Surface Ship Research Articles

Mid-fidelity NATO generic destroyer static and moving-ship airwake simulations using the Lattice-Boltzmann method compared with experimental data

During rotorcraft ship-deck landing operations, complex interactional aerodynamic phenomena occur between the rotor and ship airwakes which are not fully understood. To aid in ship–rotorcraft interactional investigations, efficient mid-fidelity ship airwake solutions are needed. Additionally, the effects of ship motion have not been explored much. In this study, the NATO Generic Destroyer, a shared, representative ship geometry created for collaborative studies, was investigated numerically using a GPU-accelerated Lattice-Boltzmann Method solver, and the ship airwake results compared with wind tunnel measurements using discrete velocity probes on the landing deck. The ship surface was modeled using a robust Grad immersed boundary approach, and ship motion was thereby included in the simulations. Ship airwake results showed good agreement with the experiment and excellent computational performance. Simulations took under four hours to run on a single GPU node. This is several orders of magnitude improvement over traditional Delayed Detached-Eddy Simulations typically used to solve this problem, which may take days or weeks on CPU-based HPC clusters.

A systems approach to hazard identification for solar-powered and wave-propelled unmanned surface vehicle

Decarbonization is a trend in the maritime industry and may include the use of alternative energy sources on ships. At the same time, autonomous ships are under development. In the future, the two technologies may be combined. The objective of this study is to identify possible hazards related to the operation of autonomous vessels using green energy sources. An extended and holistic Systems-Theoretic Process Analysis (STPA) based approach is proposed, where both safety and security is considered. Changes in level of autonomy during operation are considered, and an extension of the STPA method is proposed to highlight the interaction between the system and external energy source. A solar-powered and wave-propelled unmanned surface vehicle is analysed. The results show that mission performance may be affected by both safety and security issues, and that considering influences from the environment and the autonomous functionalities of the system together, contributes to identifying hazards. The results are compared to operational experience from multiple field campaigns. The case study focuses on a relatively simple autonomous vehicle, but some functionalities may be shared with Maritime Autonomous Surface Ships (MASS). Hence, implications for utilisation of alternative energy sources on MASS, and effects on risks, are discussed.

A dynamic-static combination risk analysis framework for berthing/unberthing operations of maritime autonomous surface ships considering temporal correlation

The autonomy of the ship is bound to bring new risk challenges to its berthing/unberthing operations process. Potential damages resulting from these risks can range from minor hull deformations to substantial losses of assets and environmental harm. To address these risks in the process of berthing/unberthing operations of MASSs or mitigate them to an acceptable level, this paper aims to propose a risk analysis framework combining dynamic and static Bayesian network models, and which can consider the temporal correlation of the values of risk factors, to help ships avoid the occurrence of operational accidents. In this framework, a set of probabilistic model is included to calculate the real-time risk value of autonomous berthing operation, so as to analyze and solve the time-dependent problem of risk change with time during berthing/unberthing of autonomous ships. In addition, the developed model can determine the key influencing factors and possible key situations during the berthing/unberthing of autonomous ships, which can support the risk prevention and mitigation measures in the process of autonomous ship berthing/unberthing. Finally, the applicability of the proposed risk analysis framework is analyzed through a virtual simulation test case of autonomous berthing/unberthing.

Navigating uncharted waters: Challenges and regulatory solutions for flag state jurisdiction of Maritime Autonomous Surface Ships under UNCLOS

The emergence of commercial Maritime Autonomous Surface Ships (MASS) is transforming the shipping industry, presenting significant challenges to flag state jurisdiction under the United Nations Convention on the Law of the Sea (UNCLOS). The flag state is under the primary obligation to ensure that MASS comply with the provisions in UNCLOS and other international instruments. This paper explores the primary challenges MASS pose to flag states to fulfil their jurisdictional obligations under UNCLOS. It also examines potential regulatory pathways to address these issues. The paper recommends using International Maritime Organization (IMO) regulatory instruments as rules of reference as the most practical and feasible approach. It advises flag state to exercise caution in registering MASS particularly when the Remote Control Centre (ROC) is located outside their territorial jurisdiction, pending the adoption of the MASS Code and amendments to relevant conventions.

EVALUATION OF THE EFFICIENCY OF THE AUTOMATED ALGORITHM FOR DRAWING SHIP HULL SURFACES

The efficiency of application of the algorithm for automated construction of ship hull surfaces using NURBS approximation is shown. In the majority of modern shipbuilding CAD-systems a considerable share of labor intensity is occupied by preparation of the ship surface model in the first approximation on the basis of electronic theoretical drawing. For carrying out such works in shipbuilding CAD-systems there is a corresponding computer toolkit, which allows to display geometrically clear smooth forms of such surfaces as a ship hull. The works of domestic and foreign researchers in this field have been analyzed. The proposed solutions have significant disadvantages: they require large computing power for training and use. The paper shows a modern approach to solving the issue of reducing the labor intensity of graphical constructions and, consequently, time costs by automating the procedures of computer modeling using web technology. Computer modeling of bulbous bow ends and tunnel-type stern parts is a labor-intensive process of manual editing of hull geometry. Experimental studies of the developed automated algorithm and software product for this type of work have been carried out. The comparative analysis of labor intensity of construction of the most complex part of the ship surface – the bow tip of ships of three projects using the developed software product and two professional CAD-systems – KOMPAS-3D and Rhinoceros – is given. The comparison showed that the developed algorithm and program are much more efficient to use compared to the toolkit of the compared CAD systems. The automated algorithms developed in the process of research can be used in shipbuilding practice, ship repair tasks, as well as for hydrostatic calculation programs.

Synthetic Maritime Traffic Generation System for Performance Verification of Maritime Autonomous Surface Ships

With the global advancement of maritime autonomous surface ships (MASS), the critical task of verifying their key technologies, particularly in challenging conditions, becomes paramount. This study introduces a synthetic maritime traffic generation system (S-MTGS) designed for the efficient and safe verification of these technologies. The S-MTGS encompasses a maritime traffic generator integrating a generator based on absolute position (GAP) and a generator based on relative position (GRP). This innovative system leverages historical maritime data to create various scenarios or generate virtual ships based on their interactions with and proximity to other ships. The virtual ships adeptly navigate and perform collision avoidance maneuvers with nearby vessels enabled by the integrated collision avoidance algorithm. The S-MTGS’s ability to generate a wide range of maritime traffic information mirroring actual maritime conditions is pivotal for thoroughly verifying the performance of MASS technology under both standard and extreme situations. The development of the S-MTGS represents a significant advancement in maritime safety and technology. It can evaluate collision avoidance and navigation systems in MASS, featuring a virtual environment for realistic scenario testing and an intelligent navigation system focused on route tracking and collision avoidance.

Adaptive federated filter–combined navigation algorithm based on observability sharing factor for maritime autonomous surface ships

The integrated navigation system ensures maritime autonomous surface ships (MASSs) to safely, efficiently, and autonomously complete various operations in different complex navigation environments. Investigating robust algorithms for integrated navigation is crucial for enhancing the fault tolerance of the system and ensuring the stable and continuous output of the ship’s motion state. However, existing research primarily focused on optimising particular filtering algorithms or examining the foundations of information allocation within a predetermined integrated navigation structure. As such, strategies for enhancing the robustness of the MASS integrated navigation system and the design of subsystems for federated filters in the event of navigation sensor failures have not been sufficiently investigated for complex maritime navigation scenarios. Consequently, this research introduces an observability sharing factor accounting for both system characteristics and state estimation performance in integrated navigation systems, employing nonlinear sampling filtering. Subsequently, a robust integrated navigation framework with distributed federal filter is developed. Within this framework, an adaptive federated filtering integrated navigation algorithm is proposed based on the observability sharing factor to allocate information in federated filtering. Finally, both the theoretical correctness and effectiveness of the algorithm were verified through simulations and real-ship experiments to assist with the development of accurate and fault-tolerant maritime navigation systems.

Research on risk assessment of maritime autonomous surface ships based on catastrophe theory

Maritime autonomous surface ship (MASS), as an alternative to conventional ships, has attracted considerable attention among shipping sectors and is expected to improve the safety and efficiency of future maritime navigation. In this paper, the catastrophe theory is applied to risk assessment of the risk state changes of MASS. Firstly, the risk-accident framework of MASS is constructed to explain the process of risk factors causing accidents, and the whole risk factors are classified into four categories including human behavior, device failure, environment impact, and management problems. Secondly, On the basis of the framework, we propose the fold and cusp catastrophe models of the risk evolution of the MASS to describe the evolution mechanism of accident causes. Finally, simulated test for the MASS was carried out near the coastal waters of China. The simulation results show that management factors play an important role in the risk assessment model of the MASS. As a result, the proposed method may be applicable to the safety monitoring of the MASS.

A framework to identify failure scenarios in the control mode transition process for autonomous ships with dynamic autonomy

The dynamic autonomy resulting from control mode transitions in the Maritime Autonomous Surface Ships (MASS) poses a significant risk to its navigation safety. While Systems-Theoretic Process Analysis (STPA) has proven effective in the hazard identification of autonomous ships, the safety challenges inherent in the dynamic autonomy of MASS with seafarers onboard controlled by three possible controllers have not been addressed much. This study proposes a three-phase framework that combines STPA, state machines and Sequentially Timed Events Plotting (STEP) diagram aiming at identifying failure scenarios in the transition process. Findings from a case study highlight the need for such a framework to understand the triggering events for mode transition, the transition process, how transition failure may occur, and refine safety constraints to ensure smooth and safe transitions. The results lay the groundwork for future research into test case generation methods that focus on these identified transition failure scenarios, intending to ensure safe and secure navigation for the progressive introduction of MASS.

Efficacy evaluation of adaptive collision avoidance systems for autonomous maritime surface ships based on target ships’ maneuvering behaviors

ABSTRACT Ongoing advancements in collision avoidance systems (CAS) for autonomous ships, built on diverse algorithms, have significantly enhanced performance. These systems, underpinned by a range of sophisticated algorithms, demonstrate increased accuracy in detecting potential collisions, quicker response times, and more reliable path prediction and avoidance maneuvers. Nevertheless, a prevalent trend in these systems is the insufficient consideration of the broader behavioral context of maritime navigation, especially for manned ships. The objective of this research is to present a methodology that can improve the capabilities of CAS in autonomous ships based on target ships’ maneuvering behaviors. This methodology will enable autonomous ships to respond adaptively to the actions of target ships, particularly manned ships, while also reacting to immediate environmental stimuli. The ultimate goal is to enhance the safety of navigation. This research utilizes information derived from simulated ship handling training, focusing on feature extraction within steering, spatial, and temporal elements. The tests prioritize the “Average Distance Deviation” metric for designing test scenarios. Simulation-based evaluation tests feature a velocity obstacle algorithm that discerns CAS action in single-ship crossing situations. The research reveals the potential of integrating behavioral dynamics into CAS, which enhances safety in autonomous maritime navigation and fosters a more integrated approach, where autonomous and manned ships coexist synergistically in shared environments.

Time-optimal control of ship manoeuvring under wave loads

Ship manoeuvrability of Maritime Autonomous Surface Ships (MASS) revolutionise the maritime industry. However, this paradigm shift necessitates the advancement of manoeuvring control models to meet the complex demands of autonomous navigation. This paper addresses the need for an improved manoeuvring control model for MASS, particularly concerning path planning and tracking in the presence of wave loads. The paper establishes a comprehensive mathematical model for ship manoeuvring, considering forces acting on the ship's hull, propellers, rudders, and wave loads. A time optimisation model using a spatial reformulation approach is introduced. A nonlinear Model Predictive Control (MPC) model is presented for path planning and tracking, with a case study investigating the influence of wave load and comparing two control strategies. 10%–20% of time consumption increases if the wave load exists. This research bridges the gap in existing literature by incorporating wave loads into MPC-based control models for MASS. The findings shed light on the significance of wave loads in ship manoeuvring and provide valuable insights into effective control strategies for autonomous vessels operating in real-world sea conditions.

Investigation of Methods for Determining the Aspect Angle of a Spatially Distributed Target in its Radar Image

The article is devoted to the problem of determining the aspect angle of a surface ship based on its radar image obtained in synthetic aperture radars. Among the main methods of solving this problem are the classical Bayesian method of multi-alternative hypothesis testing and its modifications, and/or the method of classifying surface ships located at different angles using artificial neural networks (ANNs). The study demonstrates that achieving high efficiency in aspect recognition using ANNs requires significant computational resources, as well as an access large, representative, and scalable training dataset. ANNs demonstrates high performance in various observation conditions in case sufficient computational and time resources; however, it is noteworthy that their effective training requires a substantial amount of processing time, reaching several hours. At the same time, classical methods are capable of performing calculations in fractions of a second, even on relatively low-powered devices. It is also worth noting that as the number of recognised classes increases, ANNs may consume up to tens of gigabytes of RAM, limiting the accessibility of this method in the aspect of task recognition of spatially distributed targets.

Investigation of Methods of Classifying Surface Ships by Size, Determined by Their Radar Images

The problem of determining the dimensions occupied by a spatially distributed target (SDT) in a two-dimensional raster image of the observed area of the Earth’s (water) surface, formed by a spaceborne synthetic aperture radar (SAR), is relevant in various space monitoring tasks. In this work, this problem is addressed specifically in the context of determining the dimensions of SDT of surface ships against reflections from the sea surface. Both phenomenological and real models based on SSDD database are used as SAR images. Decision-making regarding the dimensions of surface ships is performed using classical (parametric and non-parametric) algorithms as well as machine learning algorithms leveraging artificial neural networks. The results of the comparative analysis of these algorithms are presented.

AodeMar: Attention-Aware Occlusion Detection of Vessels for Maritime Autonomous Surface Ships

AodeMar: Attention-Aware Occlusion Detection of Vessels for Maritime Autonomous Surface Ships

Safety-Critical Parallel Trajectory Tracking Control of Maritime Autonomous Surface Ships Based on Integral Control Barrier Functions

Safety-Critical Parallel Trajectory Tracking Control of Maritime Autonomous Surface Ships Based on Integral Control Barrier Functions

Dynamic Event-Triggered Parallel Control of Under-Actuated Maritime Autonomous Surface Ships Subject to Ship-Shore Communications

Dynamic Event-Triggered Parallel Control of Under-Actuated Maritime Autonomous Surface Ships Subject to Ship-Shore Communications

Maritime Autonomous Surface Ships: A Review of Cybersecurity Challenges, Countermeasures, and Future Perspectives

Maritime Autonomous Surface Ships: A Review of Cybersecurity Challenges, Countermeasures, and Future Perspectives

Method for anti-collision path planning using velocity obstacle and A* algorithms for maritime autonomous surface ship

With the increasing size of ships and increasing demand for autonomous navigation, ensuring ship safety is not the only concern; the efficiency of anti-collision technology should be enhanced. In this paper, we propose a novel hybrid anti-collision path planning method called VO-PATH. This method combines the advantages of the Velocity Obstacle (VO) algorithm, which guarantees anti-collision for autonomous ships, with the A* algorithm, which is known for its capacity to optimize paths. To assess the effectiveness of the proposed method, we conducted anti-collision simulations for both single- and multiple-encounter scenarios, all of which adhered to COLREGs-defined avoidance obligations. Furthermore, we evaluated the performance of the proposed method by comparing its results with those obtained using conventional VO and A* algorithms. The findings indicate that the proposed method is superior to the A* algorithm in terms of steering away from collisions in complex multiple-encounter scenarios. Additionally, the proposed method significantly reduces the distance traveled by the ship to avoid potential collisions, with improvements of up to approximately 6.6% compared with alternative algorithms. We expect that this reduction will enhance safety and provide a more efficient anti-collision path.

Recipe for fatigue: sleep, work patterns, and well-being of galley workers on surface ships of the US Navy.

The purpose of the study was to compare the fatigue levels, work/rest patterns, health-related behaviors, and well-being of galley workers with other sailors on US Navy ships while underway. Analysis was based on a retrospective comparison of data from 3 fit-for-duty groups of sailors: 67 galley workers, 192 non-watchstanders, and 466 watchstanders. Participants completed questionnaires (Epworth Sleepiness Scale [ESS], Insomnia Severity Index [ISI], Pittsburgh Sleep Quality Index [PSQI], and Profile of Mood States [POMS]) and activity logs, and wore actigraphs. Galley workers slept MD = 6.57 (IQR = 1.42) h/d and worked MD = 12.8 (IQR = 2.42) h/d. Approximately 84% of the galley workers were classified as poor sleepers, ~57% reported having excessive daytime sleepiness (EDS), and ~38% reported elevated insomnia symptoms. Compared to non-watchstanders, galley workers had worse scores on POMS (total mood disturbance, tension-anxiety, depression, anger-hostility, fatigue, and confusion-bewilderment), ESS, ISI, and PSQI. Compared to non-watchstanders, galley workers had a higher risk for symptoms of EDS (75%), symptoms of clinically relevant insomnia (126%), and for being classified as poor sleepers (27%). Galley workers slept less and worked on average 2h/d more than non-watchstanders. Compared to watchstanders, galley workers had worse ESS, ISI, and anger-hostility scores. More watchstanders napped compared to galley workers. Although they are considered day workers, the sleep patterns, fatigue levels, and mood of galley workers are comparable to, or worse than, watchstanders or other non-watchstanders. To ameliorate the effects of long work hours on sailor well-being, ship leadership should consider adopting strategies to improve galley workers' well-being.

Research on the cooperative operation of intelligent tugs and maritime autonomous surface ships (MASS)

Research on the cooperative operation of intelligent tugs and maritime autonomous surface ships (MASS)