcomes, such as halving investments, reducing accidents and rework, accelerating operational flow, and fostering long-term investments, like a floating port for installation and maintenance improvements. This study enhances understanding of how future-oriented sensemaking in innovation ecosystems without a focal firm can drive innovation and societal value creation, offering insights for practitioners, academics, and policymakers on governance and collaborative efforts to enable value creation in innovation ecosystems. 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

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