Marine Ship Research Articles

N-PMI modified PAZ nanocomposite coatings with self-healing function for anticorrosion and antifouling applications

With the rapid development of the modern industrial marine economy, metal corrosion and biological fouling of marine ships and engineering equipment have become the main problems affecting the development of the marine industry. Traditional single-function coatings have only anti-corrosion or anti-fouling properties, which cannot meet the current needs of marine industry development for coating protection. In this study, a self-healing coating with good anticorrosion and antifouling properties has been prepared upon functionalization of a film forming resin and modification of a nanofiller. Initially, a modified acrylic resin (PAZ/N-PMI) was successfully synthesized by inserting zinc ions and a structural functional monomer (N-phenyl maleimide; N-PMI) into the polymer chain of an acrylic resin using free radical solution polymerization. A fluorinated silica coated hydroxylated multi-walled carbon nanotubes nanocomposite (F-MWCNTs-OH@SiO2) was dispersed in PAZ/N-PMI resin to prepare the composite coating samples. The composite coating showed good performance, in which the antibacterial rate of the composite coating against Staphylococcus aureus and Escherichia coli was >99.98 %. When F-MWCNTs-OH@SiO2 was 3 wt%, the Zf=0.01Hz of the composite coating reached the highest value, which was 3 orders of magnitude higher than that of the PAZ/N-PMI coating. At the same time, the prepared composite coating also exhibited good self-healing properties. This study provides a broad application prospect for the development of high quality marine anti-corrosion and anti-fouling organic coatings.

Can the International Regulatory Framework on Ships’ Routing, Ship Reporting, and Vessel Traffic Service (VTS) Accommodate Marine Autonomous Surface Ships (MASS)?

The recent maiden voyage of the Yara Birkeland witnessed yet another development in autonomy that is transforming the maritime sector. Marine autonomous surface ships (MASS) are claimed to bring many opportunities to society at large, not least in terms of operational efficiency and safety of the crew, fewer emissions, and greener shipping. On the assumption that MASS will prove safe enough to ply our seas and oceans, this article investigates the flexibility and ability of the existing International Maritime Organization (IMO) regulations on ships’ routing, ship reporting, and vessel traffic service (VTS) to respond to the technological developments, allowing for the operation of both remotely controlled ships without seafarers on board and fully autonomous ships. It argues that the regulations in question are largely supportive of autonomy. Challenges, however, exist when it comes to the employment of fully autonomous ships and the effective use of VTS.

A three-stage multi-objective heterogeneous integrated model with decomposition-reconstruction mechanism and adaptive segmentation error correction method for ship motion multi-step prediction

Reliable and accurate ship motion prediction is essential for ship navigation at sea and marine operations. Although previous studies have yielded rich results in the field of ship motion prediction, most of them have ignored the importance of the dynamic characteristics of ship motion for constructing forecasting models. Besides, the limitations of the single model and the autocorrelation characteristics of the residual series are also unfavorable factors that hinder the forecasting performance. To fill these gaps, a multi-objective heterogeneous integration model based on decomposition-reconstruction mechanism and adaptive segmentation error correction method is proposed in this paper for ship motion multi-step prediction. Specifically, the proposed model is divided into three stages, which are decomposition-reconstruction mechanism, multi-objective heterogeneous integration model and adaptive segmentation error correction method. The effectiveness of the proposed model is verified using four sets of real ship motion data collected from two sites in the South China Sea. The evaluation results show that the proposed model can effectively improve the prediction performance and outperforms other traditional models and state-of-the-art models in the field of ship motion prediction. Prospectively, the model proposed in this study can be used as an effective aid to ship warning systems and has the potential for practical application in ship marine operations.

Characterization and modeling of greenwater overtopping of a sea-level deck

Greenwater overtopping occurs when the deck of a marine structure or ship is submerged by a large non-breaking wave. This overtopping wave develops into a fast-moving mass of water that can damage deck structures. In this work we use laboratory experiments to characterize the kinematics of the greenwater fronts, showing that there are two main classes of flow. One class is an essentially two-dimensional flow with a single wave front traversing the entire deck, while the second type has multiple wave fronts originating from all sides of the platform. We characterize each type with respect to wave conditions and describe the transition between the two types. We then discuss two modeling approaches that can be used to simulate the kinematic of the overtopping fronts. A simple linear model, based on linear wave theory and on the assumption of the overtopping as a dam break, to predict the type and the propagation of the overtopping wave on the deck. The second is a three-dimensional nonlinear numerical hydrodynamics model, based on Smoothed Particle Hydrodynamics method, which reproduces the geometry of the overtopping in the laboratory more accurately. We compare the outputs of these models with laboratory observations and discuss their applicability.

ИСПОЛЬЗОВАНИЕ АППАРАТА ИСКУССТВЕННЫХ НЕЙРОННЫХ СЕТЕЙ ДЛЯ ОБЕСПЕЧЕНИЯ НАВИГАЦИОННОЙ И ЭКОЛОГИЧЕСКОЙ БЕЗОПАСНОСТИ СУДОХОДСТВА В УСЛОВИЯХ СЛОЖНОЙ ДИНАМИЧЕСКОЙ ОБСТАНОВКИ

Particulars o f factors o f navigational and ecological safety on the example o f the Obb Bay when sailing in areas
 with the situation quickly changed. Apparatus o f artificial neural networks for operational situation appraisal
 in the nearest marine ship's zone has been suggested. Basic types o f neural network architecture suitable for
 analysis o f dynamic territorial situation has been also advised. Comparison o f artificial neural structure on
 parameter o f fast acting and exactness applicable for the task o f situation appraisal has been made. It shows the
 advantage o f recurrent neural network

Marine ship instance segmentation by deep neural networks using a global and local attention (GALA) mechanism.

Marine ships are the transport vehicle in the ocean and instance segmentation of marine ships is an accurate and efficient analysis approach to achieve a quantitative understanding of marine ships, for example, their relative locations to other ships or obstacles. This relative spatial information is crucial for developing unmanned ships to avoid crashing. Visible light imaging, e.g. using our smartphones, is an efficient way to obtain images of marine ships, however, so far there is a lack of suitable open-source visible light datasets of marine ships, which could potentially slow down the development of unmanned ships. To address the problem of insufficient datasets, here we built two instance segmentation visible light datasets of marine ships, MariBoats and MariBoatsSubclass, which could facilitate the current research on instance segmentation of marine ships. Moreover, we applied several existing instance segmentation algorithms based on neural networks to analyze our datasets, but their performances were not satisfactory. To improve the segmentation performance of the existing models on our datasets, we proposed a global and local attention mechanism for neural network models to retain both the global location and semantic information of marine ships, resulting in an average segmentation improvement by 4.3% in terms of mean average precision. Therefore, the presented new datasets and the new attention mechanism will greatly advance the marine ship relevant research and applications.

Developing an advanced reliability analysis framework for marine systems operations and maintenance

Power generation system reliability is of great concern for all ship operators irrespective of sector, as it provides the highest utility and ensures collective safety of operators, passengers, equipment and cargo. A novel approach to system reliability analysis using DFTA, FMECA and BBN applied to 4 DGs has been conducted. The outcomes provide insight on faults and component criticality to vessel maintenance and availability. Building from the understanding of how multiple factors can influenced maintenance in addition to routine or age-related wear and tear of machinery. This research looked into operators’ peculiar challenges regarding environment, operational demands and technology challenges that affects maintenance and system reliability. A framework based on outputs from DFTA minimal cut set, RPN from FMECA were used as inputs for BBN to analyse ship marine DG system availability. A BBN influence diagram was used to build a maintenance strategy DSS. Overall outcome for the maintenance strategy selection DSS indicates relatively high unavailability. Therefore, DGs with low availability were recommended to be on Corrective action and ConMon, while DGs with good availability were recommended to be on PMS.

Atmospheric dry deposition fluxes of trace metals over the Eastern China Marginal Seas: Impact of emission controls

Atmospheric deposition is an important exogenous input of trace metals to Eastern China Marginal Seas (ECMS), which is strongly affected by human activities. With emission control practices implemented in China, it still remains unknown what changes have taken place in the atmospheric dry depositions of the trace metals over ECMS. This study aimed to estimate the atmospheric dry depositions of Zn, Pb, Cu, and Cd over ECMS via Weather Research and Forecasting Model-Community Multiscale Air Quality Modeling System (WRF-CMAQ) in the two winter periods of January 2012 and January 2019 as well as to explore the impacts of emission control on the depositions. The anthropogenic metal emissions from China, the Korean Peninsula, Japan, and marine ships were investigated in this study. In 2012, the dry deposition fluxes of Zn, Pb, Cu, and Cd over ECMS were in the ranges of 0.50–3.4 μg m−2 d−1, 0.22–1.9 μg m−2 d−1, 0.14–0.90 μg m−2 d−1, and 12–88 ng m−2 d−1, respectively. The deposition fluxes of the four metals over Bohai Sea (BS) and Yellow Sea (YS) were 2–3 times those over East China Sea (ECS). Outflow of polluted air masses from East Asia increased the metal depositions by 3– 5-fold relative to clear days. Compared with 2012, a 5–85 % reduction in the metal depositions over ECMS were estimated in 2019, largest reductions were found over YS and BS. Meteorological variation was able to decrease or increase the metal depositions. However, the emission control only caused a reduction in the entire study region. The metal inputs to the sea were significantly lower from the ship emissions than from the continental anthropogenic emissions, although the proportion of the ship emissions in the total metal depositions rose slightly from 2012 to 2019.

Cooperative Path Tracking for Swarm of MASSs Based on Consensus Theory

At present, marine autonomous surface ships (MASSs) play a huge role in marine shipping, surveying and mapping, safeguarding the rights and interests of marine space, and other maritime tasks. The cooperative operation of a swarm of MASSs can extend the scope of operation of the MASSs, and thus allow them to carry out more complex tasks. Path tracking is an important problem for the control of a swarm of MASSs. In this paper, the control of underactuated MASSs is decoupled, to control the heading and speed, respectively. First of all, in the path tracking, the improved arc LOS guidance law is introduced, and the heading torque controller is designed, so that the MASS can track the reference path efficiently and accurately. Then the single-path guided path tracking without formation of the swarm of MASSs is studied, the reference path of the swarm center tracking is defined, and the heave thrust controller of the swarm of MASSs is designed based on consensus theory, so that the surge velocity of the MASS can tend towards consistentcy, and finally converge to the desired speed. Finally, the effectiveness of the proposed control strategy is verified by two groups of simulation experiments.

A literature review and future research agenda on fault detection and diagnosis studies in marine machinery systems

Fault detection and diagnostics (FDD) have great potential to enable safety, efficiency, and reliability measures of critical machinery systems. However, it is clear that there is a lack of systematic literature review to identify and classify the FDD studies conducted within the scope of marine engineering. This paper offers a systematic review of FDD models particular to marine machinery and systems. The numbers of 72 core articles were highlighted through a comprehensive literature review conducted in the 2002–2022 period. The studies are classified based on the mostly utilized methods such as data-driven, model-based, knowledge-based, and new generation-hybrid. In addition, new generation and hybrid methods are discussed in detail. The experimental environment (i.e. shipboard, labs, simulator) and technical details of the conducted studies are extensively discussed. While 56.94% of the examined studies are related to the main engine, 43.06% of them are related to auxiliary engines. In addition, the main and auxiliary engine studies are also divided into subject headings and examined in detail. Given the recent developments in green and smart maritime concepts, a future research agenda of the FDD studies on marine machinery systems is then pinpointed. Consequently, the study stimulates scholars interested in FDD while it enables innovative ideas for marine engineers, technology providers, ship operators, and maritime entrepreneurs.

Enhancement of Marine Lantern’s Visibility under High Haze Using AI Camera and Sensor-Based Control System

This thesis describes research to prevent maritime safety accidents by notifying navigational signs when sea fog and haze occur in the marine environment. Artificial intelligence, a camera sensor, an embedded board, and an LED marine lantern were used to conduct the research. A deep learning-based dehaze model was learned by collecting real marine environment and open haze image data sets. By applying this learned model to the original hazy images, we obtained clear dehaze images. Comparing those two images, the concentration level of sea fog was derived into the PSNR and SSIM values. The brightness of the marine lantern was controlled through serial communication with the derived PSNR and SSIM values in a realized sea fog environment. As a result, it was possible to autonomously control the brightness of the marine lantern according to the concentration of sea fog, unlike the current marine lanterns, which adjust their brightness manually. This novel-developed lantern can efficiently utilize power consumption while enhancing its visibility. This method can be used for other fog concentration estimation systems at the embedded board level, so that applicable for local weather expectations, UAM navigation, and autonomous driving for marine ships.

Microstructure, mechanical properties, and corrosion resistance of TiSiN coating prepared by FCVA technique with different N2 flow rates

TiSiN coatings have attracted extensive attention due to excellent performance in cutting tools, Marine engineering equipment and ship critical components. However, due to the many microparticles and micropores on the coating surface prepared by traditional PVD techniques, the performance of the coatings, especially corrosion resistance, is seriously affected. Therefore, the preparation of high quality TiSiN coatings is very important for the industrial applications. In this study, high-quality TiSiN coatings were prepared by FCVA technique. The effect of N2 flow rate on microstructure, mechanical properties and corrosion resistance of TiSiN coating was investigated. The results showed that all TiSiN coatings presented smooth and compact microstructure without cracks. Meanwhile, TiSiN coatings had a nanocomposite structure of amorphous Si3N4 encapsulated TiN crystallites and possessed a preferred orientation of TiN (200) plane. With the increase of N2 flow rate, the grain size and hardness increased first and then decreased. When N2 flow rate is 20 sccm, the hardness of TiSiN coating reached the maximum owing to the fine grain strengthening effect. Moreover, TiSiN coating deposited N2 flow rate of 40 sccm had the best corrosion resistance, with the highest charge transfer resistance and pore resistance, which was primarily related to the best surface quality.

Traffic, marine ships and nucleation as the main sources of ultrafine particles in suburban Shanghai, China

The NMF algorithm assigns PNSD to multiple sources at two Shanghai suburban sites. Main sources of UFPs linked to respiratory deposition are traffic nucleation and emissions, marine ship emissions and photochemical nucleation and growth.

SafePASS: A new chapter for passenger ship evacuation and marine emergency response

Despite the current high level of safety and the efforts to make passenger ships resilient to most fire and flooding scenarios, there are still gaps and challenges in the marine emergency response and ship evacuation processes. Those challenges arise from the fact that both processes are complex, multi-variable problems that rely on parameters involving not only people and technology but also procedural and managerial issues. SafePASS Project, funded under EU's Horizon 2020 Research and Innovation Programme, is set to radically redefine the evacuation processes by introducing new equipment, expanding the capabilities of legacy systems on-board, proposing new Life-Saving Appliances and ship layouts, and challenging the current international regulations, hence reducing the uncertainty, and increasing the efficiency in all the stages of ship evacuation and abandonment process.

ПИТАННЯ ВДОСКОНАЛЕННЯ КІБЕРБЕЗПЕКИ МОРСЬКОГО ТРАНСПОРТУ: ЗАРУБІЖНИЙ ДОСВІД

An analysis of modern national publications shows that, although in Ukraine considerable attention is being payed to the general organization of maritime transport security, the due interest has not yet been shown to the new important area, such as the maritime transport cybersecurity. Therefore, the article touches upon the best practices in organizing the cybersecurity of marine ships. The assertion that the general danger of maritime transport has increased significantly in recent years is based on the facts of the introduction of information technology into the operational technologies of maritime transport. The bottom line is that information technology is closely linked to the Internet, and this is what, in the first place, makes the operational technologies of maritime transport open to deliberate external interference, which, according to experts, can provide external control of the vessel with the aim of capsizing, colliding or causing environment disaster. The general concern of the leading maritime states about the situation is confirmed by a number of documents, such as Resolution of the Maritime Security Committee MSC.428 (98), adopted on June 16, 2017 and requiring member states to take into account the management of cyber risks. The paper analyzes a typical approach to the development of an action plan for the organization of maritime cybersecurity on board a ship. The preliminary preparation steps are considered, ranging from updating passwords and software to training the team. It is indicated that today, all over the world, the direct development of such a plan is recommended to be carried out on the basis of the “The Guidelines on Cyber Security onboard Ships”, developed and supported by leading international organizations related to maritime transport operations. The plan created on the basis of this guide, in essence, is to manage cyber risks and boils down to the following: identifying internal and external threats, internal and external vulnerabilities; risk exposure assessment; development of measures to reduce these vulnerabilities; developing contingency plans; developing plans for recovery measures after cyber incidents. The general rise in the culture of personnel to a modern level is a necessary element of ensuring cybersecurity in maritime transport.

A Multi-Scale Feature Pyramid Network for Detection and Instance Segmentation of Marine Ships in SAR Images

In the remote sensing field, synthetic aperture radar (SAR) is a type of active microwave imaging sensor working in all-weather and all-day conditions, providing high-resolution SAR images of objects such as marine ships. Detection and instance segmentation of marine ships in SAR images has become an important question in remote sensing, but current deep learning models cannot accurately quantify marine ships because of the multi-scale property of marine ships in SAR images. In this paper, we propose a multi-scale feature pyramid network (MS-FPN) to achieve the simultaneous detection and instance segmentation of marine ships in SAR images. The proposed MS-FPN model uses a pyramid structure, and it is mainly composed of two proposed modules, namely the atrous convolutional pyramid (ACP) module and the multi-scale attention mechanism (MSAM) module. The ACP module is designed to extract both the shallow and deep feature maps, and these multi-scale feature maps are crucial for the description of multi-scale marine ships, especially the small ones. The MSAM module is designed to adaptively learn and select important feature maps obtained from different scales, leading to improved detection and segmentation accuracy. Quantitative comparison of the proposed MS-FPN model with several classical and recently developed deep learning models, using the high-resolution SAR images dataset (HRSID) that contains multi-scale marine ship SAR images, demonstrated the superior performance of MS-FPN over other models.

Deep Learning Method for Evaporation Duct Inversion Based on GPS Signal

Accurate evaporation duct prediction is one of the critical technologies for realizing the over-the-horizon impact of marine communication, ship radar, and other systems. Using GPS signals to invert evaporation ducts provides more benefits in terms of method realization and ease. In order to invert the evaporation duct from GPS-received power data, a deep learning technique based on Bayesian optimization is proposed to increase the prediction accuracy of evaporation ducts. The evaporation duct propagation mechanism of the GPS signal is explored. The GPS-received power is estimated using the two-parameter evaporation duct model, and a better neural network structure is built using Bayesian optimization. The study results show that the Bayesian optimization model has a smaller root mean square error (RMSE) than the human empirical model, which allows for rapid and accurate inversion of duct parameters even in noisy interference.

Recognition of the Type of Marine Ship Based on Comparison with Normalized Reference Parameters of Radiolocation Signals

Recognition of the Type of Marine Ship Based on Comparison with Normalized Reference Parameters of Radiolocation Signals

Experimental Research of the Impact of Ship’s Rolling on the Performance of PV Panels

Abstract The aim of the International Maritime Organization (IMO) to reduce by half the amount of greenhouse gases emitted by marine ships by 2050, and its vision of the fastest total decarbonisation in the maritime shipping industry within the present century, calls for implementation with various means of decarbonisation. The IMO approaches the process of decarbonisation in two phases. Firstly, short-term, compact projects are to be considered, next, more complex, medium- and long-term solutions should be aimed at. The preferred arrangements to be applied are photovoltaic systems. Their performance depends to a high degree on the solar incidence angle. In the case of a ship swinging as a result of its course in relation to the wave and incidence direction, the incidence angle undergoes significant periodic changes with a significant effect on the power generated by the PV panels. As a result, the total amount of energy produced by the PV panels diminishes. The paper presents experimental research results obtained on the stand that allowed the investigation of PV panels in simulated marine conditions. Two characteristic positions of a PV panel’s rotation axis in relation to the solar rays’ incidence direction were investigated. It was proved for both variants that the rolling period and solar incidence angle affected the power generated by the PV panel.

Explosion Effects in the Vicinity of the Nord Stream 2 Pipeline on the Environment - Theoretical Analysis

Abstract The study analyzes the effects of an underwater explosion recorded in the Baltic Sea on September 26, 2022, with coordinates: 54.675 North and 15.574 East at a depth of 76.2 m. Based on data from the seismic monitoring system, the detonated charges were estimated at 750 kg of TNT. Then, the empirical equations of R. H. Cole and Warren D. Reid were used to calculate water pressure distribution and determine the danger zones for marine technology, ships, people, and sea fauna. The results are presented in graphical and tabular form. Based on the calculations, the explosion impact area was determined at over 6,700 m from the epicenter.