Showing papers in "Journal of Marine Science and Engineering in 2021"
TL;DR: A review of the transport and oil weathering processes and their parameterization can be found in this article, where the authors examine the state-of-the-art oil spill models in terms of their capacity to simulate these processes, and assess uncertainty in the produced predictions.
Abstract: Several oil spill simulation models exist in the literature, which are used worldwide to simulate the evolution of an oil slick created from marine traffic, petroleum production, or other sources These models may range from simple parametric calculations to advanced, new-generation, operational, three-dimensional numerical models, coupled to meteorological, hydrodynamic, and wave models, forecasting in high-resolution and with high precision the transport and fate of oil This study presents a review of the transport and oil weathering processes and their parameterization and critically examines eighteen state-of-the-art oil spill models in terms of their capacity (a) to simulate these processes, (b) to consider oil released from surface or submerged sources, (c) to assimilate real-time field data for model initiation and forcing, and (d) to assess uncertainty in the produced predictions Based on our review, the most common oil weathering processes involved are spreading, advection, diffusion, evaporation, emulsification, and dispersion The majority of existing oil spill models do not consider significant physical processes, such as oil dissolution, photo-oxidation, biodegradation, and vertical mixing Moreover, timely response to oil spills is lacking in the new generation of oil spill models Further improvements in oil spill modeling should emphasize more comprehensive parametrization of oil dissolution, biodegradation, entrainment, and prediction of oil particles size distribution following wave action and well blow outs
85 citations
TL;DR: In this article, a review paper examines the possible pathways and possible technologies available that will help the shipping sector achieve the International Maritime Organization's (IMO) deep decarbonization targets by 2050.
Abstract: This review paper examines the possible pathways and possible technologies available that will help the shipping sector achieve the International Maritime Organization’s (IMO) deep decarbonization targets by 2050. There has been increased interest from important stakeholders regarding deep decarbonization, evidenced by market surveys conducted by Shell and Deloitte. However, deep decarbonization will require financial incentives and policies at an international and regional level given the maritime sector’s ~3% contribution to green house gas (GHG) emissions. The review paper, based on research articles and grey literature, discusses technoeconomic problems and/or benefits for technologies that will help the shipping sector achieve the IMO’s targets. The review presents a discussion on the recent literature regarding alternative fuels (nuclear, hydrogen, ammonia, methanol), renewable energy sources (biofuels, wind, solar), the maturity of technologies (fuel cells, internal combustion engines) as well as technical and operational strategies to reduce fuel consumption for new and existing ships (slow steaming, cleaning and coating, waste heat recovery, hull and propeller design). The IMO’s 2050 targets will be achieved via radical technology shift together with the aid of social pressure, financial incentives, regulatory and legislative reforms at the local, regional and international level.
80 citations
TL;DR: The proposed formation generation algorithm implements an approach combining a virtual structure and artificial potential field (VSAPF), which provides a high accuracy of formation shape keeping and flexibility of formationshape change, which has the advantage of less calculations.
Abstract: This paper proposes a formation generation algorithm and formation obstacle avoidance strategy for multiple unmanned surface vehicles (USVs) The proposed formation generation algorithm implements an approach combining a virtual structure and artificial potential field (VSAPF), which provides a high accuracy of formation shape keeping and flexibility of formation shape change To solve the obstacle avoidance problem of the multi-USV system, an improved dynamic window approach is applied to the formation reference point, which considers the movement ability of the USV By applying this method, the USV formation can avoid obstacles while maintaining its shape The combination of the virtual structure and artificial potential field has the advantage of less calculations, so that it can ensure the real-time performance of the algorithm and convenience for deployment on an actual USV Various simulation results for a group of USVs are provided to demonstrate the effectiveness of the proposed algorithms
66 citations
TL;DR: In this article, a coupled SPH-FEM modeling approach is presented to simulate the fluid with particles, and the flume, the debris and the structure with mesh-based finite elements.
Abstract: Field surveys in recent tsunami events document the catastrophic effects of large waterborne debris on coastal infrastructure. Despite the availability of experimental studies, numerical studies investigating these effects are very limited due to the need to simulate different domains (fluid, solid), complex turbulent flows and multi-physics interactions. This study presents a coupled SPH–FEM modeling approach that simulates the fluid with particles, and the flume, the debris and the structure with mesh-based finite elements. The interaction between the fluid and solid bodies is captured via node-to-solid contacts, while the interaction of the debris with the flume and the structure is defined via a two-way segment-based contact. The modeling approach is validated using available large-scale experiments in the literature, in which a restrained shipping container is transported by a tsunami bore inland until it impacts a vertical column. Comparison of the experimental data with the two-dimensional numerical simulations reveals that the SPH–FEM models can predict (i) the non-linear transformation of the tsunami wave as it propagates towards the coast, (ii) the debris–fluid interaction and (iii) the impact on a coastal structure, with reasonable accuracy. Following the validation of the models, a limited investigation was conducted, which demonstrated the generation of significant debris pitching that led to a non-normal impact on the column with a reduced contact area and impact force. While the exact level of debris pitching is highly dependent on the tsunami characteristics and the initial water depth, it could potentially result in a non-linear force–velocity trend that has not been considered to date, highlighting the need for further investigation preferably with three-dimensional models.
54 citations
TL;DR: A coastal ship path planning model based on the optimized deep Q network (DQN) algorithm that can plan the optimal path in line with the actual navigation rules, and improve the safety, economy and autonomous decision-making ability of ship navigation is proposed.
Abstract: Path planning is a key issue in the field of coastal ships, and it is also the core foundation of ship intelligent development. In order to better realize the ship path planning in the process of navigation, this paper proposes a coastal ship path planning model based on the optimized deep Q network (DQN) algorithm. The model is mainly composed of environment status information and the DQN algorithm. The environment status information provides training space for the DQN algorithm and is quantified according to the actual navigation environment and international rules for collision avoidance at sea. The DQN algorithm mainly includes four components which are ship state space, action space, action exploration strategy and reward function. The traditional reward function of DQN may lead to the low learning efficiency and convergence speed of the model. This paper optimizes the traditional reward function from three aspects: (a) the potential energy reward of the target point to the ship is set; (b) the reward area is added near the target point; and (c) the danger area is added near the obstacle. Through the above optimized method, the ship can avoid obstacles to reach the target point faster, and the convergence speed of the model is accelerated. The traditional DQN algorithm, A* algorithm, BUG2 algorithm and artificial potential field (APF) algorithm are selected for experimental comparison, and the experimental data are analyzed from the path length, planning time, number of path corners. The experimental results show that the optimized DQN algorithm has better stability and convergence, and greatly reduces the calculation time. It can plan the optimal path in line with the actual navigation rules, and improve the safety, economy and autonomous decision-making ability of ship navigation.
51 citations
TL;DR: An overview of the main factors of control-oriented models and control strategies for AUVs is presented and the acceptability of the reported modeling and control techniques is established.
Abstract: Autonomous underwater vehicles (AUVs) have been widely used to perform underwater tasks. Due to the environmental disturbances, underactuated problems, system constraints, and system coupling, AUV trajectory tracking control is challenging. Thus, further investigation of dynamic characteristics and trajectory tracking control methods of the AUV motion system will be of great importance to improve underwater task performance. An AUV controller must be able to cope with various challenges with the underwater vehicle, adaptively update the reference model, and overcome unexpected deviations. In order to identify modeling strategies and the best control practices, this paper presents an overview of the main factors of control-oriented models and control strategies for AUVs. In modeling, two fields are considered: (i) models that come from simplifications of Fossen’s equations; and (ii) system identification models. For each category, a brief description of the control-oriented modeling strategies is given. In the control field, three relevant aspects are considered: (i) significance of AUV trajectory tracking control, (ii) control strategies; and (iii) control performance. For each aspect, the most important features are explained. Furthermore, in the aspect of control strategies, mathematical modeling study and physical experiment study are introduced in detail. Finally, with the aim of establishing the acceptability of the reported modeling and control techniques, as well as challenges that remain open, a discussion and a case study are presented. The literature review shows the development of new control-oriented models, the research in the estimation of unknown inputs, and the development of more innovative control strategies for AUV trajectory tracking systems are still open problems that must be addressed in the short term.
46 citations
TL;DR: In this article, the authors proposed the Coastal Vulnerability index (CVI) which accounts for all relevant variables that characterize the coastal environment dealing with: (i) forcing actions (waves, tidal range, sea-level rise, etc.), (ii) morphological characteristics (geomorphology, foreshore slope, dune features, etc.).
Abstract: Coastal area constitutes a vulnerable environment and requires special attention to preserve ecosystems and human activities therein. To this aim, many studies have been devoted both in past and recent years to analyzing the main factors affecting coastal vulnerability and susceptibility. Among the most used approaches, the Coastal Vulnerability Index (CVI) accounts for all relevant variables that characterize the coastal environment dealing with: (i) forcing actions (waves, tidal range, sea-level rise, etc.), (ii) morphological characteristics (geomorphology, foreshore slope, dune features, etc.), (iii) socio-economic, ecological and cultural aspects (tourism activities, natural habitats, etc.). Each variable is evaluated at each portion of the investigated coast, and associated with a vulnerability level which usually ranges from 1 (very low vulnerability), to 5 (very high vulnerability). Following a susceptibility/vulnerability analysis of a coastal stretch, specific strategies must be chosen and implemented to favor coastal resilience and adaptation, spanning from hard solutions (e.g., groins, breakwaters, etc.) to soft solutions (e.g., beach and dune nourishment projects), to the relocation option and the establishment of accommodation strategies (e.g., emergency preparedness).
42 citations
TL;DR: A ship AIS trajectory clustering method based on Hausdorff distance and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), which can adaptively cluster ship trajectories with their shape characteristics and has good clustering scalability is proposed.
Abstract: The Automatic Identification System (AIS) of ships provides massive data for maritime transportation management and related researches. Trajectory clustering has been widely used in recent years as a fundamental method of maritime traffic analysis to provide insightful knowledge for traffic management and operation optimization, etc. This paper proposes a ship AIS trajectory clustering method based on Hausdorff distance and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN), which can adaptively cluster ship trajectories with their shape characteristics and has good clustering scalability. On this basis, a re-clustering method is proposed and comprehensive clustering performance metrics are introduced to optimize the clustering results. The AIS data of the estuary waters of the Yangtze River in China has been utilized to conduct a case study and compare the results with three popular clustering methods. Experimental results prove that this method has good clustering results on ship trajectories in complex waters.
38 citations
TL;DR: In this article, the authors calculated a tsunami magnitude of Mt~7.0, a tsunami source area of 1960 km2 and a displacement amplitude of ~1 m in the tsunami source.
Abstract: The tsunami generated by the offshore Samos Island earthquake (Mw = 7.0, 30 October 2020) is the largest in the Aegean Sea since 1956 CE. Our study was based on field surveys, video records, eyewitness accounts and far-field mareograms. Sea recession was the leading motion in most sites implying wave generation from seismic dislocation. At an epicentral distance of ~12 km (site K4, north Samos), sea recession, followed by extreme wave height (h~3.35 m), occurred 2′ and 4′ after the earthquake, respectively. In K4, the main wave moved obliquely to the coast. These features may reflect coupling of the broadside tsunami with landslide generated tsunami at offshore K4. The generation of an on-shelf edge-wave might be an alternative. A few kilometers from K4, a wave height of ~1 m was measured in several sites, except Vathy bay (east, h = 2 m) and Karlovasi port (west, h = 1.80 m) where the wave amplified. In Vathy bay, two inundations arrived with a time difference of ~19′, the second being the strongest. In Karlovasi, one inundation occurred. In both towns and in western Turkey, material damage was caused in sites with h > 1 m. In other islands, h ≤ 1 m was reported. The h > 0.5 m values follow power-law decay away from the source. We calculated a tsunami magnitude of Mt~7.0, a tsunami source area of 1960 km2 and a displacement amplitude of ~1 m in the tsunami source. A co-seismic 15–25 cm coastal uplift of Samos decreased the tsunami run-up. The early warning message perhaps contributed to decrease the tsunami impact.
38 citations
TL;DR: Improved the YOLOv5s algorithm, in which the initial frame of the target is re-clustered by K-means at the data input end, the receptive field area is expanded at the output end, and the loss function is optimized, shows that the improved model can realize the detection and identification of multiple types of ships.
Abstract: In order to realize the real-time detection of an unmanned fishing speedboat near a ship ahead, a perception platform based on a target visual detection system was established. By controlling the depth and width of the model to analyze and compare training, it was found that the 5S model had a fast detection speed but low accuracy, which was judged to be insufficient for detecting small targets. In this regard, this study improved the YOLOv5s algorithm, in which the initial frame of the target is re-clustered by K-means at the data input end, the receptive field area is expanded at the output end, and the loss function is optimized. The results show that the precision of the improved model’s detection for ship images was 98.0%, and the recall rate was 96.2%. Mean average precision (mAP) reached 98.6%, an increase of 4.4% compared to before the improvements, which shows that the improved model can realize the detection and identification of multiple types of ships, laying the foundation for subsequent path planning and automatic obstacle avoidance of unmanned ships.
37 citations
TL;DR: In this article, a review of machine learning methods for predicting harmful algal blooms and shellfish biotoxin contamination is presented, with a particular focus on autoregressive models, support vector machines, random forest, probabilistic graphical models and artificial neural networks.
Abstract: Harmful algal blooms (HABs) are among the most severe ecological marine problems worldwide. Under favorable climate and oceanographic conditions, toxin-producing microalgae species may proliferate, reach increasingly high cell concentrations in seawater, accumulate in shellfish, and threaten the health of seafood consumers. There is an urgent need for the development of effective tools to help shellfish farmers to cope and anticipate HAB events and shellfish contamination, which frequently leads to significant negative economic impacts. Statistical and machine learning forecasting tools have been developed in an attempt to better inform the shellfish industry to limit damages, improve mitigation measures and reduce production losses. This study presents a synoptic review covering the trends in machine learning methods for predicting HABs and shellfish biotoxin contamination, with a particular focus on autoregressive models, support vector machines, random forest, probabilistic graphical models, and artificial neural networks (ANN). Most efforts have been attempted to forecast HABs based on models of increased complexity over the years, coupled with increased multi-source data availability, with ANN architectures in the forefront to model these events. The purpose of this review is to help defining machine learning-based strategies to support shellfish industry to manage their harvesting/production, and decision making by governmental agencies with environmental responsibilities.
TL;DR: In this paper, simulations of a ship travelling on a given oceanic route were performed by a weather routing system to provide a large realistic navigation data set, which could represent a collection of data obtained on board a ship in operation in order to predict ship speed and fuel consumption.
Abstract: In this paper, simulations of a ship travelling on a given oceanic route were performed by a weather routing system to provide a large realistic navigation data set, which could represent a collection of data obtained on board a ship in operation. This data set was employed to train a neural network computing system in order to predict ship speed and fuel consumption. The model was trained using the Levenberg–Marquardt backpropagation scheme to establish the relation between the ship speed and the respective propulsion configuration for the existing sea conditions, i.e., the output torque of the main engine, the revolutions per minute of the propulsion shaft, the significant wave height, and the peak period of the waves, together with the relative angle of wave encounter. Additional results were obtained by also using the model to train the relationship between the same inputs used to determine the speed of the ship and the fuel consumption. A sensitivity analysis was performed to analyze the artificial neural network capability to forecast the ship speed and fuel oil consumption without information on the status of the engine (the revolutions per minute and torque) using as inputs only the information of the sea state. The results obtained with the neural network model show very good accuracy both in the prediction of the speed of the vessel and the fuel consumption.
TL;DR: A methodology for predicting the ship’s trajectory that can be used for an intelligent collision avoidance algorithm at sea is proposed using the bidirectional long short-term memory (Bi-LSTM), and the prediction accuracy was found to be the highest compared to that of LSTM and GRU.
Abstract: According to the statistics of maritime accidents, most collision accidents have been caused by human factors. In an encounter situation, the prediction of ship’s trajectory is a good way to notice the intention of the other ship. This paper proposes a methodology for predicting the ship’s trajectory that can be used for an intelligent collision avoidance algorithm at sea. To improve the prediction performance, the density-based spatial clustering of applications with noise (DBSCAN) has been used to recognize the pattern of the ship trajectory. Since the DBSCAN is a clustering algorithm based on the density of data points, it has limitations in clustering the trajectories with nonlinear curves. Thus, we applied the spectral clustering method that can reflect a similarity between individual trajectories. The similarity measured by the longest common subsequence (LCSS) distance. Based on the clustering results, the prediction model of ship trajectory was developed using the bidirectional long short-term memory (Bi-LSTM). Moreover, the performance of the proposed model was compared with that of the long short-term memory (LSTM) model and the gated recurrent unit (GRU) model. The input data was obtained by preprocessing techniques such as filtering, grouping, and interpolation of the automatic identification system (AIS) data. As a result of the experiment, the prediction accuracy of Bi-LSTM was found to be the highest compared to that of LSTM and GRU.
TL;DR: An updated overview of diesel hydrocarbon degradation, the effects of oil spills on the environment and living organisms, and the potential role of high salinity bacteria to decontaminate the organic pollutants in the water environment is provided.
Abstract: Oil pollution can cause tremendous harm and risk to the water ecosystem and organisms due to the relatively recalcitrant hydrocarbon compounds The current chemical method used to treat the ecosystem polluted with diesel is incompetent and expensive for a large-scale treatment Thus, bioremediation technique seems urgent and requires more attention to solve the existing environmental problems Biological agents, including microorganisms, carry out the biodegradation process where organic pollutants are mineralized into water, carbon dioxide, and less toxic compounds Hydrocarbon-degrading bacteria are ubiquitous in the nature and often exploited for their specialty to bioremediate the oil-polluted area The capability of these bacteria to utilize hydrocarbon compounds as a carbon source is the main reason behind their species exploitation Recently, microbial remediation by halophilic bacteria has received many positive feedbacks as an efficient pollutant degrader These halophilic bacteria are also considered as suitable candidates for bioremediation in hypersaline environments However, only a few microbial species have been isolated with limited available information on the biodegradation of organic pollutants by halophilic bacteria The fundamental aspect for successful bioremediation includes selecting appropriate microbes with a high capability of pollutant degradation Therefore, high salinity bacteria are remarkable microbes for diesel degradation This paper provides an updated overview of diesel hydrocarbon degradation, the effects of oil spills on the environment and living organisms, and the potential role of high salinity bacteria to decontaminate the organic pollutants in the water environment
TL;DR: In this article, a comprehensive review of different algorithms for tracking maximum power point, and capturing maximized output power from the wind energy conversion system (WECS) is presented, considering the parameters like complexity, convergence speed, use of sensors, memory requirement, need for knowledge of system parameters.
Abstract: Renewable energy resources are gaining a lot of popularity. Several researchers have worked on the tracking and extraction of energy from these sources. In the past few decades, among the available green energy resources, wind energy has been the most attractive option among the resources available. It is imperative to use the maximum power available in the wind to achieve the wind turbine (WT) operation at maximum power. The maximum power point tracking (MPPT) algorithms are a pioneer in this context. Many research papers are contributed in this domain which necessitates a thorough review while choosing an appropriate technique. This paper comprehensively focuses on reviewing different algorithms in the past and present for tracking maximum power point, and capturing maximized output power from the wind energy conversion system (WECS). In this paper, the algorithms are classified based on the direct and indirect power measurement, hybrid and smart algorithms for tracking maximum power point, and they are compared, considering the parameters like complexity, convergence speed, use of sensors, memory requirement, need for knowledge of system parameters, etc. The immense popularity of the different versions of perturb and observe (P&O) based algorithms due to their various features is evident from the literature. The review reveals that the hybrid maximum power point tracking algorithms can use the advantages of the conventional methods and eliminate their drawbacks.
TL;DR: This paper surveys the current collision-free MAC protocols proposed in the literature for UWSNs and presents a qualitative comparison of these strategies and also discusses some possible future directions.
Abstract: The Medium Access Control (MAC) layer protocol is the most important part of any network, and is considered to be a fundamental protocol that aids in enhancing the performance of networks and communications. However, the MAC protocol’s design for underwater sensor networks (UWSNs) has introduced various challenges. This is due to long underwater acoustic propagation delay, high mobility, low available bandwidth, and high error probability. These unique acoustic channel characteristics make contention-based MAC protocols significantly more expensive than other protocol contentions. Therefore, re-transmission and collisions should effectively be managed at the MAC layer to decrease the energy cost and to enhance the network’s throughput. Consequently, handshake-based and random access-based MAC protocols do not perform as efficiently as their achieved performance in terrestrial networks. To tackle this complicated problem, this paper surveys the current collision-free MAC protocols proposed in the literature for UWSNs. We first review the unique characteristic of underwater sensor networks and its negative impact on the MAC layer. It is then followed by a discussion about the problem definition, challenges, and features associated with the design of MAC protocols in UWANs. Afterwards, currently available collision-free MAC design strategies in UWSNs are classified and investigated. The advantages and disadvantages of each design strategy along with the recent advances are then presented. Finally, we present a qualitative comparison of these strategies and also discuss some possible future directions.
TL;DR: This paper describes how the visual perception tasks required for marine surveillance with those required for intelligent ship navigation to form a marine computer vision-based situational awareness complex and investigated the key technologies they have in common.
Abstract: The primary task of marine surveillance is to construct a perfect marine situational awareness (MSA) system that serves to safeguard national maritime rights and interests and to maintain blue homeland security. Progress in maritime wireless communication, developments in artificial intelligence, and automation of marine turbines together imply that intelligent shipping is inevitable in future global shipping. Computer vision-based situational awareness provides visual semantic information to human beings that approximates eyesight, which makes it likely to be widely used in the field of intelligent marine transportation. We describe how we combined the visual perception tasks required for marine surveillance with those required for intelligent ship navigation to form a marine computer vision-based situational awareness complex and investigated the key technologies they have in common. Deep learning was a prerequisite activity. We summarize the progress made in four aspects of current research: full scene parsing of an image, target vessel re-identification, target vessel tracking, and multimodal data fusion with data from visual sensors. The paper gives a summary of research to date to provide background for this work and presents brief analyses of existing problems, outlines some state-of-the-art approaches, reviews available mainstream datasets, and indicates the likely direction of future research and development. As far as we know, this paper is the first review of research into the use of deep learning in situational awareness of the ocean surface. It provides a firm foundation for further investigation by researchers in related fields.
TL;DR: In this paper, a laboratory method for determining compatibility and stability of residual fuel components has been developed, which makes it possible to determine the quantitative characteristics of the sediment formation activity and to preserve the quality and reduce sediment formation during transshipment, storage and transportation of marine residual fuels.
Abstract: The article shows studies of the problem of active sediment formation during mixing of residual fuels, caused by the manifestation of incompatibility. To preserve the quality and reduce sediment formation during transshipment, storage, and transportation of marine residual fuels, a laboratory method for determining the compatibility and stability of fuels has been developed, which makes it possible to determine the quantitative characteristics of the sediment formation activity. According to the method developed, laboratory studies have been carried out to determine incompatible fuel components and the influence of composition on the sedimentation process. Tests were carried out to determine the quality indicators and the individual group composition of the fuel samples. Based on the results of the studies, the dependences of the influence of normal structure paraffins in the range from 55 to 70 wt. % and asphaltenes in the range from 0.5 to 3.5 wt. % in the fuel composition on the sedimentation activity due to incompatibility were obtained. To obtain a convenient tool that is applicable in practice, a nomogram has been developed on the basis of the dependences obtained experimentally. It was also determined that, after reaching the maximum values of sediment formation with a further increase in the content of n-paraffins, saturation is observed, and the value of the sediment content remains at the same level. Maximum total sediment values have been found to depend on asphaltene content and do not significantly exceed them within 10%. The results of the research presented in this article allow laboratory and calculation to determine the possibility of incompatibility and to preserve the quality of marine residual fuels.
TL;DR: In this article, the authors analyze the scientific production from 1986 to 2020 in impact journals of the terms "nautical tourism", "maritime tourism" and "marine tourism" considering the following variables: number of documents, number of articles, period being studied, Hirsch citations and index.
Abstract: Tourism related to the sea and boating activities is becoming increasingly popular and revolves around a range of leisure, water sports, nautical or other maritime activities. This article studies the main scientific contributions in this area, bearing in mind the complexity of finding a suitable definition of this concept. Hence, the aim of this paper is to analyze the scientific production from 1986 to 2020 in impact journals of the terms “nautical tourism”, “maritime tourism” and “marine tourism” considering the following variables: number of documents, number of articles, period being studied, Hirsch citations and index. The results show an increasing trend in terms of both the number of published articles and citations publications from 2007 onwards and the review of the literature raises the need to define a new concept: “blue tourism”. Future trends in research include terms such as tourist ports, quality of websites and blue economy.
TL;DR: In this article, a review analyzes the recent advances in adsorption and desorption studies of different contaminants species, both organic and metallic, on MPs made of Poly(Ethylene terephthalate).
Abstract: Marine pollution is one of the biggest environmental problems, mainly due to single-use or disposable plastic waste fragmenting into microplastics (MPs) and nanoplastics (NPs) and entering oceans from the coasts together with human-made MPs. A rapidly growing worry concerning environmental and human safety has stimulated research interest in the potential risks induced by the chemicals associated with MPs/NPs. In this framework, the present review analyzes the recent advances in adsorption and desorption studies of different contaminants species, both organic and metallic, on MPs made of Poly(Ethylene terephthalate). The choice of PET is motivated by its great diffusion among plastic items and, unfortunately, also in marine plastic pollution. Due to the ubiquitous presence of PET MPS/NPs, the interest in its role as a vector of contaminants has abruptly increased in the last three years, as demonstrated by the very high number of recent papers on sorption studies in different environments. The present review relies on a chemical engineering approach aimed at providing a deeper overview of both the sorption mechanisms of organic and metal contaminants to PET MPs/NPs and the most used adsorption kinetic models to predict the mass transfer process from the liquid phase to the solid adsorbent.
TL;DR: Experimental results show that the proposed algorithm can plan a series of feasible ship routes to ensure safety, greenness, and economy and that it provides route selection references for captains and shipping companies.
Abstract: With the continuous prosperity and development of the shipping industry, it is necessary and meaningful to plan a safe, green, and efficient route for ships sailing far away. In this study, a hybrid multicriteria ship route planning method based on improved particle swarm optimization–genetic algorithm is presented, which aims to optimize the meteorological risk, fuel consumption, and navigation time associated with a ship. The proposed algorithm not only has the fast convergence of the particle swarm algorithm but also improves the diversity of solutions by applying the crossover operation, selection operation, and multigroup elite selection operation of the genetic algorithm and improving the Pareto optimal frontier distribution. Based on the Pareto optimal solution set obtained by the algorithm, the minimum-navigation-time route, the minimum-fuel-consumption route, the minimum-navigation-risk route, and the recommended route can be obtained. Herein, a simulation experiment is conducted with respect to a container ship, and the optimization route is compared and analyzed. Experimental results show that the proposed algorithm can plan a series of feasible ship routes to ensure safety, greenness, and economy and that it provides route selection references for captains and shipping companies.
TL;DR: A new reference spectrum model is proposed that retains the power-law dependence on speed and length but incorporates class-specific reference speeds and new spectrum coefficients, and calculates the ship source level spectrum, in decidecade bands, as a function of frequency, speed, length, and AIS ship type.
Abstract: Underwater sound mapping is increasingly being used as a tool for monitoring and managing noise pollution from shipping in the marine environment. Sound maps typically rely on tracking data from the Automated Information System (AIS), but information available from AIS is limited and not easily related to vessel noise emissions. Thus, robust sound mapping tools not only require accurate models for estimating source levels for large numbers of marine vessels, but also an objective assessment of their uncertainties. As part of the Joint Monitoring Programme for Ambient Noise in the North Sea (JOMOPANS) project, a widely used reference spectrum model (RANDI 3.1) was validated against statistics of monopole ship source level measurements from the Vancouver Fraser Port Authority-led Enhancing Cetacean Habitat and Observation (ECHO) Program. These validation comparisons resulted in a new reference spectrum model (the JOMOPANS-ECHO source level model) that retains the power-law dependence on speed and length but incorporates class-specific reference speeds and new spectrum coefficients. The new reference spectrum model calculates the ship source level spectrum, in decidecade bands, as a function of frequency, speed, length, and AIS ship type. The statistical uncertainty (standard deviation of the deviation between model and measurement) in the predicted source level spectra of the new model is estimated to be 6 dB.
TL;DR: In this paper, the authors applied the LightGBM model to predict the water levels along the lower reach of the Columbia River along with the discharges from two upstream rivers (Columbia and Willamette Rivers) and the tide characteristics, including the tide range at the estuary mouth (Astoria) and tide constituents.
Abstract: Due to the strong nonlinear interaction with river discharge, tides in estuaries are characterised as nonstationary and their mechanisms are yet to be fully understood. It remains highly challenging to accurately predict estuarine water levels. Machine learning methods, which offer a unique ability to simulate the unknown relationships between variables, have been increasingly used in a large number of research areas. This study applies the LightGBM model to predicting the water levels along the lower reach of the Columbia River. The model inputs consist of the discharges from two upstream rivers (Columbia and Willamette Rivers) and the tide characteristics, including the tide range at the estuary mouth (Astoria) and tide constituents. The model is optimized with the selected parameters. The results show that the LightGBM model can achieve high prediction accuracy, with the root-mean-square-error values of water level being reduced to 0.14 m and the correlation coefficient and skill score being in the ranges of 0.975–0.987 and 0.941–0.972, respectively, which are statistically better than those obtained from physics-based models such as the nonstationary tidal harmonic analysis model (NS_TIDE). The importance of subtide constituents in interacting with the river discharge in the estuary is clearly revealed from the model results.
TL;DR: Models that can predict fuel consumption using in-service data collected from a 13,000 TEU class container ship, along with statistical and domain-knowledge methods to select the proper input variables for the models are presented to prevent overfitting and multicollinearity while providing practical applicability.
Abstract: As interest in eco-friendly ships increases, methods for status monitoring and forecasting using in-service data from ships are being developed. Models for predicting the energy efficiency of a ship in real time need to effectively process the operational data and be optimized for such an application. This paper presents models that can predict fuel consumption using in-service data collected from a 13,000 TEU class container ship, along with statistical and domain-knowledge methods to select the proper input variables for the models. These methods prevent overfitting and multicollinearity while providing practical applicability. To implement the prediction model, either an artificial neural network (ANN) or multiple linear regression (MLR) were applied, where the ANN-based models showed the best prediction accuracy for both variable selection methods. The goodness of fit of the models based on ANN ranged from 0.9709 to 0.9936. Furthermore, sensitivity analysis of the draught under normal operating conditions indicated an optimal draught of 14.79 m, which was very close to the design draught of the target ship, and provides the optimal fuel consumption efficiency. These models could provide valuable information for ship operators to support decision making to maintain efficient operating conditions.
TL;DR: In this article, the authors investigated the descriptive spatial variability and trends of MHW events and their main characteristics of the Eastern Mediterranean Sea (EMED) from 1982 to 2020 using Sea Surface Temperature (SST) data obtained from the National Oceanic and Atmospheric Administration Optimum Interpolation ([NOAA] OI SST V2.1).
Abstract: Marine heatwaves (MHWs) can cause devastating impacts on marine life. The frequency of MHWs, gauged with respect to historical temperatures, is expected to rise significantly as the climate continues to warm. The MHWs intensity and count are pronounced with many parts of the oceans and semi enclosed seas, such as Eastern Mediterranean Sea (EMED). This paper investigates the descriptive spatial variability and trends of MHW events and their main characteristics of the EMED from 1982 to 2020 using Sea Surface Temperature (SST) data obtained from the National Oceanic and Atmospheric Administration Optimum Interpolation ([NOAA] OI SST V2.1). Over the last two decades, we find that the mean MHW frequency and duration increased by 40% and 15%, respectively. In the last decade, the shortest significant MHW mean duration is 10 days, found in the southern Aegean Sea, while it exceeds 27 days off the Israeli coast. The results demonstrate that the MHW frequency trend increased by 1.2 events per decade between 1982 and 2020, while the MHW cumulative intensity (icum) trend increased by 5.4 °C days per decade. During the study period, we discovered that the maximum significant MHW SST event was 6.35 °C above the 90th SST climatology threshold, lasted 7 days, and occurred in the year 2020. It was linked to a decrease in wind stress, an increase in air temperature, and an increase in mean sea level pressure.
TL;DR: In this article, a co-seismic response of Samos coastal zone to the 30th October 2020 earthquake provides a basis for understanding the complex tectonics of this area.
Abstract: On 30th October 2020, the eastern Aegean Sea was shaken by a Mw = 7.0 earthquake. The epicenter was located near the northern coasts of Samos island. This tectonic event produced an uplift of the whole island as well as several cases of infrastructure damage, while a small tsunami followed the mainshock. Underwater and coastal geological, geomorphological, biological observations and measurements were performed at the entire coast revealing a complex character for the uplift. At the northwestern part of the island, maximum vertical displacements of +35 ± 5 cm were recorded at the northwestern tip, at Agios Isidoros. Conversely, the southeastern part was known for its subsidence through submerged archaeological remains and former sea level standstills. The 2020 underwater survey unveiled uplifted but still drowned sea level indicators. The vertical displacement at the south and southeastern part ranges between +23 ± 5 and +8 ± 5 cm suggesting a gradual fading of the uplift towards the east. The crucial value of tidal notches, as markers of co-seismic events, was validated from the outcome of this study. The co-seismic response of Samos coastal zone to the 30th October earthquake provides a basis for understanding the complex tectonics of this area.
TL;DR: The experimental results of real ship data show that the proposed Bi-LSTM-TPA combined model has a significant reduction in MAPE, MAE, and MSE compared with the LSTM model and the SVM model, which verifies the effectiveness of the proposed algorithm.
Abstract: When ships sail on the sea, the changes of ship motion attitude presents the characteristics of nonlinearity and high randomness. Aiming at the problem of low accuracy of ship roll angle prediction by traditional prediction algorithms and single neural network model, a ship roll angle prediction method based on bidirectional long short-term memory network (Bi-LSTM) and temporal pattern attention mechanism (TPA) combined deep learning model is proposed. Bidirectional long short-term memory network extracts time features from the forward and reverse of the ship roll angle time series, and temporal pattern attention mechanism extracts the time patterns from the deep features of a bidirectional long short-term memory network output state that are beneficial to ship roll angle prediction, ignore other features that contribute less to the prediction. The experimental results of real ship data show that the proposed Bi-LSTM-TPA combined model has a significant reduction in MAPE, MAE, and MSE compared with the LSTM model and the SVM model, which verifies the effectiveness of the proposed algorithm.
TL;DR: A velocity obstacle-based risk measurement is applied to measure the risk of collision between multiple ships from the velocity perspective, based on which, the collision risk and the complexity of the encounter situation are obtained at the same time.
Abstract: Maritime accidents such as ship collisions pose continuous risks to individuals and society with due to their severe consequences on human life, economic and environmental losses, etc. Supervising the maritime traffic in the different regions and maintaining its safety level is an essential task for stakeholders such as maritime safety administrations. In this research, a new ship collision risk analysis method is developed with the utilisation of AIS (Automatic Identification System) data. A velocity obstacle-based risk measurement is applied to measure the risk of collision between multiple ships from the velocity perspective, based on which, the collision risk and the complexity of the encounter situation are obtained at the same time. Secondly, a density-based clustering technique is introduced to identify the hotspots of ship traffic in the region as an indicator for maritime safety operators. A case study using historical AIS data was implemented to verify the effectiveness of the proposed approach in a manner that simulates the real-time data scenario. Furthermore, a comparison between existing risk analysis method is conducted to validate the proposed method.
TL;DR: In this paper, the authors provide a broad overview of the latest improvements acquired on this topic, which would otherwise be difficult to obtain by the scientific and general professional community, and summarise the key challenges and recent advances related to offshore wind turbine scour protections.
Abstract: The offshore wind is the sector of marine renewable energy with the highest commercial development at present. The margin to optimise offshore wind foundations is considerable, thus attracting both the scientific and the industrial community. Due to the complexity of the marine environment, the foundation of an offshore wind turbine represents a considerable portion of the overall investment. An important part of the foundation’s costs relates to the scour protections, which prevent scour effects that can lead the structure to reach the ultimate and service limit states. Presently, the advances in scour protections design and its optimisation for marine environments face many challenges, and the latest findings are often bounded by stakeholder’s strict confidential policies. Therefore, this paper provides a broad overview of the latest improvements acquired on this topic, which would otherwise be difficult to obtain by the scientific and general professional community. In addition, this paper summarises the key challenges and recent advances related to offshore wind turbine scour protections. Knowledge gaps, recent findings and prospective research goals are critically analysed, including the study of potential synergies with other marine renewable energy technologies, as wave and tidal energy. This research shows that scour protections are a field of study quite challenging and still with numerous questions to be answered. Thus, optimisation of scour protections in the marine environment represents a meaningful opportunity to further increase the competitiveness of marine renewable energies.