Ship Operation Research Articles

The effect of failure on energy efficiency in maritime vessels autopilot systems

The increasing demands for energy efficiency and environmental sustainability in the maritime industry have underscored the critical importance of optimizing autopilot systems, which, despite their significance, are often overlooked in ship energy efficiency management. The objective of this study is to enhance the energy efficiency of ships by focusing on the efficiency of autopilot systems, which play a significant role in the management of energy efficiency. The research emphasizes the need for effective decision support systems for ship operators, not only for optimizing ship speeds but also for making informed operational decisions. By utilizing Fuzzy Fault Tree Analysis (FFTA), the study identifies and prioritizes the causes of efficiency losses in autopilot systems and examines their frequency. Based on expert opinions, the research delves into the complexity of autopilot systems and the interactions among various components. Notably, the study highlights the impact of multiple factors on the efficiency of complex autopilot systems, elucidating their relationships through Minimum Cut Sets (MCS) analysis. Furthermore, attention was drawn to the “Improper Alarm Input” event caused by insufficient knowledge and awareness among ship operators, which hinders the effective use of autopilot systems. The findings demonstrate that decision support systems can increase energy efficiency and contribute to the reduction of operational errors by reducing the human factor, which is 99% effective on the “Inefficient Heading Control System”. Additionally, proper utilization of autopilot systems can lead to a decrease in a ship's carbon footprint and operating costs. Overall, the results can affect strategic decisions in ship energy efficiency management and encourage significant steps toward achieving International Maritime Organization's (IMO) sustainability objectives.

Analysis of the Suboptimal Performance of the Steering Gear on MT. Narpatisuta

The steering gear is one of the auxiliary machineries on board a ship that is used to move the ship's rudder, allowing the ship to turn to the right or left. This machinery utilizes an electro-hydraulic system, which combines electric and hydraulic technologies to control the ship's steering direction. The research focuses on analyzing the suboptimal performance of the steering gear on the MT. Narpatisuta. The objectives of the research are to identify the factors causing the suboptimal performance of the steering gear, the impacts resulting from this suboptimal performance, and the efforts made to address the suboptimal performance of the steering gear on the MT. Narpatisuta. The research method used is a qualitative descriptive method, with data collection techniques in the form of observation, interviews, documentation and literature study. The data analysis technique uses the SHEL method. Test the validity of the data using triangulation. The research results show that suboptimal performance of the steering gear is caused by hydraulic pipe leaks, deteriorating quality of hydraulic oil, and damage to seal and o-ring components, which lead to reduced steering gear performance, slower rudder movement, and can disrupt ship operations, endangering crew safety and the ship's cargo. Efforts that can be made to remedy this include routine inspection and maintenance of the steering gear and its components. Thus, this research emphasizes the need for routine inspection and maintenance to prevent these problems which must ensure maximum steering gear function, maintain safety and ship operational efficiency.

A ship motion forecasting approach based on Fourier transform, regularized Bi-LSTM and chaotic quantum adaptive WOA

Ship motion forecasting is of great significance to assist ship navigation and ensure ship operation. However, due to the coupling influence of wind, waves, currents and other factors, the ship motion has a strong time variation, which brings great challenges to the improvement of the ship motion forecasting accuracy. This paper proposes a novel approach for ship motion forecasting. Firstly, Fourier Transform (FT) is used for data pre-processing to deal with the data noise. Secondly, the Bi-LSTM is applied to simulate the nonlinear dynamical system of ship motion, and the Dropout mechanism and l2 regularization method are used to improve the generalization performance of the network, and a novel ship motion prediction model (FRB model) is constructed. Thirdly, in view of the inherent defects of Whale Optimization Algorithm (WOA), based on chaotic mapping and quantum computing, a new Chaotic Quantum Adaptive Whale Optimization Algorithm (CQAWOA) is proposed to construct a new ship motion forecasting approach, namely FRB&CQAWOA. Finally, based on the measured pitch and roll data of ship, the feasibility and superiority of the established FRB model and the proposed CQAWOA are tested. The test results indicate that the proposed model have better prediction accuracy than other algorithms selected in this paper.

Predicting practical ship powering and speed loss in waves using added resistance test results

ABSTRACT This paper introduces a practical technique for accurately estimating ship propulsion and speed loss in the presence of waves. Utilising the previously established self-propulsion estimation framework, our approach enhances the method, which previously solely considered bare hull resistance, propeller performance, thrust deduction factor, and wake fraction, by incorporating added resistance test results. The full-scale DTC container ship is the focus of our investigation, providing a thorough examination of its performance in different sea states. The model's predictive abilities are enhanced by incorporating test data on added ship resistance, and it addresses a crucial aspect that is generally disregarded while estimating self-propulsion. This research is considered to be a contribution to the advancement of ship hydrodynamics, as it offers a practical and reliable tool for ship designers, operators, and researchers to accurately assess powering requirements and speed loss in harsh ocean conditions.

Application of Safety Procedure As Efforts To Prevent Work Accidents In MT. Serang Jaya Engine Room

Safety Procedure is a formal written safety procedure and contains procedures, identification and obligations to carry it out while working. Not optimal due to lack of understanding of Safety Procedures that pose a risk of work accidents. This study aims to determine the causes and impacts of the non-optimal implementation of Safety Procedures and efforts so that the Safety Procedure can run optimally so as to prevent work accidents in the engine room. The research method used in this study is a qualitative method with a descriptive pattern. Research data sources are obtained from primary and secondary data. Data collection techniques through observation, literature study, interviews and documentation during the time researchers carried out marine practices on the MT ship. Serang Jaya. The data analysis techniques used are by collecting data, reducing data, presenting data and drawing conclusions or verifying data which is also supported by fishbone analysis. Testing the validity of data using the tringulation method. The results stated that the factors of not optimal Safety Procedure due to lack of crew understanding of Safety Procedure, and crew concern for safety as well as lack of supervision and coordination between crew and management are the reasons why Safety Procedure cannot run optimally. The impact of not optimal Safety Procedure is the high risk of work accidents, hampering ship operations, harming the ship or the parties concerned. Efforts that can be made to optimize this are by carrying out Safety meeting activities, Toolbox Meeting, Drill, installing Safety Signs. And always provide counseling related to Safety Procedure in the engine room.

Contribution of Onshore Power Supply (OPS) and Batteries in Reducing Emissions from Ro-Ro Ships in Ports

Increasingly restrictive environmental regulations for the maritime sector have led shipping companies to look for technological alternatives to reduce emissions. This article introduces a methodology to analyse emission reductions of ships in port by incorporating batteries into the ships or using an onshore power supply system. These have not yet been considered together for comparison or with a focus on ship operation. The aim is to avoid the use of auxiliary engines in ports. First, the cost calculation method to be used is specified; then, the engine’s behaviour and the established basic navigation criteria are analysed; and finally, different alternatives are considered. A methodology is afterwards defined for selecting alternatives, comparing their costs with those of using auxiliary engines in port. As an example, it is applied to a Ro-Ro route between the ports of Montoir (France) and Vigo (Spain). The results indicate that incorporating batteries into the ship produces greater savings in annual costs than onshore power supply. The cost savings from onshore power supply depend on the range of prices in each port. However, the greatest emission savings are obtained by using the onshore power supply. This methodology can be extrapolated to other routes and vessels by incorporating real operating data.

Big Data Analysis of the Speed Performance of a 176k DWT Bulk Carrier in Real Operating Conditions

Assessment of ship performance under in-service conditions is challenging due to the complex effects of many environmental disturbances. ISO 15016 and ISO 19030 standards are commonly used to evaluate ship operating performance. However, ISO 15016 requires numerous variables, a complex calculation formula, and considerable time and cost, and ISO 19030 only evaluates the reduction of ship speed caused by wind and neglects the effect of waves. To improve both standards and achieve a more accurate ship performance assessment, this study proposes a new performance prediction model, the multi-input single-output (MISO) system, which assumes that each ship has specific frequency characteristics according to type and size. Based on this new model, in-service navigation data collected from a 176k DWT bulk carrier, which amount to 5.7 million data points, are analyzed to assess the speed performance of the vessel subject to environmental disturbances. The proposed model was validated by comparing its results with ISO 19030 and specifically assessing the speed–power curves and speed reduction measured in operational data with the influence of environmental disturbances removed.

Methods of reducing fuel consumption by seagoing vessels in order to reduce emissions of harmful substances into the atmosphere

The aim of the paper is to draw attention to the emissions of toxic compounds into the atmosphere problem by seagoing ships. Annex VI of the MARPOL Convention, which came into force in 2005, concern the prevention of air pollution from ships, forced shipowners to use solutions aimed at limiting the emission of these harmful substances into the atmosphere. One of the instruments enabling the implementation of these guidelines is the design energy efficiency index EEDI, introduced obligatorily for designed and newly built ships. In turn from 2013, the regulations also oblige shipowners to maintain an effective energy management plan during ship's operation, SEEMP (Ship Energy Efficiency Management Plan). The paper presents factors that influence the reduction of fuel consumption by ships, leading to a reduction in emissions of harmful substances into the atmosphere and thus achieving the required EEDI and other indicators. These are factors related to the ship's hull, the main propulsion engine and the propeller-rudder system. The influence of each of these factors on the level of fuel consumption by the ship was indicated. The work highlights the methods of reducing fuel consumption by seagoing ships aimed at reducing emissions of toxic compounds into the atmosphere, which are currently the most popular and effective, and relate mainly to the hull and main propulsion engines.

A Risk Identification Method for Ensuring AI-Integrated System Safety for Remotely Controlled Ships with Onboard Seafarers

The maritime sector is increasingly integrating Information and Communication Technology (ICT) and Artificial Intelligence (AI) technologies to enhance safety, environmental protection, and operational efficiency. With the introduction of the MASS Code by the International Maritime Organization (IMO), which regulates Maritime Autonomous Surface Ships (MASS), ensuring the safety of AI-integrated systems on these vessels has become critical. To achieve safe navigation, it is essential to identify potential risks during the system planning stage and design systems that can effectively address these risks. This paper proposes RA4MAIS (Risk Assessment for Maritime Artificial Intelligence Safety), a risk identification method specifically useful for developing AI-integrated maritime systems. RA4MAIS employs a systematic approach to uncover potential risks by considering internal system failures, human interactions, environmental conditions, AI-specific characteristics, and data quality issues. The method provides structured guidance to identify unknown risk situations and supports the development of safety requirements that guide system design and implementation. A case study on an Electronic Chart Display and Information System (ECDIS) with an AI-integrated collision avoidance function demonstrates the applicability of RA4MAIS, highlighting its effectiveness in identifying specific risks related to AI performance and reliability. The proposed method offers a foundational step towards enhancing the safety of software systems, contributing to the safe operation of autonomous ships.

Vulnerability Evolution of a Container Shipping Network in an Uncertain Environment: The Case of China–United States Connections

Container transportation has the advantages of standardization, high efficiency, and high safety, which are essential for promoting the development of the world economy and trade. Emergencies such as severe weather, public health incidents, and social security incidents can negatively affect the operational reliability of the container shipping network. To ensure the network security and high-quality operation of container shipping, a double-layer coupled container transportation network is first described to analyze the evolution of the container shipping network and the risk propagation dynamics of operation participants. On this basis, a cascade failure model of the container shipping network considering risk level is constructed. To evaluate the vulnerability of the container shipping network, the transmission mechanism of cascade failure effects of the container shipping network under different emergency development trends and the evolution law and influence path of the container shipping network structure are both analyzed. Finally, we empirically studied the container shipping network in China and the United States, and characteristic parameters of the China–U.S. container shipping network are calculated and analyzed. The model’s validity is verified through practical cases and model simulation results, and the cascading failure process of the container shipping network in China and the United States under three types of attacks is simulated. Suggestions are provided for effective improvement in the vulnerability of the container shipping network under every kind of contingency.

Maritime Cyber Security: Adopting a Checklist Based on IACS UR E26 Standard

The efficient operation of ship systems that control navigation, communications, sensors, and power and machinery is dependent on the increasing digitization of the maritime sector and the intense use of information and operational technologies. The goal of issuing and enforcing global regulations and standards is to lessen the impact of potential dangers that could jeopardize on-board systems, network and data integrity, and operation, functionality and safety. At this point, "Cyber Resilience of Ships" (UR E26) is recently released by the International Association of Classification Societies (IACS) to address the need to improve ships' cyber resilience. This regulation will be applicable to new ships built on and after 1 July 2024. This study aims to create a check list for ship cyber security based on IACS UR E26 standard. A ship cyber security checklist was developed by first analyzing ship operational technologies, identifying potential cyber risks and vulnerabilities, and then creating a checklist in accordance with the IACS UR E26 standard to ensure cyber security on board. With a focus on clean seas and safe ships, the IACS provides technical assistance, verifies compliance, and conducts research and development to enhance maritime safety, security and regulation. This study provides practical tool to ships for ship cyber security management under the safety management system besides IACS standard benefits. Creating a checklist in accordance with the IACS UR E26 standard also allows ship owners and operators to comply with the standards and facilitate inspection processes. This reduces the effort spent to comply with international regulations. It helps to proactively manage cyber risks by providing a systematic approach to ship cyber security management.

Gap analysis and harmonization of International Standards for Maritime Autonomous Surface Ships

Abstract This paper addresses the challenges and solutions associated with the implementation of autonomous ships, focusing on the Maritime Autonomous Surface Ships (MASS) Code developed by the International Maritime Organization (IMO). With the goal of adopting a mandatory goal-based MASS Code by 2030, this study examines the interoperability and standardization requirements necessary for the effective global operation of autonomous ships. The paper analyses the MASS Code in relation to existing international standards, identifies gaps, and proposes standards works to accommodate the emerging technologies. Additionally, it investigate on the application of Autonomous Navigation Systems (ANS) and introduces the concepts of Shore ANS and Shore Bridge to enhance remote control capabilities. The analysis emphasizes the need for comprehensive standardization to ensure the safe and efficient operation of autonomous ships in diverse maritime environments.

Bridge Detection in Autonomous Shipping: A YOLOv8 Approach with Autodistill and GroundedSAM

Abstract Inland waterway transportation remains a crucial and cost-effective mode of global transport despite challenges such as skilled personnel shortages, increasing competition, and ecological impacts. Autonomous technology enhances vessel navigation, scheduling, safety, and efficiency, making it a viable solution for smart ship and port operations. Reliable object detection is essential for autonomous ships to navigate safely and avoid collisions with static structures like bridges, piers, bollards, and locks. This paper presents an innovative approach for training a network using the Autodistill pipeline for bridge detection and segmentation. We generate a labeled bridge dataset using GroundedSAM, which integrates Grounding DINO and the Segment Anything Model (SAM) to detect and segment regions based on text input. The system focuses on identifying ‘bridge’ and ‘water’ classes, producing high-quality labeled data. Manual filtering improves label quality, enhancing the training of the YOLOv8 model, known for its superior object detection capabilities. Our approach demonstrates high performance in accurately detecting bridges, confirmed through evaluations with and without manual filtering. To validate our solution’s feasibility in real-world applications, we deployed the model on a NVIDIA Jetson AGX Orin for performance evaluation. Future work will extend this approach to additional static and mobile objects relevant to smart ship and port operations, such as ship locks and various ship types.

Evaluating Environmental Impact and the Significance of Shipboard Carbon Capture and Storage for Emission Reduction in Maritime Sector

Abstract This study conducts Life Cycle Assessment (LCA) to evaluate the environmental impacts of Shipboard Carbon Capture & Storage (SCCS) across its entire lifecycle. It examines the full-spectrum carbon capture and storage effects from well-to-tank and tank-to-propeller for four types of marine fuels: Very Low Sulphur Fuel Oil (VLSFO), Marine Gas Oil (MGO), Liquefied Natural Gas (LNG), and Methanol. Additionally, the study examines the Energy Efficiency Existing Ship Index (EEXI) and Carbon Intensity Indicator (CII) of a current ocean-going container ship to quantify the potential benefits of SCCS in reducing ship carbon emissions. The results indicate that the application of SCCS can significantly reduce emissions from carbon-based fuels during ship operations. In particular, ships using VLSFO as fuel can achieve 61.0% reduction in emissions by installing SCCS. However, the application of SCCS leads to 7.8% increase in fuel consumption due to additional energy requirement. In addition, this study also found while fossil methanol reduces emissions during ship combustion, its life cycle emissions are 17.5%-19.6% higher than conventional fossil fuel due to production emissions. The overall SCCS has a positive impact on emission reduction for ships and is reflected in the improvements in EEXI and CII. With the implementation of SCCS, ships fuelled by VLSFO and MGO can remain compliant with IMO Greenhouse Gases emission strategy until 2030. For ships fuelled by LNG and Methanol, compliance can remain beyond 2030.

Preface

These proceedings contain the peer-reviewed full papers presented at the joint conference ICMASS (the 6th International Conference on Maritime Autonomous Surface Ships) and MTEC (International Maritime Port Technology and Development Conference), held October 29th – 30th, 2024, in Trondheim, Norway. MTEC and ICMASS bring together academia and industry from around the world showcasing cutting-edge research and development within maritime technology, autonomous ships and port operations. https://www.ntnu.edu/mtec-icmass2024/home Trondheim, Norway’s hub for technological innovation, has utilized its rich maritime heritage to position itself at the forefront of maritime innovation. With the world’s first test area for autonomous ships, its strong position within academia and research, Trondheim has gained a leading position in the field of maritime innovation through collaboration between maritime stakeholders. This year’s conference topics include: • Green shipping and ports • Smart and autonomous ship and port operations • Maritime safety • Maritime digitalization, connectivity and cybersecurity The theme of this year’s conference, featuring contributions covering topics such as AI & autonomy, cybersecurity, onboard control, remote & shore control, smart ships as part of smart logistics, green shipping, safety evaluations, collision avoidance, regulatory & legal frameworks, as well as discussions on liability, etc. The papers, published in these proceedings contain theoretical contributions as well as reports on practical implementations in field labs and industry, and the conference will also have oral only presentation. Totally there will be more than 90 presentations, where more than 60 papers are invited. A conference such as this can only succeed as a team effort, and, as such, we thank the International Program Committee and the Reviewers for their excellent work in reviewing the papers as well as their invaluable input and advice. On behalf of the organizing committee, List of MTEC/ICMASS-24 Organizing Committee, International Programme and Management Committee and International Reviewers are available in this Pdf.

BEYOND – Building an Experience to Yield Optimal Nautical Displays – Measuring Nautical Situation Awareness

Abstract The growing digitisation and integration of ship operations, navigation, communication, safety equipment and systems have expanded the functional scope of modern navigation systems. However, this has also increased the workload for the crew, making it more challenging to cultivate situation awareness and make appropriate decisions, particularly in critical situations. The research project, BEYOND – Building an Experience to Yield Optimal Nautical Displays, aims to develop and validate a test methodology for evaluating maritime Human-Machine Interfaces (HMI). Besides system usability and user experience, maritime HMIs are generally evaluated in terms of the situation awareness acquired during navigation. For this purpose, the established Situation Awareness Global Assessment Technique (SAGAT) was adapted such that participants experienced a series of simulated navigation scenarios and were interrupted at unexpected intervals to answer situation awareness questions. This paper addresses the challenge of formulating the appropriate situation awareness questions and their associated frameworks. An assembly of questions reflecting the knowledge of a navigator was created, taking into account different categories of information, including the own ship, the route taken, and the surrounding area. The number of questions needed to capture the necessary knowledge comprehensively was also defined. Additionally, it was crucial to determine the appropriate answer options for each question. Besides graphically presented multiple-choice questions, where the answer had to be selected based on a screenshot of the environment, there are also alphanumeric questions where the participant had to retrieve knowledge from the situation and enter numerical values. For instance, this included information about the course, navigation hazards, etc. A major challenge in assessing these numerical values was determining the acceptable range, i.e., the thresholds that define an answer as correct or incorrect. This paper outlines the BEYOND methodology developed for the comprehensive testing of maritime HMIs, focusing primarily on situation awareness assessment. It describes the steps taken to compile a list of situation awareness questions and their corresponding accuracy thresholds based on the findings from expert interviews. The paper also discusses the experimental results of a user study conducted to test the overall methodology.

A combined method for the evaluation of contributing factors to maritime dangerous goods transport accidents

This paper evaluates the contributing factors to maritime dangerous goods (DG) transport accidents by integrating the Entropy Weight (EW) and Grey Relational Analysis (GRA) methods. For this purpose, investigation reports of maritime DG transport accidents that occurred worldwide between 2000 and 2023 are derived from the International Maritime Organization’s Integrated Shipping Information System (IMO GISIS) database’s Marine Casualties and Incidents (MCI) module. Eleven main ship operations and thirteen primary causes were selected by analysing accident investigation reports. The weights of main ship operations are calculated utilizing the EW method. The correlational degrees of the primary causes are then calculated using the GRA method. Most maritime DG transport accidents occur during unberthing, bunkering, and pilotage operations. The most common contributing factors of maritime DG transport accidents are collisions and occupational accidents. Specifically, maritime DG transport accidents are most likely to be caused by collisions during sailing, passage, maneuvering, and bunkering operations, as well as occupational accidents during cargo loading, anchoring, berthing, and mooring operations. The results of this paper can support stakeholders in developing the needed policies to guarantee the safety of maritime DG transport.

Enhancing Situational Awareness for Remote Control of Ship Cranes

Abstract Cargo handling in ship operations poses risks due to heavy loads, necessitating innovation to mitigate accidents. Automation and remote control offers safer and more efficient operations by moving people from hazardous situations. Smaller and remote ports often rely on geared ships, which are typically manually operated. A first step towards automation is to remotely control these cranes. In remote control, the operator needs to gain situational awareness through sensor inputs. Previous research has shown that there is a need to investigate the sensor configuration and placement to provide situational awareness, particularly considering the placement of cargo onboard. This study seeks to identify needed sensor configurations for situational awareness in remote control of ship cranes using a simulator framework. A modular crane simulator framework has been developed to evaluate situational awareness for different sensor configurations. The sensor configuration has been evaluated with feedback from a crane expert. This research has identified potential sensor configurations for enhancing situational awareness in remote crane operations. The developed simulator framework has demonstrated its flexibility and efficiency as a platform for testing and optimizing cargo handling operations.

A systematic literature review of factors influencing the regulation of autonomous inland shipping in Europe

The onset of autonomous inland shipping comes with regulatory challenges. First, regulations currently in place do not permit the operation of autonomous ships, mainly due to crewing requirements. Second, autonomous transport modes are deemed to be disruptive technologies that present a particular challenge to public regulators with regard to their potential for unforeseen risks and uncertainties inherent to their implementation. From a public regulator’s perspective, it is, therefore, of the utmost importance to identify and regulate the factors currently hindering the introduction of autonomous inland shipping, on the one hand, and the newly emerging issues, on the other hand, to minimise potential risk and uncertainty as much as possible. This paper presents a systematic literature review which was conducted to answer the research question: What factors influence the regulation of autonomous inland shipping? Following the outcome of the literature review, various factors could be identified, and four main categories of factors were established: technological, infrastructure, institutional and socio-economic readiness. The identified factors were subsequently discussed with regard to their significance for the regulatory agenda in light of factors found in the literature on autonomous maritime ships. The findings are of relevance for public regulators and policymakers working in the field of autonomous inland shipping. Moreover, the results presented in this paper might be also of interest for those seeking regulatory innovation regarding autonomous processes in other modes of transport.

Methodology for Real-Time Torque Estimation in a Ship Propulsion Digital Twin

Abstract The safe operation of ships requires the condition of propulsion components to be maintained. Digital twins are a promising alternative for intelligent monitoring of these complex systems. Digital twins require models which ensure that the digital representation is able to mimic the behavior of the physical system. Alternate modeling solutions must be found when intellectual property restrictions or lack of available information limit the usability of physics-based models. This paper considers such a case where a system model of the propulsion system requires a real-time capable model of the propeller hydrodynamic torque. The creation of a data-driven hydrodynamic torque model based on full-scale, operational measurements is discussed. The described method focuses on the significant challenges associated with data cleaning and preparation while also evaluating whether well-known machine learning methods are suited for this application. The methods use speed-over-ground, heading, course, rotational speed, and propeller pitch as inputs. The outputs of the models are the single quadrant propeller torque coefficient and the amplitude of harmonic torsional excitation. These outputs are then combined to create a holistic prediction of the torque. Results indicate that both a polynomial least-squares fit and a shallow neural network predict the mean and the amplitude of harmonic components of the torque well. This prediction can be used to isolate the hydrodynamic torque when more than one torque source is present or to simulate what-if scenarios in a digital twin environment.