Search Results (231)

With the tightening of marine carbon emission reduction policies, the sustainable development of the shipping industry has attracted much attention, and it is of great significance to use Automatic Identification System (AIS) big data to study the carbon emissions of marine ships. Taking ships around Bohai Bay as the research object, this paper constructs a calculation method of ship carbon emissions driven by the ship AIS trajectory. The AIS information of ships is extracted, and the sailing status is determined. The carbon emission calculation model is built based on the AIS data, the carbon emission in 2023 is empirically measured, and the characteristics are analyzed. At the same time, a speed simulation model was built to evaluate the impact of speed reduction on carbon emissions and put forward emission reduction measures. The results show that the carbon emission of ships around Bohai Bay in 2023 was 8.8072 million tons, with cargo ships contributing the most, and the carbon emissions of the cruise state was significant. A 10% reduction in speed would reduce annual carbon emissions by about 6%. This study provides a reference for understanding the impact of speed on carbon emissions and formulating emission reduction measures, which can be used to compare historical and future data to support the emission reduction in ports and shipping enterprises. Full article

On-board emission measurements were conducted at the exhaust of a passenger ship operating under real-world conditions. The chemical composition of exhaust particulate emissions from a turbocharged four-stroke marine diesel engine, operated on Marine Gas Oil was studied. A variety of organic compounds, including alkanes, alkenes, alcohols, cycloalkanes, cycloalkenes, esters, ketones, carboxylic acids, etc., were analyzed. Alkanes were the most abundant organic compounds, followed by alkenes, esters, and alcohols. Emission factors for these compounds were determined under two operating conditions: low engine load (at berth at 400 rpm/4% load, and during port maneuvers at 800 rpm/14% load) and high engine load (during cruising at 1000 rpm, 68% load). A clear increase in organic-compound emission factors was observed at lower loads. The total particulate matter emission factors were between 0.02 and 0.03 g/kWh at high-load points and exhibited significant variability under low-load conditions, from 0.02 to 2.83 g/kWh. The effect of a marine fuel additive was evaluated in this study. Using this fuel additive resulted in a significant decrease in both particulate matter and organic-compound emission factors, especially at low engine loads. Furthermore, the marine fuel additive decreased the total emission factors (${EF}_{TOCs}$) by a factor of 56 under low-load conditions. For high loads, the additive had no effect on the EF_TOCs. Full article

Shipping activities continue to experience growth across a multitude of industrial sectors within the Arctic, hence there are risks in terms of severity and likelihood of accidents. The Arctic region is inherently dangerous to transportation and human existence due to its extreme climate and environmental conditions, and hence the complexities associated with emergency situations within the maritime domain are amplified when operating within the Arctic and North-Atlantic (ANA). The definition and characterisation of potential seaborne disasters and catastrophic incidents in the ANA region are significant enablers in providing a set of critical and sustainable tools for Search and Rescue (SAR), Oil Spill Response (OSR), and emergency management practitioners. Therefore, in this paper we aim to identify and characterise high-priority potential seaborne disasters and catastrophic incidents in the ANA region such as cruise ship accidents, oil leaks, radiological leaks, and fishing boat groundings. These were compiled as an outcome of a set of workshops carried out as part of the ARCSAR, EU Horizon 2020 funded project, and from analysis of the literature. We also provide root cause analysis techniques, tools for strategic decision-making, and means of mitigation. We demonstrate how such tools can be used by applying some of them to a selective case study and drawing lessons learned from the application of root cause analysis, which can help emergency response organisations with preparedness work and hence more efficient response. In doing so, we provide a set of tools that can be used for strategic and operational learning. Such approaches can help standardise the definition and characterisation of potential seaborne disasters and catastrophic incidents in the ANA region in both prospective and retrospective analysis. Full article

Cruise ships function as a means of transport while simultaneously accommodating thousands of guests, providing a holiday experience with various entertainment options. This translates to high energy requirements for propulsion and hotel operations, typically covered by the combustion of fossil fuels. The operation of cruise vessels with fossil fuels contributes to carbon dioxide and also local harmful emissions in ports when shore power connections are not available. To enable cleaner and sustainable cruising, alternative technologies and fuels must be adopted. The present study evaluated the applicability of hydrogen fuel in combustion engines in a Meraviglia-class cruise ship. The fuel consumption of the ship was based on a real operation in Europe. This study examined how fuel energy in the form of LH₂ could be stored on the ship for a European cruise route and concludes that 3700 m³ of storage space would be needed to accommodate the liquid hydrogen. The mass of the LH₂ would only be one-third of that of fossil fuels, but the weight of the LH₂ tanks would most likely increase the total weight of the hydrogen storage. Additional new technologies and combined power production could significantly reduce the amount of LH₂ to be stored. Full article

Written accounts of cultural festivals often deal with the various activities that comprise those types of events. There is a paucity of analyses that discuss how festivals encourage the status quo of consumption practices, while conjuring their hidden costs on society. This paper analyses how the Hamburg Cruise Days Festival attempted to perpetuate the status quo of the cruising industry. The research answers the following question: What would it take to help change the current “cobalt” color promoted by the organizers of the Hamburg’s Waterfront Cruise Days Festival to a “True Blue”, a symbol of the cleanest sky and harbor waters in Germany, and the best example of sustainable Green and Blue Infrastructure in Europe? The research methods comprised in loco fieldwork participant observation in the tradition of participatory action research. It is argued that, from a governance perspective, festival organizers ought to be required to disclaim, in the fashion of “truth in advertising”, the ecological impacts and sponsors’ progress toward reaching existing environmental standards to eradicate costly social and environmental injustices. Said practice will increase our individual and collective awareness of the invaluable richness of the world’s land- and water-based environment before it is irreplaceably exhausted. The article suggests extending events’ emphasis on sustainable tourism to also encompass three additional measures: (i) the socio-ecological performance of the cruise (and shipping) industry; (ii) in the fashion of a Solomonic approach to justice; and (iii) within a formalized Porto of Call Sister Cities Network. Full article

Background: In recent decades, the size and passenger capacity of cruise/passenger ships has been associated with noticeable growth; in turn, this has created significant concerns regarding the adequacy of existing evacuation protocols during an “abandon the ship” situation (life threatening emergency). This study provides a systematic overview of related weaknesses and challenges, identifying critical factors that influence evacuation efficiency, and also proposes innovative/interdisciplinary solutions to address those challenges. It further emphasizes the growing complexity of cruise/passenger ship evacuations due to increased vessel size/heavy density of human population, as well as identifying the necessity of addressing both technical and human-centered elements to enhance safety and efficiency of those specific operations. Methods: Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, a comprehensive systematic literature search was conducted across academic databases, including Scopus, Science Direct, Google Scholar, and a limited number of academic journals that are heavily maritime-focused in their mission. Emphasis was placed on peer-reviewed articles and certain gray studies exploring the impacts of ship design, human behavior, group dynamics, and environmental conditions on evacuation outcomes. This review prioritized research incorporating advanced simulation models, crowd management solutions (applied in various disciplines, such as stadiums, airports, malls, and ships), real-world case studies, and established practices aligned with contemporary maritime safety standards. Results: The key findings identify several critical factors influencing the overall evacuation efficiency, including ship heeling angles, staircase configurations, and passenger (physical) characteristics (with their mobility capabilities and related demographics clearly standing out, among others). This effort underscores the pivotal role of group dynamics, including the influence of group size, familiarity among the group, and leader-following behaviors, in shaping evacuation outcomes. Advanced technological solutions, such as dynamic wayfinding systems, real-time monitoring, and behavior-based simulation models, emerged as essential tools for optimizing an evacuation process. Innovative strategies to mitigate identified challenges, such as phased evacuations, optimized muster station placements, and tailor made/strategic passenger cabin allocations to reduce congestion during an evacuation and enhance the overall evacuation flow, are also highlighted. Conclusions: Protecting people facing a life-threatening situation requires timely preparations. The need for a holistic evacuation strategy that effectively integrates specific ship design considerations and human factors management, along with inputs related to advanced information technology-related solutions, is the best way forward. At the same time, the importance of real-time adaptive management systems and interdisciplinary approaches to address the challenges of modern cruise/passenger ship evacuations clearly stands out. These findings provide a robust foundation for future research and practical applications, contributing to advancements in maritime safety and the development of efficient evacuation protocols for large-in-size cruise/passenger vessels. Full article

Satellite-retrieved sea-surface skin temperature (SST_skin) is essential for many Near-Real-Time studies. This study aimed to assess the potential to improve the accuracy of satellite-based SST_skin retrieval in the Caribbean region by using atmospheric correction algorithms based on four readily available machine learning (ML) approaches: eXtreme Gradient Boosting (XGBoost), Support Vector Regression (SVR), Random Forest (RF), and the Artificial Neural Network (ANN). The ML models were trained on an extensive dataset comprising in situ SST measurements and atmospheric state parameters obtained from satellite products, reanalyzed datasets, research cruises, surface moorings, and drifting buoys. The benefits and shortcomings of various ML methods were assessed through comparisons with withheld in situ measurements. The results demonstrate that the ML-based algorithms achieve promising accuracy, with mean biases within 0.07 K when compared with the buoy data and ranging from −0.107 K to 0.179 K relative to the ship-derived SST_skin data. Notably, both XGBoost and RF stand out for their superior correlation and efficacy in the statistical results of validation. The improved SST_skin derived using the ML-based algorithms could enhance our understanding of vital oceanic and atmospheric characteristics and have the potential to reduce uncertainty in oceanographic, meteorological, and climate research. Full article

The International Maritime Organization (IMO) has set stringent regulations to reduce the carbon footprint of maritime transport, using metrics such as the Energy Efficiency Existing Ship Index (EEXI) and Carbon Intensity Indicator (CII) to track progress. This study introduces a novel approach using deep reinforcement learning (DRL) to optimize energy efficiency across five types of vessels: cruise ships, car carriers, oil tankers, bulk carriers, and container ships, under six different operational scenarios, such as varying cargo loads and weather conditions. Traditional fuels, like marine gas oil (MGO) and intermediate fuel oil (IFO), challenge compliance with these standards unless engine power restrictions are applied. This approach combines DRL with alternative fuels—bio-LNG and hydrogen—to address these challenges. The DRL algorithm, which dynamically adjusts engine parameters, demonstrated substantial improvements in optimizing fuel consumption and performance. Results revealed that while using DRL, fuel efficiency increased by up to 10%, while EEXI values decreased by 8% to 15%, and CII ratings improved by 10% to 30% across different scenarios. Specifically, under heavy cargo loads, the DRL-optimized system achieved a fuel efficiency of 7.2 nmi/ton compared to 6.5 nmi/ton with traditional methods and reduced the EEXI value from 4.2 to 3.86. Additionally, the DRL approach consistently outperformed traditional optimization methods, demonstrating superior efficiency and lower emissions across all tested scenarios. This study highlights the potential of DRL in advancing maritime energy efficiency and suggests that further research could explore DRL applications to other vessel types and alternative fuels, integrating additional machine learning techniques to enhance optimization. Full article

The continued growth of the global cruise market has posed a major challenge to maritime search and rescue, as available rescue resources are limited compared to the scale of passenger ship disasters. To bridge the gap between the number of passengers in distress and the availability of rescue resources, this paper develops a two-stage Mass Rescue Operation (MRO) decision support model (MRO model) to fully utilize the available multiple rescue resources. Based on the combinatorial optimization theory, in the MRO model we consider the rescue capacity of multiple rescue resources and the synergy between them, the accident types and the marine environment conditions to optimize two objectives (rescue time and number of rescue resources dispatched) in two stages. In the first stage, the objective is to minimize the rescue time by Classical Selection Sort Algorithm. In the second stage, the rescue time and the number of rescue resources are simultaneously optimized by Simulated Annealing Arithmetic (SAA) integrated with Genetic Algorithm (GA). Furthermore, considering the actual role of helicopters in MROs, the MRO model is enhanced to schedule helicopters mandatorily or non-mandatorily. Finally, the MRO model was verified by simulating accidents in the Taiwan Strait. The simulation results show that compared with the first stage, the rescue time in the second stage model is saved by up to 16.18% and the number of rescue resources is reduced by up to 45.16%. Full article

The poor maneuverability inherent to large ships is a non-negligible problem that restricts the development of the shipping industry, as large ships can only cruise at an excessively conservative speed when they encounter complicated traffic conditions; nevertheless, ship collision accidents still occasionally occur. In the present study, the novel concept of spoiler fins for modern large ships is proposed. In order to assess their effectiveness in enhancing ship maneuverability, a KRISO container ship (KCS) was selected to carry a pair of spoiler fins, after which a simplified simulation approach for saving the calculation resource was designed for ship collision avoidance conditions, and a full-scale numerical model, including the ship hull, fin, and fluid field domain, was established. Transient-state hydrodynamic forces were calculated during collision avoidance maneuvers using the CFD method; the pressure and velocity contours around the ship were demonstrated; and the ship motion trajectories under different initial ship speeds were simulated and predicted through the adoption of overset mesh and 6-DOF dynamic mesh techniques. Eventually, the improved course-changing performance, dependent on the spoiler fins, was validated. Full article

The shipping industry plays a crucial role in global trade, but it also contributes significantly to environmental pollution, particularly in regard to carbon emissions. The Carbon Intensity Indicator (CII) was introduced with the objective of reducing emissions in the shipping sector. The lack of familiarity with the carbon performance is a common issue among vessel operator. To address this aspect, the development of methods that can accurately predict the CII for ships is of paramount importance. This paper presents a novel and simplified approach to predicting the CII for ships, which makes use of data-driven modelling techniques. The proposed method considers a restricted set of parameters, including operational data (draft and speed) and environmental conditions, such as wind speed and direction, to provide an accurate prediction of the CII factor. This approach extends the state of research by applying Deep Neural Networks (DNNs) to provide an accurate CII prediction with a deviation of less than 6% over a considered time frame consisting of different operating states (cruising and maneuvering mode). The result is achieved by using a limited amount of training data, which enables ship owners to obtain a rapid estimation of their yearly rating prior to receiving the annual CII evaluation. Full article

This study aims to address the problem in tracking technology in which targeted cruising ships or submarines sailing near the water surface are tracked at low frame rates or with some frames missing in the video image, so that the tracked targets have a large gap between frames, leading to a decrease in tracking accuracy and inefficiency. Thus, in this study, we proposed a water surface dynamic multi-target tracking algorithm based on the fusion of YOLOv7 and DeepSORT. The algorithm first introduces the super-resolution reconstruction network. The network can eliminate the interference of clouds and waves in images to improve the quality of tracking target images and clarify the target characteristics in the image. Then, the shuffle attention module is introduced into YOLOv7 to enhance the feature extraction ability of the target features in the recognition network. Finally, Euclidean distance matching is introduced into the cascade matching of the DeepSORT algorithm to replace the distance matching of IOU to improve the target tracking accuracy. Simulation results showed that the algorithm proposed in this study has a good tracking effect, with an improvement of 9.4% in the improved YOLOv7 model relative to the mAP50-95 value and an improvement of 13.1% in the tracking accuracy in the DeepSORT tracking network compared with the SORT tracking accuracy. Full article

Hyper-heuristic algorithms are known for their flexibility and efficiency, making them suitable for solving engineering optimization problems with complex constraints. This paper introduces a self-learning hyper-heuristic algorithm based on a genetic algorithm (GA-SLHH) designed to tackle the logistics scheduling problem of prefabricated modular cabin units (PMCUs) in cruise ships. This problem can be regarded as a multi-objective fuzzy logistics collaborative scheduling problem. Hyper-heuristic algorithms effectively avoid the extensive evaluation and repair of infeasible solutions during the iterative process, which is a common issue in meta-heuristic algorithms. The GA-SLHH employs a genetic algorithm combined with a self-learning strategy as its high-level strategy (HLS), optimizing low-level heuristics (LLHs) while uncovering potential relationships between adjacent decision-making stages. LLHs utilize classic scheduling rules as solution support. Multiple sets of numerical experiments demonstrate that the GA-SLHH exhibits a stronger comprehensive optimization ability and stability when solving this problem. Finally, the validity of the GA-SLHH in addressing real-world decision-making issues in cruise ship manufacturing companies is validated through practical enterprise cases. The results of a practical enterprise case show that the scheme solved using the proposed GA-SLHH can reduce the transportation time by up to 37%. Full article

This study examines the challenges related to the storage and distribution of cabin materials on board during the construction of cruise ships. The construction process of cruise ships involves the assembly of various cabin materials, which are temporarily stored and transported in a complex deck environment with multiple obstacles. These processes can be viewed as location-routing problems (LRPs). However, existing solutions for LRPs do not account for the obstacles commonly encountered in deck environments. Therefore, this paper introduces a new variant of LRPs, termed as the BE-LRP (blocking environment location-routing problem). Initially, by considering the optimization objectives of minimizing the distribution distance and the number of vehicles required, the paper develops an optimization model for the location-routing of on-board materials while taking obstacles into consideration. Subsequently, a method for estimating blocked distances is proposed. This method utilizes Gaussian process regression to predict blocked distances and enables the rapid estimation of distances obstructed by obstacles between different destinations. Lastly, a hybrid obstacle blocking distance processing HO-NSGAII algorithm is formulated to address the BE-LRP. Experimental comparisons demonstrate that the distribution scheme derived from this research method does not necessitate modifications to the delivery distance when compared to the traditional NSGAII algorithm, and better aligns with the actual deck conditions. With an equivalent number of delivery vehicles, the scheme results in shorter delivery distances. The delivery distance can be reduced by 3–17% under varying circumstances. These findings are advantageous for enhancing the efficiency of cruise ship construction and mitigating the impact of warehousing and distribution on construction progress. Full article

The study of fire simulation and emergency evacuation in cruise ships is a challenging aspect of cruise safety research. Investigating the impact of fire byproducts on passenger evacuation is a critical issue that urgently needs to be addressed. This paper utilizes PyroSim 2022 software to establish five fire cases, analysing the fire products concentration under each case. The influence of fire products on passenger evacuation was analysed using PathFinder. The results showed that when the fire source is in the stage area, the impact of fire byproducts on passenger evacuation is relatively minor. However, when the fire source is near the exit, especially in cases 1 and 2, fire byproducts tend to accumulate in the right area of the second floor, significantly affecting passenger evacuation. Moreover, during the evacuation process, a large number of passengers exhibit herd behaviour, leading to some passengers being unable to evacuate safely. To address the congestion issue, an optimized evacuation guidance plan is proposed, that can effectively improve evacuation efficiency and reduce the average congestion time per person. This has a positive role in enhancing the safety level of cruise ship fires. Full article