Search Results (7,375)

This study employs a fluid–structure interaction (FSI) collision-modeling approach to investigate the hydrodynamic effects on impact forces during collisions involving ships and bridges. The influences of the collision speed, the mass of the ship, and the water-flow velocity on the impact force are investigated. The constant added-mass (CAM) method is a widely employed technique in relevant studies to account for water influence due to its efficiency in conserving computational resources and reducing analysis time. This method is also employed in numerical simulations for comparative analysis. The impact force and dynamical response of a container ship using the FSI and CAM methods are investigated to determine whether the CAM method is suitable for considering the influence of the water surrounding the ship. The impact forces assessed by numerical simulations are also compared with the existing formulae. It is found that the water flow significantly affects the collision force, which must be taken into account in high-energy collision situations. Full article

As intelligent vessel technology moves from the proof-of-concept stage to engineering applications, the performance testing and evaluation of autonomous collision avoidance algorithms have become core issues for safeguarding maritime traffic safety. The International Maritime Organization (IMO)’s Maritime Safety Committee (MSC), at its 109th session, agreed to a revised road map for the development of the Maritime Autonomous Surface Ships (MASS) Code; the field has experienced the development stages of single-vessel collision avoidance validation based on COLREGs, multimodal algorithm collaborative testing, and the current construction of a progressive validation system for the integration of a mix of virtual reality and actual reality. In recent years, relevant studies have achieved research achievements, especially in the compatibility of COLREGs and in accurate collision avoidance in complex situations, and other algorithm tests and evaluations have made great breakthroughs. However, a systematic literature review is still lacking. In this paper, we systematically review the research progress of autonomous collision avoidance performance testing and the evaluation of intelligent vessels; summarize the advantages and disadvantages of virtual testing, model testing, and full-scale vessel testing; and analyze the applicability and limitations of mainstream algorithms such as the velocity obstacle algorithm, the artificial potential field algorithm, and reinforcement learning. It focuses on the key technologies such as diverse scene generation, local scene slicing, and the construction of an evaluation index system. Finally, this paper summarizes the challenges faced by autonomous collision avoidance performance testing and the assessment of intelligent vessels and proposes potential technical solutions and future development directions in terms of virtual–real fusion testing, dynamic evaluation index optimization, and multimodal algorithm co-validation, aiming to provide a reference for the further development of this field. Full article

The present article provides an abstract overview of the issue of optimal blank searching for integral parts utilized in complex engineering projects, including those pertaining to the fabrication of machine, ship, and aircraft components. The manufacturing process for these components is intricate and necessitates meticulous precision and strict adherence to the design model. Conventional blank calculation techniques are marred by substantial inaccuracies. The present research proposes and verifies an effective method based on the reverse solution of a mathematical problem. The focal point of this study is the aerodynamic curvature of aluminum alloys belonging to the Al–Mg–Mn family. The formation of the object is achieved through the employment of a hydroelastomer press of the QFC (Quintus Technologies) type. The forming process is simulated using PAM-STAMP software, developed by the French company ESI Group. The objective of the present study is to ascertain the optimal configuration of the blank by optimizing the discrepancy between the dynamic calculations and the design model using sweep contours. The resulting new shape of the part allows for the formation of parts with minimal deviation from their design contours. Full article

As a core component of both the ship propulsion system and mission-critical equipment, shipboard motors are undergoing a technological transition from traditional fault diagnosis to multi-physical-field collaborative modeling and integrated intelligent maintenance systems. This paper provides a systematic review of recent advances in shipboard motor fault monitoring, with a focus on key technical challenges under complex service environments, and offers several innovative insights and analyses in the following aspects. First, regarding the fault evolution under electromagnetic–thermal–mechanical coupling, this study summarizes the typical fault mechanisms, such as bearing electrical erosion, rotor eccentricity, permanent magnet demagnetization, and insulation aging, and analyzes their modeling approaches and multi-physics coupling evolution paths. Second, in response to the problem of multi-source signal fusion, the applicability and limitations of feature extraction methods—including current analysis, vibration demodulation, infrared thermography, and Dempster–Shafer (D-S) evidence theory—are evaluated, providing a basis for designing subsequent signal fusion strategies. With respect to intelligent diagnostic models, this paper compares model-driven and data-driven approaches in terms of their suitability for different scenarios, highlighting their complementarity and integration potential in the complex operating conditions of shipboard motors. Finally, considering practical deployment needs, the key aspects of monitoring platform implementation under shipborne edge computing environments are discussed. The study also identifies current research gaps and proposes future directions, such as digital twin-driven intelligent maintenance, fleet-level PHM collaborative management, and standardized health data transmission. In summary, this paper offers a comprehensive analysis in the areas of fault mechanism modeling, feature extraction method evaluation, and system deployment frameworks, aiming to provide a theoretical reference and engineering insights for the advancement of shipboard motor health management technologies. Full article

Intelligent manufacturing research has focused on digital twins (DTs) due to the growing integration of physical and cyber systems. This study thoroughly explores the Internet of Things (IoT) as a cornerstone of DTs, showing its promise and limitations in intelligent shipbuilding digital transformation workshops. We analyze the progress of IoT protocols, digital twin frameworks, and intelligent ship manufacturing. A unique bidirectional digital twin system for shipbuilding workshops uses the Internet of Things to communicate data between real and virtual workshops. This research uses a steel-cutting workshop to demonstrate the digital transformation of the production line, including data collection, transmission, storage, and simulation analysis. Then, major hurdles to digital technology application in shipbuilding are comprehensively examined. Critical barriers to DT deployment in shipbuilding environments are systematically analyzed, including technical standard unification, communication security, real-time performance guarantees, cross-workshop collaboration mechanisms, and the deep integration of artificial intelligence. Adaptive solutions include hybrid edge-cloud computing architectures for latency-sensitive tasks and reinforcement learning-based smart scheduling algorithms. The findings suggest that IoT-driven digital transformation may modernize shipbuilding workshops in new ways. Full article

This study introduces a novel deep learning architecture for vessel trajectory prediction based on Automatic Identification System (AIS) data. The motivation stems from the increasing importance of maritime transport and the need for intelligent solutions to enhance safety and efficiency in congested waterways—particularly with respect to collision avoidance and real-time traffic management. Special emphasis is placed on river navigation scenarios that limit maneuverability with the demand of higher forecasting precision than open-sea navigation. To address these challenges, we propose a Principal Component Analysis (PCA)-driven layered attention mechanism integrated within an encoder–decoder model to reduce redundancy and enhance the representation of spatiotemporal features, allowing the layered attention modules to focus more effectively on salient positional and movement patterns across multiple time steps. This dual-level integration offers a deeper contextual understanding of vessel dynamics. A carefully designed evaluation framework with statistical hypothesis testing demonstrates the superiority of the proposed approach. The model achieved a mean positional error of 0.0171 nautical miles (SD: 0.0035), with a minimum error of 0.0006 nautical miles, outperforming existing benchmarks. These results confirm that our PCA-enhanced attention mechanism significantly reduces prediction errors, offering a promising pathway toward safer and smarter maritime navigation, particularly in traffic-critical riverine systems. While the current evaluation focuses on short-term horizons in a single river section, the methodology can be extended to complex environments such as congested ports or multi-ship interactions and to medium-term or long-term forecasting to further enhance operational applicability and generalizability. Full article

Maritime traffic accounts for more than 90% of world trade. Noise, pollution, and litter are its drawbacks, affecting especially vulnerable migratory fish. Here, a motivated team of citizen scientists analyzed maritime traffic from three estuaries of the south Bay of Biscay and three from the south of the Iberian Peninsula, where the European eel is critically endangered, during the season of the entrance of glass eels. More than 164,000 data points about ship types and positions were collected. The results showed that traffic differences between estuaries would explain, at least partially, the different eel conservation statuses. The participants appreciated learning about ships and nature conservation and acquiring an awareness of the real volume of shipping and its potential impacts. All the citizen scientists, new and experienced, would like to get involved in ocean research again. Full article

As the IMO and the EU strengthen carbon emission regulations, eco-friendly voyage planning is increasingly recognized by ship owners as one of the most important performance factors of the vessel fleet. The eco-friendly voyage planning aims to reduce carbon emissions and fuel consumption while satisfying voyage constraints. In this study, a novel route waypoint optimization method is proposed, which combines a fuel consumption forecasting model based on the Transformer and a Proximal Policy Optimization (PPO) algorithm for adaptive waypoint planning. The developed framework suggests a multi-objective methodology unlike the traditional approaches where a single objective is sought after, which characterizes fuel efficiency against navigational safety and operational simplicity. The methodology consists of three sequential phases. First, the transformer model is employed to predict ship fuel consumption using navigational and environmental data. Next, the predicted consumption values are utilized as a reward function in a PPO-based reinforcement learning framework to generate fuel-efficient routes. Finally, the number and placement of waypoints are further optimized with respect to terrain and bathymetric constraints, improving the practicality and safety of the navigational plan. The results show that the proposed method could decrease average fuel consumption by up to 11.33% across three real-world case studies: Busan–Rotterdam, Busan–Los Angeles, and Mokpo–Houston, compared to AIS-based routes. The transformer model outperformed Long Short-Term Memory (LSTM) and Random Forest baselines with the highest prediction accuracy, achieving an R² score of 86.75%. This study is the first to incorporate transformer-based forecasting into reinforcement learning for maritime route planning and demonstrates how the method adaptively controls waypoint density in response to environmental and geographical conditions. These results support the practical application of the approach in smart ship navigation systems aligned with IMO’s decarbonization goals. Full article

Sea ice is highly dynamic. Differences in the sea ice drift velocity and direction can cause deformations such as ridges and rubble fields or open up leads. These and other deformations have a major impact on the interaction between the atmosphere, sea ice and the ocean, and strongly influence ship navigability in polar waters. Spaceborne Synthetic Aperture Radar (SAR) data is well suited to observing the sea ice and retrieving sea ice drift vector fields at a small scale (<1 km), revealing deformation zones. This paper introduces a software processor designed to retrieve high-resolution sea ice drift vector fields from pairs of subsequent SAR acquisitions using phase correlation embedded in a multiscale Gaussian image pyramid. We assess the accuracy of the algorithm by using drift buoys and landfast ice boundaries manually outlined from large series of TerraSAR-X acquisitions taken during winter and spring sea ice break up. In particular, we provide a first analysis of the localization accuracy in deformation zones. Overall, our experiments show that deformation zones are well detected, but can be misplaced by up to 1.1 km. An additional interferometric analysis narrows down the location of the landfast ice boundary. Full article

Domestic and short-sea shipping play a crucial role in ensuring food and energy security, employment, and connectivity in Small Island Developing States (SIDSs) and Least Developed Countries (LDCs). Despite accounting for up to 26.2% of global maritime emissions by voyage activity, these sectors remain underrepresented in policy and academic discussions on greenhouse gas (GHG) reduction. This study presents a structured and transdisciplinary assessment of decarbonization pathways tailored to the unique operational characteristics of domestic fleets. It reviews key operational, technical, and port-based strategies, identifying both opportunities and challenges in the transition to zero-emission shipping. Highlighted measures include the adoption of carbon-neutral fuels, advanced energy-efficiency technologies, and optimized vessel design. The paper emphasizes the pivotal role of ports as clean energy hubs and advocates for integrating domestic shipping into National Action Plans and Nationally Determined Contributions. Coordinated stakeholder engagement, targeted public investment, and supportive regulatory frameworks are essential to unlock decarbonization potential—contributing not only to climate mitigation, but also to sustainable development and energy resilience in emerging maritime regions. Full article

Multi-layer multi-pass welding plays an important role in manufacturing industries such as nuclear power plants, pressure vessel manufacturing, and ship building. However, distortion or welding defects are still challenges; therefore, welding monitoring and quality control are essential tasks for the dynamic adjustment of execution during welding. The aim was to propose a machine vision system for monitoring and surface quality evaluation during multi-pass welding using a line scanner and infrared camera sensors. The cross-section modelling based on the line scanner data enabled the measurement of distortion and dynamic control of the welding plan. Lack of fusion, porosity, and burn-through defects were intentionally generated by controlling welding parameters to construct a defect inspection dataset. To reduce the influence of material surface colour, the proposed normal map approach combined with a deep learning approach was applied for inspecting the surface defects on each layer, achieving a mean average precision of 0.88. In addition to monitoring the temperature of the weld pool, a burn-through defect detection algorithm was introduced to track welding status. The whole system was integrated into a graphical user interface to visualize the welding progress. This work provides a solid foundation for monitoring and potential for the further development of the automatic adaptive welding system in multi-layer multi-pass welding. Full article

Structural safety is of utmost importance for polar icebreakers under both navigation and icebreaking conditions. In this research, the Palmgren–Miner linear cumulative damage theory is employed to evaluate the structural fatigue lifespan of polar icebreakers. A spectral analysis, incorporating the time distribution coefficients for three load conditions, is executed to assess the fatigue damage at typical hot spots during navigation. For icebreaking activities, the ship–ice interaction loads with time history are simulated using the discrete ice element method, taking into account five sub-operating conditions. This simulation is coupled with rainflow counting to evaluate the fatigue damage. The results show that the cumulative fatigue damage during navigation is much less than that during icebreaking. Additionally, shoulder areas suffer more serious fatigue damage during icebreaking as a result of the direct impact of broken ice. Consequently, both navigation and icebreaking conditions should be considered in the design of hull structures and the assessment of fatigue strength for polar icebreakers. Full article

As critical assets for surveillance, reconnaissance, and transport, shipborne helicopters play an indispensable role in modern maritime operations. Ensuring the safety and stability of shipboard landings is therefore of paramount importance, particularly under complex sea conditions. This study presents a comprehensive investigation into the dynamic interaction between helicopters and moving ships during the landing phase, with a particular emphasis on the influence of ship motions on the unsteady aerodynamic flow field and rotor loads. A coupled numerical–theoretical framework is developed, which overcomes the limitations of traditional models that typically consider static or single-degree-of-freedom (SDOF) ship motions. This work systematically analyzes the effects of multi-degree-of-freedom (MDOF) ship motions—including roll, pitch, and heave—on the coupled aerodynamic environment and rotor dynamic response. The results demonstrate that each motion component imposes a distinct influence on the flow-field characteristics, with pitch identified as the dominant contributor to turbulence intensity, particularly during the mid-to-late landing phase. Furthermore, it is found that a linear superposition of individual motions cannot accurately represent the combined effect of MDOF motions. Instead, their interaction leads to complex nonlinear effects, which may attenuate certain flow instabilities. These findings provide critical insights into ship–helicopter dynamic coupling and offer a scientific basis for improving landing safety under adverse sea conditions. Full article

A great ellipse route (GER), as one of the fundamental routes for ocean voyages, directly influences the actual voyage distance and the complexity of vessel maneuvering through the location and number of its waypoints. Against the backdrop of global warming, the melting of Arctic sea ice has accelerated the opening of the Arctic shipping route. This paper addresses the issue of how to reasonably segment and adopt rhumb line routes to approximate the GER in the special navigational environment of the Arctic. Using historical routes, recommended routes, and geospatial data that have passed through the Arctic shipping lane as constraints, this paper proposes a waypoint optimization model based on an adaptive hybrid particle swarm optimization-genetic algorithm (AHPSOGA). Additionally, by integrating Arctic remote sensing ice condition data and the Polar Operational Limit Assessment Risk Indexing System (POLARIS), a safety assessment model tailored for this route has been developed, enabling the quantification of sea ice risks and dynamic evaluation of segment safety. Experimental results indicate that the proposed waypoint optimization model reduces the number of waypoints and voyage distance compared to recommended routes and conventional shipping industry methods. Furthermore, the AHPSOGA algorithm achieves a 16.41% and 19.19% improvement in convergence speed compared to traditional GA and PSO algorithms, respectively. In terms of computational efficiency, the average runtime is improved by approximately 12.00% and 14.53%, respectively. The risk levels of each segment of the optimized route are comparable to those of the recommended Northeast Passage route. This study provides an effective theoretical foundation and technical support for intelligent planning and decision-making for Arctic shipping routes. Full article

Immunofluorescence (IF) microscopy of ciliated epithelium is gaining increased popularity as a pre-genetic diagnostic method in primary ciliary dyskinesia (PCD). Ensuring reliable IF-based diagnostics in PCD requires robust standardization of staining methods and antibody performance. We applied whole slide scanning and automated image analysis to systematically evaluate the influence of various sample storage conditions on the specificity of IF staining. We tested eight polyclonal antibodies targeting diverse axonemal protein epitopes, routinely used for PCD diagnostics, under seven different temperature and time combinations. The storage conditions simulated handling of epithelial brushing on glass slides: after material collection at the clinic, during transport, or after reception at the diagnostic laboratory. Our study revealed that proper slide storage conditions are essential for the reliable PCD diagnosis via IF staining. We suggest continuous storage at −80 °C or −20 °C for slides prepared at the diagnostic laboratory, and storage at −20 °C or 4 °C for slides prepared remotely and shipped. Moreover, the IF sensitivity to slide storage conditions differs among antibodies targeting various ciliary elements, with molecular ruler proteins being particularly sensitive to prolonged storage at room temperature. We emphasize the inclusion of additional control slides to mitigate the inter-individual differences and the crucial correlation of IF results with comprehensive patient clinical history for enhanced diagnostic reliability. Full article