Applied Science, Faculty of
Electrical and Computer Engineering, Department of
DSpace
UBCV
Morcos, Kirollos
Master of Applied Science - MASc
University of British Columbia
Simulation, planning, and maintenance play critical roles in the demanding environment of naval operations. Despite recent technological advancements, the number of real-life incidents continues to persist. The root cause of many of those failures is a lack of understanding of the interoperability between seemingly small yet critical subsystems, as well as human error. In this thesis, a modelling technique and optimization tool is presented, combining human factors with a subsystem interdependence model via the Infrastructure Interdependencies Simulator (i2SIM). The goal of this work is to improve operational readiness through appropriate allocation of resources, considering the interdependence of vessel subsystems and the operational capabilities of the crew. The research proposes two i2SIM models, one that aims to be representative of the operational conditions of a vessel during its regular course of action. The operational model relates subsystem interactions that offer vessel operators insight into their system behaviours. The other model aims to incorporate human factors such as training and fatigue and their impact on recovery efforts post-faults. These models were constructed based on manufacturer data as well as failure modes, effects, and criticality analysis reports. Two case studies are presented that explore the operation and repair scheme of a combined diesel and gas naval vessel. This model evaluates the simulated real-time state of these subsystems, their importance for the vessel's mission readiness and their interdependencies that may impair the vessel's operational capabilities if one or more subsystems fail or underperform. These investigative methods entail scenario-based testing in i2SIM, where multiple "what-if" scenarios are simulated to mimic different operational states and emergency conditions to evaluate various resource allocation strategies. Through these simulations, the research explores possible interoperable systems. It also assesses repair distribution techniques. The findings of the case studies call for an integrated approach combining an understanding of cascading system failures as well as human factorsâ impact. This thesis aims to be an effort in bridging the gap between theoretical modelling and practical application, offering a scalable, flexible framework for improving resilience in naval operations while addressing broader challenges in system-of-systems engineering.
IMPROVING THE OPERATIONAL RESILIENCE OF NAVAL VESSELS THROUGH THE MODELLING OF SUBSYSTEM INTERDEPENDENCY by Kirollos Morcos B.A.Sc., The University of British Columbia, 2021 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF APPLIED SCIENCE in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Electrical and Computer Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2025 © Kirollos Morcos, 2025 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the thesis entitled: Improving Operational Resilience of Naval Vessels through the Modelling of Subsystem Interdependency submitted by Kirollos Morcos in partial fulfilment of the requirements for the degree of Master of Applied Science in Electrical and Computer Engineering Examining Committee: Dr. José R. Martí, Electrical and Computer Engineering, UBC Supervisor Dr. Christine Chen, Electrical and Computer Engineering, UBC Supervisory Committee Member Dr. Shahriar Mirabbasi, Electrical and Computer Engineering, UBC Supervisory Committee Member iii Abstract Simulation, planning, and maintenance play critical roles in the demanding environment of naval operations. Despite recent technological advancements, the number of real-life incidents continues to persist. The root cause of many of those failures is a lack of understanding of the interoperability between seemingly small yet critical subsystems, as well as human error. In this thesis, a modelling technique and optimization tool is presented, combining human factors with a subsystem interdependence model via the Infrastructure Interdependencies Simulator (i2SIM). The goal of this work is to improve operational readiness through appropriate allocation of resources, considering the interdependence of vessel subsystems and the operational capabilities of the crew. The research proposes two i2SIM models, one that aims to be representative of the operational conditions of a vessel during its regular course of action. The operational model relates subsystem interactions that offer vessel operators insight into their system behaviours. The other model aims to incorporate human factors such as training and fatigue and their impact on recovery efforts post-faults. These models were constructed based on manufacturer data as well as failure modes, effects, and criticality analysis reports. Two case studies are presented that explore the operation and repair scheme of a combined diesel and gas naval vessel. This model evaluates the simulated real-time state of these subsystems, their importance for the vessel's mission readiness and their interdependencies that may impair the vessel's operational capabilities if one or more subsystems fail or underperform. iv These investigative methods entail scenario-based testing in i2SIM, where multiple "what-if" scenarios are simulated to mimic different operational states and emergency conditions to evaluate various resource allocation strategies. Through these simulations, the research explores possible interoperable systems. It also assesses repair distribution techniques. The findings of the case studies call for an integrated approach combining an understanding of cascading system failures as well as human factorsâ impact. This thesis aims to be an effort in bridging the gap between theoretical modelling and practical application, offering a scalable, flexible framework for improving resilience in naval operations while addressing broader challenges in system-of-systems engineering. v Lay Summary In the testing environment of naval vessels, system failures and/or human errors can cause direct impact to the well-being of crew members and mission success. To increase the resilience of these vessels and their crews, a new methodology is used to simulate and mimic real-life systems in the Infrastructure Interdependencies Simulator (i2SIM). Two models are developed in this software. One model studies closely the interactions between subsystems that seemingly have no impact on one another yet prove to be. The other model assesses human factors, such as training and fatigue, and aims to optimally distribute these repair resources on the damaged systems such that the vesselâs operability is restored. This research helps vessel operators make informed decisions in both the planning and operation phases to better protect crew members and achieve a highly resilient naval fleet. vi Preface This thesis is based on the research work completed in the Department of Electrical and Computer Engineering at the University of British Columbia, Vancouver, under the supervision and guidance of Dr. José R. Martí. All published works are included in this thesis. Portions of this thesis benefited from the use of generative AI tools, which were employed to assist with editing drafts, improving the clarity of technical writing, and organizing content structure. All modeling, analysis, case study design, data interpretation, and original research contributions are solely my own. A version of Chapter 3 is based on the following published paper: â Morcos, K.M., Martí, A.T.J., Martí, J.R. (2025). Resilience Analysis of Naval Vessels with i2SIM. In: Oliva, G., Panzieri, S., Hämmerli, B., Pascucci, F., Faramondi, L. (eds) Critical Information Infrastructures Security. CRITIS 2024. Lecture Notes in Computer Science, vol 15549. Springer, Cham. https://doi.org/10.1007/978-3-031-84260-3_11 I am the principal contributor to the work. Dr. José R. Martí and Ms. Andrea Martí provided me with advice on research methodology and helped proofread the text. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ..................................................................................................................................x List of Figures ............................................................................................................................... xi List of Abbreviations .................................................................................................................. xii Acknowledgements .................................................................................................................... xiv Dedication .....................................................................................................................................xv Chapter 1: Introduction ................................................................................................................1 1.1 Problem Statement & Motivation ................................................................................... 1 1.2 Research Gaps ................................................................................................................. 2 1.3 Resilience as the Focus ................................................................................................... 4 1.4 Research Objectives ........................................................................................................ 7 1.5 Research Questions ......................................................................................................... 8 1.6 Significance of Study ...................................................................................................... 9 1.7 Thesis Structure ............................................................................................................ 10 Chapter 2: Literature Review .....................................................................................................11 2.1 Modelling & Simulation in Defense ............................................................................. 11 2.2 Naval Vessel Subsystem Interdependence.................................................................... 12 2.3 Human Factors in Naval Operations ............................................................................. 13 viii 2.4 Summary of Literature Review Findings ...................................................................... 14 Chapter 3: Methodology..............................................................................................................16 3.1 Data Sources ................................................................................................................. 16 3.1.1 Manufacturer Specifications ................................................................................. 16 3.1.2 Historical & Real-Life Data .................................................................................. 17 3.1.3 Human Factor Data ............................................................................................... 18 3.2 Design Framework ........................................................................................................ 18 3.2.1 Component Criticality Analysis for Improved Ship Machinery Reliability ......... 18 3.2.2 Reliability Modelling and Analysis of a Diesel Engine Design Phase Based on 4F Integration Technology ......................................................................................................... 19 3.2.3 Maritime Accident Prevention Strategy Formulation from a Human Factor Perspective Using Bayesian Networks and TOPSIS ............................................................ 21 3.3 Development of Operational & Repair Models ............................................................ 22 3.4 Choice of Modelling Tool ............................................................................................. 24 Chapter 4: i2SIM Models ............................................................................................................26 4.1 i2SIM Ontology ............................................................................................................ 26 4.2 Naval System Topology ................................................................................................ 30 4.2.1 i2SIM Operational Model ..................................................................................... 31 4.2.2 i2SIM Repair Model ............................................................................................. 37 4.2.3 Human Factor Integration Model.......................................................................... 38 Chapter 5: Case Studies & Discussion .......................................................................................41 5.1 Initial Case Studies ....................................................................................................... 41 5.2 Detailed Case Studies ................................................................................................... 45 ix 5.2.1 Cascading Failures in a CODAG Naval Ship During Rescue Scenarios .............. 45 5.2.2 External Cause of Failure and Repair Optimization in a Naval CODAG Ship .... 54 5.3 Cost-Benefit, Risk and Sensitivity Analysis ................................................................. 59 Chapter 6: Conclusion & Future Work .....................................................................................63 6.1 Summary of Findings .................................................................................................... 63 6.2 Study Limitations .......................................................................................................... 64 6.3 Potential Future Work ................................................................................................... 64 6.4 Final Remarks ............................................................................................................... 66 Bibliography .................................................................................................................................67 Appendices ....................................................................................................................................71 Appendix A ............................................................................................................................... 71 A.1 I2SIM Operational Model ......................................................................................... 71 A.2 I2SIM Repair Model ................................................................................................. 72 Appendix B ............................................................................................................................... 73 B.1 Operational Model HRTs .......................................................................................... 73 x List of Tables Table 2.1 Current Research Solutions, their Strengths, and Weaknesses ..................................... 15 Table 3.1 FMECA [33] ................................................................................................................. 21 Table 4.1 GT HRT ........................................................................................................................ 35 Table 4.2 DE HRT ........................................................................................................................ 35 Table 5.1 Failure Scenarios. Operability Output: No Redistribution vs i2SIM Optimized .......... 43 Table 5.2 Scenario 1 Global Cell HRT ......................................................................................... 51 Table 5.3 Scenario 2 Global Cell HRT ......................................................................................... 52 xi List of Figures Figure 3.1 Integrated Elements of a Naval Vessel ........................................................................ 23 Figure 4.1 i2SIM Operational Cycle ............................................................................................. 29 Figure 4.2 i2SIM Operational Model Components ...................................................................... 32 Figure 4.3 Sample Subsystem Block â GTâs FS .......................................................................... 36 Figure 4.4 Sample Repair Distribution Schematic between 2 Subsystems .................................. 37 Figure 5.1 Entity Relationship Diagram (ERD) for the Initial i2SIM Case Study ....................... 41 Figure 5.2 GTâs CS Losses ........................................................................................................... 46 Figure 5.3 DEâs LS Constrained Operation .................................................................................. 47 Figure 5.4 Global Objective Cell Operational Level .................................................................... 48 Figure 5.5 Actual vs Desired Power Output of the GT & DE ...................................................... 49 Figure 5.6 i2SIM Window showing the Health of Cells .............................................................. 50 Figure 5.7 GTâs CS Losses ........................................................................................................... 52 Figure 5.8 Combat System, Propeller, and Global Cellâs Operational Levels ............................. 53 Figure 5.9 Vesselâs Power System Operational Level .................................................................. 57 Figure 5.10 Recovery Level of LS, CS, and FI Systems .............................................................. 58 xii List of Abbreviations AI Artificial Intelligence AIS Air Intake System BN Bayesian Networks CFD Computational Fluid Dynamics CODAG Combined Diesel and Gas CoS Control System CPTs Conditional Probability Tables CS Cooling System DE Diesel Engine DFTA Deductive Fault Tree Analysis ERH Effective Repair Hours EX Exhaust System FHA Functional Hazard Analysis FI Fuel Intake System FMECA Failure Mode, Effects, and Criticality Analysis FRACAS Failure Reporting, Analysis, and Corrective Action System FS Fuel Supply System GE General Electric GT Gas Turbine HRT Human Readable Table i2SIM Infrastructure Interdependencies Simulator ICAS Integrated Condition Assessment System xiii IMO International Maritime Organization IoT Internet of Things LS Lubricating System MAN MAN Energy Solutions M&S Modelling and Simulation NATO North Atlantic Treaty Organization PHM Prognostics and Health Management RPNs Risk Priority Numbers SBT Simulation-Based Training SOLAS Safety of Life at Sea TOPSIS Technique for Order Preference by Similarity to Ideal Solution xiv Acknowledgements I would like to offer my heartfelt gratitude to Dr. José R. Martí, whose expertise and guidance have helped shape my academic journey, during my bachelorâs degree as well as my masterâs. I have been very fortunate to have had this opportunity to work under his supervision and mentorship. Special thanks are owed to Ms. Andrea Martí whose support, valuable contributions, and countless collaborative hours have made this very work a reality. I am also grateful for being part of the i2SIM research group. I would like to thank my colleagues in the i2SIM group for the discussion, inspiration, and interesting ideas that weâve shared over the years. xv Dedication To my family 1 Chapter 1: Introduction 1.1 Problem Statement & Motivation Military simulation models have historically served as rudimentary tools for replicating battlefield scenarios [1]. These early models were often limited by simplistic rule-based approaches, a lack of computational power, and an inability to represent the dynamic and unpredictable nature of real-world conflicts. They primarily relied on manual calculations and static representations, making them inadequate for simulating complex interactions and adaptive strategies in military engagements. Over time, these simulations have evolved alongside technological advancements, particularly with the rise of computer-based modelling in the 20th century. These advancements enabled greater complexity and detail in military operational models, shifting the focus toward improving tactical and strategic training. Simulation technologies allowed military leaders to explore potential operational outcomes rapidly and cost-effectively without the need for extensive real-world exercises [2]. During the Cold War era, the increased computational power of high-performance computing systems led to the development of large-scale models that integrated multiple operational aspects, including logistics, resource allocation, and strategic planning. These comprehensive simulations provided more profound insights into military operations, vulnerabilities, and risk management strategies [3]. Modern naval operations are undergoing a continual digital transformation driven by technological advancements aimed at enhancing operational efficiency and resilience. Central to 2 this transformation is the role of Modelling and Simulation (M&S) technologies, which allow for the analysis of behavioural interoperability among naval vessel systems. M&S tools enable strategic planning, real-time decision-making, and post-mission analysis. Naval vessels operate as highly interdependent systems where failures in one subsystem can impact multiple other systems. Conventional risk management and disaster response plans often treat these subsystems in isolation, overlooking the complex interactions between them [4], [5]. This fragmented approach can result in prolonged downtime and may fail to prevent cascading failures under operational stress. The implementation of predictive maintenance strategies and digital twins enables the identification and mitigation of potential system failures before they occur, improving vessel availability and extending operational lifespan. Furthermore, naval vessels must maintain resilience in hostile and uncertain environments. Simulation-based resilience analysis provides critical insights into failure modes, their effects on vessel performance, and possible countermeasures. 1.2 Research Gaps Despite advances in M&S technologies, current resilience evaluation studies for naval vessels primarily focus on individual components rather than the complex interdependencies among systems. Key subsystems, such as propulsion, communication, and controllers, are often analyzed in isolation, resulting in an incomplete understanding of overall system behaviour during failures [6], [7], [8], [9]. 3 These studies also advocate for methodologies and techniques that increase the involvement of human factors. They recognize that human error is at the core of failures and that a human model must be integrated into these M&S technologies to mitigate risks. Recognizing these limitations, NATO established the Naval Ship Code in 2004 to provide safety assurance that aligns with military operational needs while maintaining universally recognized safety standards. However, the Naval Ship Code explicitly warns against treating hazards in isolation, emphasizing the need for comprehensive interdependency modelling [10]. A significant gap in existing naval resilience studies is the lack of methodologies that holistically integrate real-life performance data, human factors, and socio-physical interactions. Human factors are particularly crucial in naval resilience, as errors in decision-making, fatigue, and inadequate training have historically contributed to mission failures and increased risk exposure. For example, the USS Fitzgerald collision in 2017 was partly attributed to human error, where failures in situational awareness and decision-making led to a fatal accident [11]. Incorporating human factors into resilience modelling helps to identify and mitigate such vulnerabilities, ensuring safer and more effective naval operations. Many modern simulations rely on cloud-based data processing, introducing cybersecurity risks that could compromise mission success if data is intercepted, tampered with, or blocked during operations. A more secure, offline-capable, and integrated approach is needed to ensure the resilience of naval vessels against emerging threats [12]. Another limitation of conventional models is their focus on reliability rather than resilience. While reliability aims to prevent failures, resilience encompasses both proactive and reactive 4 strategiesâanticipating failures, mitigating their impacts, and rapidly restoring functionality [5]. Given the complexity of modern naval operations, resilience is not merely about preventing breakdowns but ensuring adaptability and recovery in dynamic and unpredictable conditions. 1.3 Resilience as the Focus Today's engineering environment involves systems that operate in highly dependent and often unstable conditions. A resilient system can continue to function during disruptions caused by mechanical breakdowns, environmental stress, or intentional attacks. In the engineering context, resilience can also incorporate the following terms: reliability, recovery, and redundancy. These elements combine to form a system that can resist failures and quickly restore functionality. The first component is reliability, which can be defined as the probability that the system will perform its intended function without failure during a specified time frame under defined conditions [13]. Reliability should be considered within the design stage of a system to ensure components can withstand expected operational stresses. A reliable system is also thought of through the lens of readiness or preventive maintenance to identify and rectify failure points before they become operational problems. For instance, reliability efforts may focus on using stronger materials, strict quality control during manufacturing, and regular inspections to avoid wear-related failures in a propulsion system. Although reliability reduces the risk of failure, it does not account for the systemâs response to failure; this is where recovery comes in. 5 Recovery is when a system can regain its function after being faulted. This resilience aspect focuses on the dynamic processes of operation restoration, like fault detection, diagnostics, and repair strategies. Good recovery depends on the system being able to identify faults quickly, isolate affected components and reallocate resources to keep critical functions running. Suppose a cooling system fails on a naval vessel; recovery would involve finding out what caused the failure, deploying backup resources to handle the thermal load, and coordinating repair teams to replace the primary cooling system. Recovery is not only dictated by how well these physical systems were designed and are maintained but also through human and organizational factors such as crew training and decision-making, which affect the speed and effectiveness of restoration efforts. Redundancy is the third pillar of resilience and involves additional components, systems or paths that act as backups in case the primary system fails. Redundancy could be active, where backup systems work alongside primary systems, or standby, where backups are only activated when needed [14]. Redundancy is employed in naval engineering for power and propulsion systems that allow a vessel to continue operating even if one of the sources fails. Although redundancy improves resilience, it also introduces challenges in terms of increased cost, weight and complexity, which have to be balanced against the operational benefits. The growing complexity of engineering systems and their critical role in modern society require a high degree of resilience. Interconnected and interdependent systems like transportation networks, power grids and naval vessels are particularly prone to cascading failures. A single disturbance in one subsystem can have wide repercussions across the network. For example, a 6 failure in a ship's propulsion subsystem could affect power distribution, auxiliary loads and mission-critical operations. The constantly changing threat landscape, including natural disasters, cyberattacks, and geopolitical tensions, requires engineering systems to operate in uncertain conditions. Resilient systems can better cope with such challenges, preserving essential functions while minimizing disturbances. Resilience is critical for systems supporting critical infrastructure whose failures may have severe economic, environmental and social consequences. Naval vessels are an example of such highly resilient systems. In isolated and often hostile environments, such vessels must contend with mechanical failures and environmental stresses. Naval vessels must be resilient to ensure mission success, crew safety and national security interests. Human factors also contribute to the vesselâs resilience. Crew readiness to handle disruptions depends on training, experience and decision-support systems. Fatigue management also becomes especially important during extreme stress as it directly affects the cognitive and physical performance of the crew. Including human factors in modelling accounts for both technical and non-technical aspects that should be considered in system design and resilience evaluation. This is another avenue that is not yet sufficiently investigated in this field [5], [15]. This thesis contributes to naval resilience through advanced modelling and simulation methods. Exploring interdependencies between subsystems, strategies for optimizing recovery processes and integrating human and organizational factors into resilience assessments are explored using the i2SIM framework. 7 1.4 Research Objectives The overarching goal of this research is to enhance the operational resilience of naval vessels through advanced modelling and simulation techniques, focusing on interdependence, human factors, and repair challenges. The specific objectives are as follows: Develop an Interdependency Modelling Framework: â Utilize a modelling platform to represent and analyze the complex interconnections between naval subsystems such as propulsion, cooling, and auxiliary systems. â Identify key points of failure and their cascading effects on overall system performance. â Assess the role of resource allocation and system prioritization in mitigating the effects of failures. â Assess the sensitivity of the overall objective of the model based on subsystem changes. Incorporate Human Factors into System Models: â Quantify the impact of crew fatigue, training, and rest levels on system recovery times and operational effectiveness. â Develop a methodology for calculating effective repair hours and integrating these into resilience models. Propose Strategies for Enhanced Naval Resilience: â Recommend actionable measures for improving system reliability, reducing downtime, and optimizing resource use during critical scenarios. 8 1.5 Research Questions To achieve the stated objectives, the following research questions guide this investigation: Subsystem Interdependencies: â How do failures in critical subsystems propagate across a naval vessel, and what are the key factors influencing the severity of cascading effects? â How can the i2SIM framework be adapted to accurately model these interdependencies under various operational scenarios? â What are the operational impacts of common failures on naval systems, and how can resource allocation mitigate these impacts? â What limitations does i2SIM face in simulating failure scenarios, and how can these be addressed in future research? Human Factors: â What is the relationship between crew fatigue, training levels, and effective repair times in restoring system functionality? â How can effective repair hours be optimally allocated to minimize downtime and prioritize critical system recovery? Broader Applications: â How can the findings of this research be applied to non-combat scenarios, such as disaster relief missions and environmental operations? 9 1.6 Significance of Study This work has major implications for academic research and practical naval operations. It provides a detailed and integrated simulation model of M&S technologies used in defense. Lessons learned from this study may help design more resilient naval vessels to improve their operational effectiveness and crew safety. In addition, the study's focus on integrating real-life performance data, human factors and repair measures into the simulation model fills in important gaps in existing methodologies. Besides improving the relevance of the simulations, it gives a broader picture of the risks and challenges of naval operations. The risk and sensitivity analysis components of the study provide practical insight into the economic and operational implications of different risk management strategies. Such information helps decision-makers prioritize investments and interventions to improve naval vessel resilience. 10 1.7 Thesis Structure The thesis is structured as follows: â¢ Chapter 1 consists of the background, motivation, problem statement, research objectives, research questions, significance of the study and overview of the thesis structure. â¢ Chapter 2 covers the work previously done in modelling and simulation of naval vessel systems, digital twin initiatives, interdependence modelling efforts, and human factors in naval operations. This chapter identifies gaps in existing research and prepares the ground for the proposed methodology. â¢ Chapter 3 goes over the methodology of how the simulation model was developed. This chapter includes data collection from different sources, model construction, real-time data integration, human factors, and repair measures. â¢ Chapter 4 describes the ontology of the Infrastructure Interdependencies Simulator (i2SIM). It also provides a schematic of the naval vessel, showing the dependencies between subsystems. â¢ Chapter 5 showcases the findings of the different scenarios and case studies that were simulated in i2SIM. It additionally discusses modelling sensitivity analysis as well as other how risk could be quantified from these scenarios. â¢ Chapter 6 is a summary of the findings of the research and is where potential future work is discussed. 11 Chapter 2: Literature Review 2.1 Modelling & Simulation in Defense New simulation and modelling techniques, including digital twins, have improved the resilience of naval vessels. Research shows how these technologies can help improve operational effectiveness, maintenance strategies, and disaster response capabilities. Simulation and modelling provide a way to create digital twins of naval vessels, making real-time digital replicas of physical systems. Zhang et al. [16] review prognostics and health management (PHM) in marine systems and discuss how digital twins could optimize resource allocation and operational efficiency. Continuous monitoring of naval systems health allows predictive maintenance enabling timely interventions to increase resilience and reduce downtime. Moreover, the machine learning techniques incorporated in condition-based maintenance frameworks have shown promising results for the resilience of naval propulsion systems. Coraddu et al. [17] describe machine learning approaches to improve maintenance strategies for better system health monitoring and management. With this proactive maintenance, critical components live longer, and vessels can continue to operate during critical missions. Sterbenz et al. [18] found that simulations can give insight into the network resilience of interconnected systems, which is an important topic for naval vessels that rely on the operation of complex networks for operation. Failure simulations can reveal vulnerabilities and mitigation strategies for naval architects. Furthermore, computational fluid dynamics (CFD) for vessel maneuver simulation improves resilience by optimizing design and operation parameters. Franceschi et al. [19] demonstrate that CFD simulations can predict the maneuverability characteristics of naval vessels, enabling better design decisions to improve operational performance in difficult conditions. Apart from these 12 technical advances, Luâs et al. [20] work on risk evaluation in naval vessel maintenance highlights the need to integrate linguistic approaches in maintenance processes. It demonstrates, using two-tuple linguistic theory, how risk assessment can be improved to decrease errors and strengthen naval operations' resilience. Finally, distributed systems in naval vessels explored by Shields and Rigterink [15] call for scalable and resilient designs. This work illustrates how simulations may guide the architectural design of distributed ship service systems to ensure vessel functionality during component failures. 2.2 Naval Vessel Subsystem Interdependence Modelling interdependence among subsystems is of prime interest for operational effectiveness. The following references highlight the importance of simulations for understanding and improving the interactions of naval systems. Pelissero et al. [21] discuss the requirement of real-time monitoring and control of these multilayered networks to ensure mission success. Their work argues that management of such interdependencies can limit risks of system failures and improve operational resilience. Brefort et al. [5] provide an architecture for analyzing distributed naval ship systems to address the vulnerability of such interconnected systems. This framework explains how cascading failures may occur and how systematic approaches to analyze such complex interactions may be required during the design phase. Another important issue that simulations may address is cost efficiency. A framework for identifying and managing operational requirements during naval vessel batch-building programs that may reduce design and production costs is outlined by Morris [22]. Simulations allow naval architects to optimize resource allocation and speed up production by evaluating different design scenarios and operational requirements. Also, in this issue, Paul and Maloni [23] present a model that updates 13 port capacities dynamically according to changing conditions to optimize network capacity use and reduce port disaster costs. Disaster response capabilities are also improved by simulation modelling. According to Jang et al. [24], simulations may help personnel become more prepared for disasters through realistic training scenarios. Additionally, advanced technologies such as the Internet of Things (IoT) and their integration into naval systems may further enhance operational effectiveness. The study of Kim et al. [25] considers virtualization schemes for distributed combat systems and concludes that such technologies may improve system resilience and operational continuity. Simulation of various operational scenarios reveals potential weaknesses and suggests ways to improve system performance in adverse conditions. 2.3 Human Factors in Naval Operations Human factors have gained the attention of researchers and have been investigated in simulations for naval vessel planning and operations. Incorporating human factors is essential for operational efficiency, safety, and effectiveness. Methodologies and technologies for humanizing naval simulations have been studied, aiming at improving decision-making and operation results. Deere et al. [26] highlight the difficulty of evaluating ship layouts for human factors, especially during evacuation and normal operation. Their systematic methodology demonstrates the need to identify pertinent scenarios and define performance measures for crew and vessel effectiveness. Such an approach highlights human-centric design in naval architecture, which can affect operational readiness and safety. Furthermore, artificial intelligence (AI) for situational awareness and threat monitoring is increasingly seen as a transformative factor in naval operations. Dmitrov [27] describes how AI can cope with the dynamic maritime environment and improve decision-making in naval operations. Artificial intelligence being integrated into 14 simulation models enables real-time data analysis and improved response to threats, which are crucial for modern naval warfare. A third and important human factor is simulation-based training (SBT). Hindorf [28] examines the effects of workload and situational awareness during training environments for naval Command and Control teams. Different simulation fidelity levels are studied, and it is shown that higher fidelity simulations can better prepare the crews for real-life situations by providing more realistic training scenarios. This study supports the view that human factors like workload management and situational awareness are important in designing training programs for naval personnel. Human factors also affect the operational requirements of naval vessels. Morris [22] discusses the need for a framework to identify and manage new operational requirements during naval vessel batch-building programs. The study demonstrates how new technologies and strategic circumstances require a rethink of how vessels are deployed and operated and how human adaptability is required in naval operations. Zhang [29] outlines challenges faced in integrating intelligent technologies into command decision making systems, but future developments may significantly enhance operational effectiveness. This fits with a trend toward having human factors incorporated into technological systems to support decision-making in complex environments. 2.4 Summary of Literature Review Findings The table below summarizes the current research efforts, based on the sections above, that have tried to tackle the presented research gaps in section 1.2. It also presents each solutionâs strengths and weaknesses. 15 Research Aspect Strengths Weaknesses NATOâs Naval Ship Code: Compliance Standard for âbest practiceâ in design, operation, and maintenance Provides a breadth of practical experience and expert input Lacks real-time influence and understanding of vessel-specific needs Machine Learning methods for conditional-based maintenance Captures highly detailed interactions between systems Lacks holistic view of other complex system behaviours and cross-impact Cyber-Physical System Interdependency to avoid cascading failures in operation Studies insightful system behavioural relationships Lacks integration of the human (social) impact Evaluating the Human Factor from a vessel design or mission profile perspective Gives insight for normal and evacuation crew operations Does not look at the problem from the crewâs perspective (fatigue, training, etc.) Failure Mode Analysis and Repair Guidelines Identifies weakness points and failure trends Lacks real-time data contribution Table 2.1 Current Research Solutions, their Strengths, and Weaknesses 16 Chapter 3: Methodology This research incorporates multiple data sources as well as methodologies and techniques from multiple studies to build high-fidelity operational and repair models within the i2SIM framework. Each reference contributes unique perspectives and methods that enhance the modelling accuracy of said models. Below is an exploration of these sources and methodologies and their integration into i2SIM models. 3.1 Data Sources 3.1.1 Manufacturer Specifications Data collection started with original equipment manufacturer (OEM) specifications for the critical subsystems of the vessel. That data also included equipment data sheets and operational manuals from MAN Energy Solutions (MAN), General Electric (GE), and Alfa Laval. Those specifications describe the design, the performance and the operation parameters of the key components such as diesel engines, gas turbines, cooling, lubrication, and air supply systems. For instance, the specifications for diesel engines from MAN [30] will include details about engine power output, fuel consumption rates, operational limits, maintenance requirements, and efficiency metrics. Similarly, GEâs specifications [31] include gas turbine thermal efficiency, power output, operational range, and cooling requirements. Cooling and lubricating system data from MAN and Alfa Laval was used to capture heat exchange capacities, coolant types and flow rates. 17 This manufacturer information along with the studies in section 3.2 formed the basis of the building blocks in the i2SIM model. 3.1.2 Historical & Real-Life Data Incident reports from Naval records can describe different failure types and their effects on vessel operations. These include reports on the conditions that led to the incidents, their sequence, crew responses, and outcomes. This also includes reports of average repair time for subsystem components. Using such reports, trends may be deduced in subsystem failures like recurring problems with cooling system pumps or common crew errors. These records do not necessarily go into great detail of specific metrics and exact operational conditions. This did not allow for the use of a real-life scenario but still guided the general direction of the case studies. It did, however, help shed light on training levels and how those relate to incidents. Although no real-life data was gathered as part of this work, a mimic of the Integrated Condition Assessment System (ICAS) was replicated. The ICAS gathers information that comes mainly from sensors and monitoring systems aboard the vessel, such as the shipboard energy use monitoring systems. Our model was developed in such a way that in the event that the ICAS data was available, it could be integrated into the i2SIM model. The ICAS would monitor and feed into the i2SIM model the following: the health and performance of critical systems like cooling, lubrication, and air supply systems. This instrument 18 collects data on temperature, pressure, vibration, and flow rates to get a real-time picture of the system state. 3.1.3 Human Factor Data Data on human factors is necessary to understand the impacts of human behaviour and performance during naval vessel operations. This information comes from surveys, training records, and performance evaluations. The human factors data can reveal cognitive, physical, and social aspects of crew performance, indicating areas for improvement in safety and efficiency. Crew members' experiences, perceptions and challenges with their tasks and environment are qualitatively collected through surveys and interviews. They may include surveys on fatigue, stress, communication and teamwork. So, crew members might report high fatigue during long deployments or poor communication during high-stress situations. 3.2 Design Framework In addition to the data sources mentioned above, some key studies were used to further shape the i2SIM models. These studies, their techniques, methodologies and findings are discussed below. 3.2.1 Component Criticality Analysis for Improved Ship Machinery Reliability This study [32] employs Dynamic Fault Tree Analysis (DFTA) and Reliability Importance Measures (IMs) to systematically evaluate the criticality of ship machinery components. These methods provide a comprehensive approach to identifying vulnerabilities in key subsystems. 19 â¢ Fault Tree Development: The study constructs detailed fault trees to trace how failures propagate from minor subsystem issues to system-wide malfunctions. Each node in the fault tree represents a failure mode, allowing the analysis of cause-and-effect relationships. â¢ Quantitative Criticality Metrics: Reliability importance measures, such as Birnbaum's Importance and Criticality Importance, quantify the contribution of individual subsystems to overall system reliability. This ensures targeted focus on components that significantly impact operational outcomes. Contributions to i2SIM Models: â¢ Fault tree methodologies were translated into i2SIMâs interdependence mapping, enabling a dynamic representation of how subsystem failures affect the broader system. This mapping facilitated scenario-based analysis for high-risk failure modes. â¢ Criticality metrics were directly used to assign weightings to subsystems in operational models, influencing the prioritization of repair actions and resource allocation. 3.2.2 Reliability Modelling and Analysis of a Diesel Engine Design Phase Based on 4F Integration Technology This study [33] integrates four advanced reliability assessment tools: Failure Mode, Effects, and Criticality Analysis (FMECA), Functional Hazard Analysis (FHA), Dynamic Fault Tree Analysis (DFTA), and Failure Reporting, Analysis, and Corrective Action System (FRACAS). These tools collectively provide a robust framework for understanding subsystem behaviour and reliability. 20 â¢ FMECA: Identifies failure modes, their causes, and their impacts, assigning scores based on severity, occurrence probability, and detectability. Risk Priority Numbers (RPNs) are calculated to prioritize failure modes. â¢ FHA: Evaluates the functional hazards associated with subsystems, identifying critical scenarios that could compromise operational safety. â¢ Dynamic Fault Tree Analysis: Enhances traditional fault tree analysis by incorporating time-dependent variables, allowing the modelling of repairable systems and temporal failure behaviours. â¢ FRACAS: Collects and analyzes field data on subsystem failures, providing empirical insights into recurring issues and their root causes. Contributions to i2SIM Models: â¢ FMECA-derived RPNs informed the assignment of probabilistic failure rates in i2SIMâs operational models. Subsystems with high RPNs were modelled with increased failure likelihood, influencing resource planning. â¢ Time-to-failure and time-to-repair distributions from FRACAS data were incorporated to create realistic repair models. These distributions enabled dynamic simulation of repair activities and their impact on overall system availability. â¢ The combined use of FHA and DFTA ensured that rare but critical failure modes were represented in i2SIM, adding depth to the risk assessment framework. 21 Failure Mode Severity Occurrence Detection RPN Intake and exhaust system 4 6 3 108 Control System 6 5 3 90 Fuel Supply System 4 3 6 72 Cooling System 6 2 2 24 Lubricating System 4 5 2 40 Table 3.1 FMECA [33] 3.2.3 Maritime Accident Prevention Strategy Formulation from a Human Factor Perspective Using Bayesian Networks and TOPSIS This study [34] combines Bayesian Networks (BN) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to analyze and mitigate human factor risks in maritime operations. The focus is on quantifying the influence of human factors on system performance and developing strategies to reduce accident likelihood. In addition to the aforementioned study, the book by D. Butler [35] served as a basis to the estimated number of repair hours needed for the different systems onboard. â¢ Bayesian Networks: These probabilistic models capture dependencies between variables, such as crew training, fatigue, and decision-making, and their impacts on system reliability. Conditional probability tables (CPTs) were developed to quantify these relationships. â¢ TOPSIS: A multi-criteria decision-making method used to rank and select the best mitigation strategies by evaluating alternatives against a set of criteria. 22 Contributions to i2SIM Models: â¢ Bayesian networks provided a probabilistic foundation for modelling human factors in i2SIM. For example, crew fatigue levels were linked to slower repair times, while training levels influenced fault detection rates. â¢ Inspired by TOPSIS, repair prioritization in i2SIM was enhanced with multi-criteria evaluation, considering factors such as subsystem criticality, repair time, and available resources. â¢ Socio-technical interactions, such as communication delays and decision-making under stress, were explicitly modelled, enriching the human factor dimension of the simulations. â¢ Estimates of repair hours to restore various mechanical and electrical systems. 3.3 Development of Operational & Repair Models The combined methodologies and data sources were synthesized to create robust operational and repair models in i2SIM. Key advancements included: 1. Operational Model (i2SIM Model): â¢ Subsystem interdependencies were mapped using DFTA and Bayesian networks, capturing the cascading effects of failures across the system. â¢ Probabilistic failure and degradation rates derived from FMECA and manufacturer data ensured realistic simulation of subsystem reliability under different scenarios. 23 2. Repair Model (i2SIM Model): â¢ Repair activities were modelled using time-to-repair distributions informed by FRACAS and manufacturer guidelines. The stochastic nature of repairs added variability to the simulations, reflecting real-world conditions. â¢ Multi-criteria decision-making frameworks inspired by TOPSIS guided repair prioritization, balancing criticality, resource availability, and operational urgency. 3. Human Factor Integration (Non-i2SIM Model): â¢ Socio-technical interactions, such as the impact of crew fatigue and training on fault detection and repair efficiency. â¢ Human performance variability was incorporated into repair models, highlighting the importance of crew readiness and coordination in maintaining system resilience. Figure 3.1 Integrated Elements of a Naval Vessel 24 By combining these advanced methodologies with empirical data, this research developed i2SIM models that accurately represent the operational and repair dynamics of naval systems. The resulting simulations provide valuable insights into system behaviour, enabling improved decision-making for operational planning and maintenance strategies. 3.4 Choice of Modelling Tool To turn all the gathered metrics into a usable model, a modelling tool is needed to carry out the following tasks: 1. Capture the dynamic behaviour of the systems and their sub-components, considering both steady-state and transient conditions. 2. Consider the interactions and interdependencies between subsystems, including physical and social layers. 3. Account for the variability and uncertainty in the inputs and outputs of the subsystems. 4. Analyze the impact of disturbances and failures in one subsystem on the vesselâs overall performance. 5. Allow for non-linear interactions between inputs and outputs of various subsystems. 6. Allow for real-time simulation of changing ship topologies and conditions due to attacks and failures, enabling quick response and decision-making. 7. Integrate all relevant data sources and information, such as system specifications, equipment characteristics, operational profiles, and environmental conditions. 8. Provide a user-friendly interface and visualization capabilities to facilitate the analysis and interpretation of simulation results. 25 9. Ensure the scalability and flexibility of the simulation tool, allowing for the modelling of various naval vessel types and sizes and accommodating future technological advancements. 26 Chapter 4: i2SIM Models 4.1 i2SIM Ontology i2SIMâs Production Cell concept [36], [37], [38] builds upon Leontiefâs input-output production model [39], extending it to incorporate non-linear input-output relationships and non-physical quantities. For each Cell, the input-output relationship is initially defined in a Human Readable Table (HRT). These functional relationships may be derived from physical laws or modeled probabilistically. i2SIM employs graph theory principles, where nodes represent nonlinear production cells at varying levels of detail, and edge weights correspond to decisions on the quantities transported between nodes through designated channels [40]. The i2SIM relation of inputs to output is defined by a function of Cell operations. This function is achieved through the HRT. The minimum resource required for the Cell determines its operability. ð¦ð¦(ð¶ð¶ð¶ð¶ð¶ð¶ð¶ð¶) = ðððððð{ðð1(ðððððððððð1),ðð2(ðððððððððð2), â¦ ,ðððð(ðððððððððððð)} (1) where ð¦ð¦(ð¶ð¶ð¶ð¶ð¶ð¶ð¶ð¶) is the output of the cell, and fi is the functional relationship between ðððððððððððð and the Cell's output [41]. The relationships between inputs and outputs and the connections among Cells form a system of algebraic non-linear equations. The i2SIM components that were used for the models are listed below: â Feeds: They produce these external tokens. A token is a quantity of an input ot an output in the i2SIM environment. Feeds represent infrastructure systems that are not included in the i2SIM model. 27 â Cell (Production Unit): Modelling system components like hospitals, electrical substations and water stations is done in a Cell. â Channel (Transportation Unit): Virtual channels represent unidirectional connections between cells. Some examples are delays due to repair time or human decision time, water pipes, and air ducts. â Distributor (Control Unit): The Distributor is a decision point where actions are taken to allocate resources. â Aggregator (Control Unit): Aggregator is another decision point. It puts two outputs of the same token in one Cell or Channel. 28 â Sinks: These components send external tokens outside the i2SIM model. There is one output (product) and one or more inputs (resources) for each Cell. The Cellâs operating state depends on the tokens (resources), the PM (physical damage of the Cell), and sources (external information) that are input into the Cell. The physical modes have five possible color-coded levels. There are five color levels - red = 0-25%, orange = 26-44%, yellow = 45-69%, blue = 70-84% and green = 85-100% [41]. 29 Figure 4.1 i2SIM Operational Cycle There are three types of discrete events that affect the i2SIM system: The uncontrollable event, the time-dependent event, and the decision-making event. Uncontrollable Event The uncontrolled event is an unplanned change in the system's internal variables, such as cell and channel functionality. The physical modes of cells and channels can change in i2SIM during an uncontrolled event. Event effects on the system depend on the type of disaster and the time of the disaster. For example, the cooling system failure during sailing in arctic waters would affect the system differently than during sailing in equatorial waters. Uncontrollable events include system failures during operation or natural disasters like tsunamis. External damage would have been treated as uncontrollable in this thesis. 30 Time-dependent Event A time-dependent event is an arranged event that occurs to a Cell or a Channel after some time. Other entities connected to the Cell or the Channel of the event will be affected directly, but not the entire system. However, the effect can be spread to the latter layers of the connected entity. A good example of a time-dependent event trigger is the run time of internal back-up resources such as a generator or water tank. Decision-making Event A decision-making event is triggered by human decisions following the analysis of the current system condition. Such decisions as prioritizing repairs and allocating resources can greatly increase the probability of human survival during disasters. It is described in the i2SIM as an operation mode change in the distributors, the cells, and the channels. For example, both the cooling systems of the gas turbine and diesel engine are partially damaged. If the repair resources are equally split among the major systems, the increase in performance may not be optimal. If the operation of the engine is already capped at a certain value because of some other subsystemâs sub-optimal operation, then the repair of the cooling system does not, in reality, result in a net positive change to the engineâs performance. It is then very important to study each set of conditions separately and have a dynamic solution for such dynamic conditions. 4.2 Naval System Topology To follow the framework mentioned in section 3.2, the following three i2SIM models were built to tackle different parts of the design. 31 4.2.1 i2SIM Operational Model The operational model was based on the physical system topology of a simplified combined gas and diesel (CODAG) ship. The model could be used for any other type of vessel structure, including the newer fully electric generation ones. It can also be developed in greater granularity with additional subsystem intricacies. For the purpose of this research, it was sufficient to model the two major components that directly weigh on the global operability and hence on the resilience of the ship, which are the propulsion and combat systems. The propulsion system is directly impacted by the state of two prime movers: the gas turbine and the diesel engine. Each of these prime movers has the following subsystems. The Gas Turbine (GT) for the cases of this study is based on the parameters of a GE LM25000 (~10 MW capacity option). Its subsystems include a Fuel Supply (FS), Lubricating System (LS), Cooling System (CS), Control system (Control), Air Intake System (AI), and Exhaust System (ES). Similarly, the Diesel Engine (DE) for the cases of this study is based on the parameters of a MAN 48/60 (~30 MW capacity option). Its subsystems consist of a Fuel Intake (FI), Lubricating System (LS), Cooling System (CS), Control system (Control), and Exhaust System (ES). These two prime movers supply power to the loads of the vessel. The two most critical loads are the propeller and combat system, depending on the nature of the vesselâs mission. The prime movers also supply power to the subsystems that feed them. 32 Figure 4.2 i2SIM Operational Model Components Each one of those subsystems is designed and modelled through a similar block structure. A subsystem gets fed from an external feed that is not controlled from within the i2SIM 33 environment. For example, the FS subsystem is fed the fuel flow rate into the turbine in kg/s. This metric tells i2SIM how healthy and efficient the subsystem is. Based on that metric, the operational condition of the subsystem is determined through a lookup for the row that corresponds to these conditions in the HRT. The HRT relates the external input as well as the power feed coming from within the i2SIM model. This power feedback loop is one of the key parts of the model. Even though these subsystems come together to power the DE and GT, they, themselves, are powered and controlled through auxiliary circuits that receive their power from the two powerhouses: the DE and GT. The output of each respective subsystem is then weighted based on criticality and actual impact on its respective major system. The subsystemsâ outputs come together to dictate the total actual output of the major system. The two major systems being the GT and the DE. The HRT of the major system is capped by the performance of its weakest subsystem. To construct the HRTs for the GT and DE, we followed the sources and methodologies outlined in Chapter 3. Each subsystem was analyzed independently to determine its impact on the overall performance of the GT or DE. A performance relationship was then established, linking each subsystemâs functionality to the engineâs overall capability. For example, our analysis showed that when the fuel pump operates at 50% capacity, the GT can still deliver 75% (or 22.5 MW) of its rated power, provided that all other subsystems remain fully operational. A similar one-to-one relationship was developed for other subsystems at their 50% performance level, as this threshold was often the most critical. In many cases, a 50% 34 reduction in performance corresponded to the loss of a redundant or backup system, making it a key reference point in the HRT. Since each subsystem affects the total power output differently, the HRT expands by adding a new row for each additional subsystem. Certain subsystems, such as the cooling system and exhaust system, influence performance in a similar manner, meaning that when both operate at 50% capacity, the overall performance of the GT is capped at 60% (or 18 MW) of its rated power. To fill the rest of the HRT and estimate the performance at other subsystem operational levels, we used a linear approximation/fitting method, centering calculations around the 50% reference point. Since these values were estimates, they are denoted as ââ¦â in the tables below. The complete HRTs are shown in Appendix B. These HRTs could be readjusted as deemed fit in the future if real-life metrics are available. Both HRTs of the two major systems, the GT and DE, are shown below. 35 GT Power Output (MW) FP Op. (%) LS Op. (%) CS Op. (%) AIS Op. (%) EX Op. (%) 30 100 100 100 100 100 22.5 50 â¦ â¦ â¦ â¦ 21 â¦ â¦ â¦ 50 â¦ 18 â¦ â¦ 50 â¦ 50 15 â¦ 50 â¦ â¦ â¦ â¦ â¦ â¦ â¦ â¦ â¦ 0 0 0 0 0 0 Table 4.1 GT HRT DE Power Output (MW) FI Op. (%) LS Op. (%) CS Op. (%) AIS Op. (%) EX Op. (%) 10 100 100 100 100 100 7 â¦ â¦ â¦ 50 â¦ 6 50 â¦ 50 â¦ â¦ 5 â¦ 50 â¦ â¦ 50 â¦ â¦ â¦ â¦ â¦ â¦ 0 0 0 0 0 0 Table 4.2 DE HRT Next, the output of the two major systemsâ HRTs is re-fed through feedback loops into the subsystemsâ inputs. These feedback loops act as overloading controllers. As the operation of the 36 DE or GT increases, the more mechanical burden there is on the subsystems, and the greater the losses there are in performance due to overheating losses. Figure 4.3 Sample Subsystem Block â GTâs FS The two major systemsâ outputs are aggregated together to produce the total power production of the vessel. As discussed earlier, a very highly regarded indicator of how resilient a vessel is the ability of its propellers and maneuvering systems to remain functional. A vessel has a higher chance of both completing its mission and/or returning safely to port if the propeller and maneuverability systems are healthy. The second most critical system is the combat system. For the purposes of this study, it was not necessary to delve into the exact details of the weaponry systems. In reality, each vessel could be very different in terms of arsenal compared to other vessels in the fleet. It was sufficient to consider the combat system simply as an additional load, similar to the propeller. These two major loads, the propeller and combat systems, were both fed as inputs to the final objective Cell in the model. The weighted performance level of loads of that objective Cell was determined based on the nature of the mission of the vessel. If the vessel was simply in cruise mode, more weight went to the propeller and even then, it was not necessary for the propeller to operate at its maximum capacity. If the vesselâs mission was a search and rescue, for instance, an even higher weight would be placed on the propeller, especially for specific upper limits of the propellerâs capabilities. A higher weight would also be allocated to the combat system if applicable. 37 4.2.2 i2SIM Repair Model To address failures and the time needed for recovery, the repair model was developed. Similar to the structure of the operational model mentioned above, the repair model had the same two major systems as well as their respective subsystems. In this case, the external feed is the number of effective repair hours that the crew can offer. The model that determines the number of repair hours is discussed in the following section. The repair model takes the total number of repair hours and distributes it to the systems that have been damaged. i2SIMâs optimizer tool is used here to optimally distribute these hours so that the major systems are restored to the highest degree of performance. The software also allows the user to have their own allocation of these hours based on the selection of a Distributor that the user wishes to optimize. Depending on the hour-allocation, i2SIMâs Channel is used to represent a delay the user should expect as a result of the distribution of these repair hours. Figure 4.4 Sample Repair Distribution Schematic between 2 Subsystems This model acts independently of the operational model but could dictate events in the operational model. For example, it determines how much time a damaged subsystem continues to operate at its reduced level and to what level it is restored. A damaged subsystem is only restored to its expected performance when the allocated number of repair hours has passed. 38 It was necessary to separate the two models since each operates at a different time step. The operational model consists of systems that operate at a higher frequency. The conditions of the subsystems are changing by the minute, while in the repair model, the human latency is considered and so the model is simulated at a lower frequency, every hour. 4.2.3 Human Factor Integration Model Finally, to calculate how many effective repair hours the crew can actually provide, this third model was developed. This model takes in two sources: the rest levels and the training levels of the different crew members. Although there are other factors that could be taken into account, it is widely accepted within the literature that these two factors are the most prevalent [42]. In naval terminology, the training of the crew is referred to as the âfitâ. And for an overall vessel, the âfillâ is how many members of the total crew are equipped with the necessary training and have sufficient experience to carry out their duties. Crew Rest Levels Crew members are assigned a rest efficiency factor based on hours of sleep in the past 24-hour cycle; the regular 8 hours of rest equates to a multiplier of 1.0. We introduce an additional factor of 0.05 for every additional hour of rest a crew member receives up to a maximum of 0.2 = 1.2 (the equivalent of 12 hours of rest, which is the maximum number of hours for a shift). With every 1 hour less than the base a crew member experiences, the multiplier is reduced with a factor 0.05 to a maximum reduction of 0.2 to the base multiplier (equivalent of 4 hours of rest). The reduction also reflects that a crew member should not work for more than 14 hours straight [43]. That means that if a crew member is not well rested, after only a few hours, they would 39 have to rest and stop their work, reducing the number of effective repair hours they can offer [44]. Training Levels Crew members are categorized into three skill levels â Basic, Intermediate, and Advanced â with corresponding task efficiency multipliers of 1.0, 1.10, and 1.15, respectively [45]. â¢ Intermediate-level training is acquired through simulated training rather than field experience. â¢ Advanced-level training is obtained through on-field experience over time. At first glance, it may seem counterintuitive to associate experience with an increase in repair time rather than a decrease. However, in this context, effective repair hours function as contribution tokens rather than actual time spent. This means that a crew member with more training can contribute the equivalent of more hours of service compared to a less experienced counterpart. For example, a crew member with advanced training can handle more tasks within the same time frame than a basic-level crew member. Additionally, because i2SIM optimizes the allocation of the total repair hours across all crew members, these increased effective hours do not necessarily introduce delays. i2SIM does not dictate that a particular crew member should be assigned to that particular damage, but instead a total number of repair hours that is necessary for recovery. 40 In practice, this allows experienced crew members to prioritize repairs that require specialized expertise, ensuring that the most critical and complex repairs receive a proportionally greater share of the available repair effort. The effective repair hours (ERH) for each crew member are calculated as: ERH = Base Repair Hours Ã Rest Efficiency Factor Ã Training Multiplier (2) For example, a motorman with a base 6-hour shift, 4 hours of rest prior to their assignment, and an Intermediate training level would contribute: ERH = 6 Ã 0.8 Ã 1.1 = 5.28 hours It is worth noting that in actual incidents, and especially in emergency conditions, these limitations might not continue to be realistic. The performance of a crew member depends on so many different factors that cannot be simply amalgamated into a singular quantity. However, this model is useful for design and planning. It provides operators with a more realistic view and actual insight into what could be a likely case. As mentioned in the section before, these hours are then passed through a distributor to the different physical systems onboard depending on the damage levels. Another example of this calculation/model is showcased in the cases below. 41 Chapter 5: Case Studies & Discussion 5.1 Initial Case Studies For an initial set of case studies, which do not include the human factors [40], we developed a simplified i2SIM Operational Model of a CODAG vessel. The model contained the two prime movers, the gas turbine and the diesel engine, in addition to the air intake subsystem for the GT and the cooling and lubricating subsystems for the DE. The Entity Relationship Diagram (ERD) for the model is shown below. Figure 5.1 Entity Relationship Diagram (ERD) for the Initial i2SIM Case Study 42 In the simulated scenarios we considered the restoration of the physical operability of the ship subsystems under several fault conditions. The goal was to validate i2SIMâs capabilities of capturing subsystem interdependence while running its resource optimizer. The following failure scenarios were simulated: â¢ Scenario 1 considers a partial failure of DEâs cooling system. Due to clogging in the tubes, the water circulation capacity is limited to 50% of the original flow. â¢ Scenario 2 Simulates a feed of misinformation of subsystem performance as part of a cyber-attack. Often, cyber-attacks target the sensor systems of the vessel. In this scenario, the attackers feed incorrect readings to the integrated bridge system (IBS) for the DEâs cooling subsystem sensors. â¢ Scenario 3 emulates a combat scenario in which the demand for power for the combat system is increased due to a change in the vesselâs normal operational functional profile to a combat profile. â¢ Scenario 4 involves two simultaneous failures. In this scenario, the DEâs cooling subsystem and the GTâs air intake subsystem are partially damaged due to an attack on the ship. Both subsystems now operate at 75% capacity. â¢ Scenario 5 also considers two simultaneous failures. Only this time, the damage is to the cooling subsystem as well as the lubricating system. Both systems serve the same power source, the DE. Both subsystems are now operating at 50% capacity. 43 Failure Operability Not Redistributing Resources (%) Operability with i2SIM Optimized Redistribution (%) Improvement (%) Scenario 1 62% 77% 15% Scenario 2 85% 100% 15% Scenario 3 60% 90% 30% Scenario 4 83% 83% 0% Scenario 5 62% 72% 10% Table 5.1 Failure Scenarios. Operability Output: No Redistribution vs i2SIM Optimized In scenario 1, the power initially delivered to the cooling jacket is reduced to 50% since the jacketâs performance is capped at 50%. The optimizer also reduced the amount of power going to the lubricant. This is because the output of the cooling jacket will still limit the diesel engineâs performance. The excess power was routed to the Sink (the auxiliary loads). i2SIM successfully identified waste and optimally redistributed useful power. In scenario 2, we show that when the tampered-with sensor gives incorrect in-formation, the users can manually override the data. For example, the attack showed that the available power input was 0.75 MW instead of 1 MW, limiting the control system output to 75%. The i2SIM model detected the inconsistency in the data, and the operator manually applied the correct setting. 44 In scenario 3, the combat systemâs power demand is doubled. If the system were to continue distributing power as before, the shipâs operability would have dropped to 60%. In this case, i2SIM reduces the power given to the control system and raises the power supplied to the combat system. This allocation ensures that the other systems are still operational at reasonable values and optimizes the shipâs combat capability. In scenario 4, i2SIM cannot optimize since both power suppliers, the diesel engine and the gas turbine, are capped at the same level of reduced operation, and the optimizer cannot reroute power differently to achieve a higher operability level. In scenario 5, unlike scenario 4, both subsystems affected by the fault affect only one of the power suppliers, the diesel engine. The power given to the cooling jacket and lubricating system serving the affected diesel engine is rerouted to the air pump, allowing a maximum output for the gas turbine. These initial cases provided the foundational basis for the more detailed studies presented next, which are more comprehensive in scope, with the second case explicitly incorporating the influence of human factors. 45 5.2 Detailed Case Studies 5.2.1 Cascading Failures in a CODAG Naval Ship During Rescue Scenarios This case study explores a cascading failure scenario in a CODAG-powered naval ship during a rescue operation, emphasizing the critical role of operational modes, subsystem interoperability, and realistic operational states. Additionally, it evaluates how i2SIM could have been employed to diagnose and anticipate such failures through constraint monitoring and operator aid. The subject vessel operates with two primary propulsion modes: â Cruise Mode: Powered by diesel engines operating at 70% load, suitable for steady, fuel-efficient transit. In this mode, cooling and lubrication system demands are moderate, ensuring reliability over extended periods. â High-Speed Mode: Utilizes both diesel engines and gas turbines at full load for rapid maneuvering and high-speed operations. In this mode, cooling demands increase by 100%, and auxiliary systems such as navigation and emergency lighting draw additional power. Scenario Description During a rescue operation, the ship receives a distress signal from a vessel located 40 nautical miles away. To reach the distressed vessel within the critical 45-minute window for rescue operations, the CODAG ship transitions from cruise mode to high-speed mode. This transition introduces significant mechanical and thermal stress, amplifying the potential for subsystem failures. Minute 0: Initial Condition 46 The initial condition of the system is with the DE producing about 7 MW to operate the propeller for cruise mode and with the GT offline. Minute 10: Initial Failure â GT CS Overloading Ten minutes later, due to the new nature of the vesselâs mission, the GTâs demand is rapidly increased to meet new speed demands. In doing so, the cooling system of the GT undergoes thermal stress and underperforms. This was reflected in the model as an overloading loss. The CS experiences an average loss of 12% from its peak performance. Figure 5.2 GTâs CS Losses The overheating loss of the CS caps the output of GT to 27 MW instead of the expected 30 MW capacity. The combined power output between the GT and DE is now only 34 MW, instead of the expected 30 MW of the GT plus 7 MW of the DE. And so, after the power is distributed among the auxiliary loads, the total power that is actually consumed by the propeller is about 30 MW. This initial failure sets the output of the propeller at a little less than 80% of its rated speed. 024681012140 5 10 15 20 25 30 35 40Losses (%)Time (min)47 Minute 20: Response & Hidden Constraint â Blockage in LS of DE So, a few minutes later, the operator is forced to increase the operation of the diesel engine even further to compensate for the sub-optimal operation of the turbine. The operator proceeds to increase the fuel input to increase the power production of the DE from 7 MW to its rated 10 MW. Another unforeseen event that could have been observed in the i2SIM environment was that the lubricating system of the DE was operating at only 80% due to pipe blockage. This was not previously observed since the DE was operating at 70% of its rated capacity, and so the 80% operation of the LS was not a limiting factor, but it has now turned into a constraint. This blockage now caps the output of the DE to 8 MW, bringing the total power produced to 35 MW of the expected 40 MW. Figure 5.3 DEâs LS Constrained Operation Minute 30: Cascading Failure â DE Increased Output & Cooling System Shutdown An additional 10 minutes go by. The DE tries to maintain the increased power demand, but because of the blocked LS, the thermal load is increased, and the DE's CS experiences an 01020304050607080901000 5 10 15 20 25 30 35 40Operation (%)Time (min)Actual Op Desired Op48 overload. The CSâs performance is dropped to half, and the output of the DE goes down to 5.8 MW, which drops the performance of the vessel even further and causes further delays in getting to the distressed vessel. Complete Timeline The complete timeline showing the key events and performance of the vessel, and its constraining input, the propeller, is shown below: â¢ Minute 10: Underperformance of the GT, due to CS overheating â¢ Minute 20: Underperformance of the DE, due to LS blockage â¢ Minute 30: Underperformance of the DE, due to CS preventative shutdown Figure 5.4 Global Objective Cell Operational Level The power produced by the GT and DE, versus what was expected of those units, is shown in the figure below. 01020304050607080901000 10 20 30 40Operation/Performance (%)Time (min)Global Operation (%) Propeller Performance (%)49 Figure 5.5 Actual vs Desired Power Output of the GT & DE Prevention of Cascading Failures through i2SIM Even though some of these events were pre-determined by simulation, the use of i2SIM allows for such scenarios to be tested and studied. This case study shows that if the i2SIM Cells were monitored, hidden flags would have been spotted, like the constrained performance of the DEâs LS, which subsequently caused the protective shutdown of the DEâs CS. The operator could have kept track of which subsystems have reached certain levels of overloading and started reducing the controlled output of the major system they supply. This would have reduced the total power output and, hence, the propulsion speed, but it would have avoided the cascading failure of the DEâs shutdown, dropping the performance even further. The operator could have balanced the power demand between the two prime movers and reduced the demand of the DE after observing the LS blockage. 051015202530350 5 10 15 20 25 30 35 40Power (MW)Time (min)Actual GT Power Output (MW) Desired GT Power Output (MW)Actual DE Power Output (MW) Desired DE Power Output (MW)50 Figure 5.6 i2SIM Window showing the Health of Cells As mentioned in Chapter 4, The five color levels present the following operational levels: red = 0-25%, orange = 26-44%, yellow = 45-69%, blue = 70-84% and green = 85-100% [41]. In the future, i2SIMâs capability of supporting external feeds could be integrated to the ICAS system of the vessel and monitor the subsystems' actual real-life status. For example, a shutdown 51 of the DEâs CS would simply be reflected as input and would not have to be simulated by the user in the i2SIM environment. Alternative Scenario: Shifted Objective Cell Weights (Sensitivity Analysis) i2SIMâs âwhat-ifâ scenario tool is utilized to simulate the exact conditions of the previous scenario but with a different weight for the two elements that make up the Objective Cell. In an alternative scenario, the combat system is required to be on standby and ready to be used at any point during the search and rescue in the likely chance that there is an external threat on the vessel. In the original scenario above, the HRT of the Objective was as follows: Vessel Operability (%) Propeller Performance (%) Combat Readiness (%) 100 90 10 â¦ â¦ â¦ 50 45 5 â¦ â¦ â¦ 0 0 0 Table 5.2 Scenario 1 Global Cell HRT Given the mission at hand, the search and rescue, most of the weight that determined the mission's success was the performance of the propeller. This weighting drives the distribution of resources within the i2SIM environment. Most of the weight was distributed to the propeller directly or to the subsystems serving the propeller. For this alternative scenario, this modified HRT was used: 52 Vessel Operability (%) Propeller Performance (%) Combat Readiness (%) 100 80 50 â¦ â¦ â¦ 50 40 25 â¦ â¦ â¦ 0 0 0 Table 5.3 Scenario 2 Global Cell HRT This HRT puts less weight (importance) on the propeller and more weight on the readiness of the combat system. Figure 5.7 GTâs CS Losses The overall operability of the two central vessel systems is shown in the figure below. 01234567890 5 10 15 20 25 30 35 40Losses (%)Time (min)53 Figure 5.8 Combat System, Propeller, and Global Cellâs Operational Levels Discussion This shows that because there is a lower requirement for the propeller, its subsystem is under less stress. The losses out of the CS are not as high, 8% compared to 12%. In this scenario, since the GT is able to provide a higher power output, no increased demand was required of the DE, which in turn would prevent the automatic shutdown of the CS. The fact that there was less weight on the propeller, given the nature of the mission, the total power consumption was less, which served in favour of the general health of the vessel. The goal of this case study was to show that the operational model can be used to uncover the exact operational levels of the major subsystems. As was shown, the limiting factor is not always intuitive or obvious. It is also not always the case that the subsystemsâ lower performance due to overloading is directly linked to the performance of the major system. This knowledge could act as an early warning or fault detection system. 01020304050607080901000 5 10 15 20 25 30 35 40Operation (%)Time (min)Global Operation (%) Propeller Performance (%)Combat System Performance (%)54 This case study shows how the operational model could have also been utilized during a vessel's design stages and for off-line mimicking of fault scenarios and sequenced events. This case also shows how changing the weighting of the HRTs can greatly impact the final outcome and performance of the vessel. In the example where the vessel was under threat and needed its defense weaponry, and the combat system needed to be supplied with a set amount of power, the subsystem would have behaved differently. The following case shows even further capabilities of the modelling tool in terms of optimized distribution of resources. A complete model is shown in Appendix A. 5.2.2 External Cause of Failure and Repair Optimization in a Naval CODAG Ship This case study explores a multiple-failure scenario in a CODAG-powered naval ship during normal operation, emphasizing the human impact on the repair timeline of the vesselâs systems. Additionally, it evaluates how i2SIM is employed to optimally redistribute repair resources to achieve a higher recovery state for the vessel. Scenario Description During a normal operation in rough seas, the ship experiences damage to three subsystems that serve the two major systems. This damage triggers an emergency response plan and the allocation of repair hours across those subsystems to bring back the operation of the vessel to the 55 pre-fault state. The way these resources are distributed impacts both the recovery timeline and the final repaired state of the vessel. Subsystem Failures â GTâs LS and CS + DEâs FI The scenario starts with an uncontrolled event; that is the damage of the gas turbineâs lubricating and cooling systems. Both systems are operating at 50% capacity post-fault. The diesel engineâs fuel intake also experiences damage, but it only affects one of the feed pipes, which limits the fuel flow to 50% of the original value. Making use of the Human factor Integration Model In this scenario, the human factor integration model is utilized to calculate how many actual repair hours the crew is capable of providing post-failure. It takes into account their fatigue and training level and adds up all those values. The total number of hours that the crew can actually offer is estimated at 20 hours for this case. Two sets of failures occur at 5:00 and 6:00. These events are similar to the previous case study, so that a realistic series of events is followed. At 5:00, both GT subsystems are damaged, followed an hour later by damage to the DE fuel pipe. For both scenarios, the repair resources are deployed at time step 7:00. Scenario 1: Following Existing Guidelines These 20 hours are then fed into the repair model. In the base scenario (scenario 1), we implemented a methodology that follows a typical damage control handbook/disaster response 56 [46]. The guide starts by addressing certain sections of the ship individually: the forward section, amidships, and aft section. Since all these failures occurred in the engine room, that prioritization is not applicable. The next step, according to the guide, would be to assess the damage and criticality level of these subsystems and assign more resources to those systems that are deemed more critical and at a higher damage level. Again, in this scenario, all three subsystems are critical as all three directly impact the performance of the two powerhouses. Additionally, all three are damaged to the same degree. So, in this case, in lieu of following the typical disaster response plan, and realistically what a crew would do, the repair hours are split evenly across all three damaged subsystems to try and tackle all three issues at once in the hope that this resolves the reduced power capacity in the best way possible. Scenario 1 is simulated in i2SIM, and it is observed that after approximately 7 hours, the shipâs power output is only restored to approximately 90% of the maximum value. Seven hours was not sufficient for the LS to be brought back to a completely healthy state, so the overall output power is capped at 90% of the rated capacity. Scenario 2: Optimized Repair Hour Distribution Scenario 2 is then simulated, utilizing i2SIMâs optimizer tool. Since i2SIM knows exactly how many hours are required to bring each subsystem back to a healthy state based on the HRTs, it can allocate repair hours in a manner that the final state of the system is the best it can get to with the assigned number of effective repair hours. 57 Total repair hours: 20 Scenario 1: split 0.33 (around 7 hours) to each damaged subsystem Scenario 2: Split 0.33 (~7 hours) to FI, 0.23 (~5 hours) to LS, and 0.43 (~9 hours) to CS Figure 5.9 Vesselâs Power System Operational Level 01020304050607080901000 2 4 6 8 10 12 14 16 18 20Operation (%)Time (hours)Scenario 1 Scenario 20204060801001200 5 10 15 20Operation (%)Time (hours)LS Recovery Scenario 1 LS Recovery Scenario 258 Figure 5.10 Recovery Level of LS, CS, and FI Systems Final State i2SIM achieves a final recovered state of 94%, which is the maximum that it can achieve with the limited number of hours available post-fault. 0204060801001200 5 10 15 20Operation (%)Time (hours)CS Recovery Scenario 1 CS Recovery Scenario 201020304050607080900 5 10 15 20Operation (%)Time (hours)FI Recovery Scenario 1 FI Recovery Scenario 259 Improvement through i2SIM i2SIM does not only result in a higher final recovered state, compared to the base case, but it also does so with a two-hour lead where the repair resources assigned to the LS are freed up after 5 hours since this is all the time needed for the LS to regain its original undamaged state. Discussion This case study showed how an optimal distribution of repair resources that considers the actual representation of the crewâs capabilities ensures a higher recovery level for a vessel post-failure. Following traditional methods of disaster response only achieved a certain level of restoration while a model that considers a holistic view of the vessel and objective goal of the mission ensures that the limited resources are distributed in a way that achieves a maximum objective Cell output. The GT and DE HRTs are identical to those in the operational model. This ensures that the results of the repair model could be effective when applied to the operational model. 5.3 Cost-Benefit, Risk and Sensitivity Analysis Cost and Risk Analysis in Naval Operations In naval operations, traditional cost and risk analyses involve qualitative assessments and historical data analysis. Such ways provide some basic information about potential risks and their financial effects. However, they are not able to model complex interactions between subsystems and dynamic operational conditions. 60 Usually, qualitative risk assessments involve expert judgment and subjective evaluations of risks. Such assessments help identify and prioritize risks but lack the objectivity and precision required for detailed analysis. The historical data analysis, in turn, involves examining past incidents and failures to find trends and patterns. This method can give insight into risks but is constrained by historical data availability and accuracy. A traditional qualitative risk assessment, for example, might involve a panel of experts assessing the risk of a new propulsion system. Such expert knowledge and experience would identify failure modes and their likely impact. Though this gives useful insights, it is subjective and might not cover all risks [47]. Modelling dependencies between subsystems using these methodologies can give a more accurate and comprehensive assessment of risks. For example, in the first case study, by changing the cooling system with one that has a different HRT relationship matrix with the GT, a different overloading curve would be produced, and hence, a final operational behaviour of the GT and the vessel would be observed. This type of modelling allows designers to test different configurations, topologies, and system components to study which would produce an acceptable behaviour for their design criteria. Cost-Benefit Analysis (CBA) in Risk Management CBA is a key component of risk management that systematically assesses the financial implications of various strategies. The risk mitigation strategy is then compared with expected 61 financial benefits such as reduced risk of failure and improved system performance in CBA. CBA helps decision-makers prioritize investments and allocate resources to the best strategies. In naval operations, CBA can be used to evaluate risk mitigation strategies ranging from technical upgrades to training programs. Suppose a CBA weighs the cost of installing a different prime mover like the GT or DE but keeps some of the auxiliary subsystems that service the major system. Running that simulation shows that even if one were to replace the DE with a more capable model, the output would still be constrained because of the existing infrastructure. The cost associated with that replacement could then be considered as a loss and hence would guide designers on how to allocate their investments properly. Quantifying the benefits of CBA in naval operations is difficult. They are often intangible benefits that cannot be measured in terms of money. To meet this challenge, multi-criteria decision analysis (MCDA) can be applied together with CBA. In MCDA, several quantitative and qualitative criteria are considered for the evaluation of different strategies. For example, replacing the lubrication system in an attempt to improve the performance of a Diesel Engine (DE) may initially yield positive results. However, beyond a certain point, it may become evident that a different subsystem is actually the bottleneck, imposing a limiting constraint on the DE's power output. 62 Sensitivity Analysis of Risk Management Sensitivity analysis is a further important tool in risk management that reveals how changes in key parameters affect the results of a risk analysis. That means changing values of different parameters repeatedly and evaluating the impact on results. Sensitivity analysis identifies the most influential parameters and explains uncertainties in risk analysis. For naval operations, sensitivity analysis may be applied to assess the robustness of various risk mitigation strategies. For example, assessing how changing the training level of the crew would reflect on how much repair capacity they can provide and thereafter affect how much downtime the vessel would experience in the case of failures would allow decision-makers better equip their crew members with the appropriate training. 63 Chapter 6: Conclusion & Future Work 6.1 Summary of Findings This thesis examined naval vessel operational resilience in the face of cascading subsystem failures while incorporating human impact. Using the i2SIM framework, we showed that dynamic resource allocation, constraint visualization and optimization techniques can help the system recover quickly and reduce risks during critical scenarios. The findings include: Understanding Interdependencies: Naval systems are complex and interconnected - failures in one subsystem often affect other subsystems. For instance, cooling system failure may cause propulsion system degradation and auxiliary power instability. Modelling these interdependencies reveals system vulnerabilities and mitigation strategies. Quantifying Human Factors: Crew performances are related to fatigue and training levels, thus affecting repair efficiency and system recovery. This research quantified effective repair hours based on rest levels and training multipliers, highlighting the human factor influencing operational outcomes. This research shows how technological advances can complement strategic human resource management to strengthen naval operations. 64 6.2 Study Limitations While this thesis offers an overall framework for analyzing and improving naval resilience, some limitations must be acknowledged: Data Availability: Accurate analysis demands access to real-world operational data from naval fleets, which is currently unavailable because of classification and data-sharing restrictions. Modelling Simplifications: The developed framework deconstructs complex naval systems and concentrates on resource allocation and interdependencies. Although i2SIM is capable of the following, the approach taken for this study did not put too much emphasis on advanced dynamics like cascading cyberattacks, nuanced human behaviour or probabilistic failure modelling. Operational Scope: The selected scenarios could be expanded to reflect a wider range of realistic circumstances and scenarios. 6.3 Potential Future Work Addressing such limitations as well as building on the findings of the present thesis, several directions for future research are suggested: Enhanced Modelling Techniques Integration with Advanced Simulation Tools: i2SIM can be combined with MATLAB Simulink or other naval simulation platforms to model subsystem interactions and failure propagation. For 65 example, finite element analysis tools can model thermal stresses on propulsion systems during high-speed operations, which can then be used as an external feed into i2SIM. Machine Learning Integration: Machine learning algorithms may improve predictive capabilities based on historical data and real-time inputs. For example, anomaly detection algorithms could detect patterns indicating subsystem failures and provide proactive interventions. Complex Human Factor Modelling Behavioural Dynamics: Newer models should include the cognitive and psychological responses of crew members under stress, fatigue, and high-pressure conditions. This would allow a more nuanced account of emergency decision-making. Training Optimization: Trying out different training regimes may reveal the best ways to improve crew readiness. This could be scenario-based drills, virtual reality simulations, or adaptive training based on individual ability. Economic Analysis with Real-life Performance Data Validation of Cost Models: Operations data from naval fleets will be collected to validate placeholder values used for sensitivity analysis. Cost modelling is required for sound investment decisions. 66 Lifecycle Cost Assessment: Extending the analysis beyond the immediate costs and benefits of mitigation measures to potential savings from reduced failures will provide a more complete economic picture. Broader Application Domains Joint Force Operations: Using i2SIM to simulate resource allocation and resilience in joint operations involving multiple naval assets, such as carrier strike groups or multinational exercises, can improve collaboration and efficiency. 6.4 Final Remarks Advanced modelling techniques and sensitivity analyses of naval vessels are major steps forward in this thesis toward understanding and improving their resilience. Identifying and pursuing the future work suggested by researchers and naval operators will further improve the operational reliability and safety of naval assets. Human factors combined with advanced post-fault recovery measures and detailed cost models will ensure that future naval operations are flexible and able to adapt to a changing threat landscape. To fully exploit these advances, academic researchers, naval engineers and modelling experts will need to continue to collaborate. Those insights derived from such research may thus contribute to a more agile and flexible naval fleet capable of meeting the challenges of modern maritime operations. 67 Bibliography [1] E. Ho, A. Rajagopalan, A. Skvortsov, S. Arulampalam, and M. Piraveenan, âGame Theory in Defence Applications: A Review,â Sensors, vol. 22, no. 3, p. 1032, Jan. 2022, doi: 10.3390/s22031032. [2] K. Niemeyer, âModeling and Simulation in Defense,â ISIJ, vol. 12, pp. 19â42, 2003, doi: 10.11610/isij.1201. [3] P. C. Van Fenema and T. Van Kampen, âFoundational Concepts of Military Logistics,â in Handbook of Military Sciences, A. M. Sookermany, Ed., Cham: Springer International Publishing, 2021, pp. 1â25. doi: 10.1007/978-3-030-02866-4_71-1. [4] C. J. Goodrum, C. P. F. Shields, and D. J. Singer, âUnderstanding cascading failures through a vulnerability analysis of interdependent ship-centric distributed systems using networks,â Ocean Engineering, vol. 150, pp. 36â47, Feb. 2018, doi: 10.1016/j.oceaneng.2017.12.039. [5] D. Brefort et al., âAn architectural framework for distributed naval ship systems,â Ocean Engineering, vol. 147, pp. 375â385, Jan. 2018, doi: 10.1016/j.oceaneng.2017.10.028. [6] V. Mahulkar, S. McKay, D. E. Adams, and A. R. Chaturvedi, âSystem-of-Systems Modeling and Simulation of a Ship Environment With Wireless and Intelligent Maintenance Technologies,â IEEE Trans. Syst., Man, Cybern. A, vol. 39, no. 6, pp. 1255â1270, Nov. 2009, doi: 10.1109/TSMCA.2009.2025140. [7] âTowards Agile Systems Engineering in Early Stage Design for Complex Naval Vessels,â in DS 126: Proceedings of the 25th International DSM Conference (DSM 2023), The Design Society, 2023, pp. 048â057. doi: 10.35199/dsm2023.06. [8] T. J. Allensworth and J. G. Schuster, âAdding Resilience to Naval Systems for Mission Success,â Johns Hopkins APL Technical Digest, vol. 34, no. 4, p. 10, 2019. [9] N. HasanspahiÄ, S. VujiÄiÄ, V. FranÄiÄ, and L. Äampara, âThe Role of the Human Factor in Marine Accidents,â JMSE, vol. 9, no. 3, p. 261, Mar. 2021, doi: 10.3390/jmse9030261. [10] âNaval Ship Code.â The International Naval Safety Association, 2004. [Online]. Available: https://navalshipcode.org/publiccodes [11] National Transportation Safety Board (NTSB), âCollision between US Navy Destroyer Fitzgerald and Philippine-Flag Container Ship ACX Crystal,â Washington, DC, NTSB/MAR-20/02, 2020. [Online]. Available: https://www.ntsb.gov/investigations/AccidentReports/Reports/MAR2002.pdf [12] E. Nack and N. Bastian, âNavigating Challenges and Opportunities in the Cyber Domain with Sim2Real Techniques,â CSIAC, vol. 8, no. 2, pp. 24â39. [13] Z.-H. Lu, X.-W. Wang, L. Liu, X.-Y. Zhang, and C.-Q. Li, âAn efficient method for network connectivity reliability computation considering correlation of components,â Reliability Engineering & System Safety, vol. 257, p. 110805, May 2025, doi: 10.1016/j.ress.2025.110805. [14] J. Navarro, P. Fernández-Martínez, J. Fernández-Sánchez, and A. Arriaza, âRELATIONSHIPS BETWEEN IMPORTANCE MEASURES AND REDUNDANCY IN SYSTEMS WITH DEPENDENT COMPONENTS,â Prob. Eng. Inf. Sci., vol. 34, no. 4, pp. 583â604, Oct. 2020, doi: 10.1017/S0269964819000159. 68 [15] C. P. F. Shields, D. T. Rigterink, and D. J. Singer, âInvestigating physical solutions in the architectural design of distributed ship service systems,â Ocean Engineering, vol. 135, pp. 236â245, May 2017, doi: 10.1016/j.oceaneng.2017.02.037. [16] P. Zhang et al., âMarine Systems and Equipment Prognostics and Health Management: A Systematic Review from Health Condition Monitoring to Maintenance Strategy,â Machines, vol. 10, no. 2, p. 72, Jan. 2022, doi: 10.3390/machines10020072. [17] A. Coraddu, L. Oneto, A. Ghio, S. Savio, D. Anguita, and M. Figari, âMachine learning approaches for improving condition-based maintenance of naval propulsion plants,â Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, vol. 230, no. 1, pp. 136â153, Feb. 2016, doi: 10.1177/1475090214540874. [18] J. P. G. Sterbenz, E. K. C, M. A. Hameed, A. Jabbar, and J. P. Rohrer, âModelling and analysis of network resilience,â in 2011 Third International Conference on Communication Systems and Networks (COMSNETS 2011), Bangalore: IEEE, Jan. 2011, pp. 1â10. doi: 10.1109/COMSNETS.2011.5716502. [19] A. Franceschi, B. Piaggio, R. Tonelli, D. Villa, and M. Viviani, âAssessment of the Manoeuvrability Characteristics of a Twin Shaft Naval Vessel Using an Open-Source CFD Code,â JMSE, vol. 9, no. 6, p. 665, Jun. 2021, doi: 10.3390/jmse9060665. [20] Y. Lu, L. Fang, and J. Gong, âResearch of Two-Tuple Linguistic in Process of Maintenance on Risk Evaluation for Naval Vessel,â AMM, vol. 472, pp. 1094â1098, Jan. 2014, doi: 10.4028/www.scientific.net/AMM.472.1094. [21] N. Pelissero, P. M. Laso, O. Jacq, and J. Puentes, âTowards modeling of naval systems interdependencies for cybersecurity,â in OCEANS 2021: San Diego â Porto, San Diego, CA, USA: IEEE, Sep. 2021, pp. 1â7. doi: 10.23919/OCEANS44145.2021.9705918. [22] B. A. Morris, âA Framework for Identifying and Managing New Operational Requirements during Naval Vessel BatchâBuilding Programs,â INCOSE International Symp, vol. 31, no. 1, pp. 713â727, Jul. 2021, doi: 10.1002/j.2334-5837.2021.00864.x. [23] J. A. Paul and M. J. Maloni, âModeling the effects of port disasters,â Marit Econ Logist, vol. 12, no. 2, pp. 127â146, Jun. 2010, doi: 10.1057/mel.2010.2. [24] I. Jang, J. Kim, J. Lee, and Y. Seo, âEducational needs and disaster response readiness: A crossâsectional study of clinical nurses,â Journal of Advanced Nursing, vol. 77, no. 1, pp. 189â197, Jan. 2021, doi: 10.1111/jan.14570. [25] D. S. Kim, J. Y. Im, and S. H. Kim, âFeasibility Analysis of Virtualization Scheme for Distributed Combat Systems,â in Sixth International Conference on Advances in Computing, Control and Networking - ACCN 2017, Institute of Research Engineers and Doctors, Feb. 2017, pp. 138â141. doi: 10.15224/978-1-63248-117-7-53. [26] S. J. Deere, E. R. Galea, and P. J. Lawrence, âA systematic methodology to assess the impact of human factors in ship design,â Applied Mathematical Modelling, vol. 33, no. 2, pp. 867â883, Feb. 2009, doi: 10.1016/j.apm.2007.12.014. [27] T. Dimitrov, âAPPLYING ARTIFICIAL INTELLIGENCE FOR IMPROVING SITUATIONAL AWARENESS AND THREAT MONITORING AT SEA AS KEY FACTOR FOR SUCCESS IN NAVAL OPERATION,â ETR, vol. 4, pp. 49â55, Jun. 2024, doi: 10.17770/etr2024vol4.8224. 69 [28] M. Hindorf, D. Bäckström, C.-O. Jonson, A. Jonsson, and P. Berggren, âMilitary medical decision-making during training of a Swedish Navy Command and Control Team: a quantitative study of workload effects,â Feb. 08, 2024. doi: 10.21203/rs.3.rs-3924545/v1. [29] M. Zhang, âApplication of Intelligent Technology in Operation Command Assistant Decision System,â in Proceedings of the 2nd International Conference on Information, Control and Automation, ICICA 2022, December 2-4, 2022, Chongqing, China, Chongqing, Peopleâs Republic of China: EAI, 2023. doi: 10.4108/eai.2-12-2022.2327997. [30] MAN Energy Solutions, âMAN 48/60CR Project Guide â Marine Four-stroke Diesel Engine,â May 2024. [Online]. Available: https://man-es.com/applications/projectguides/4stroke/Propulsion/PG_P-II_4860CR.pdf [31] G. H. Badeer, âGEâs LM2500+G4 Aeroderivative Gas Turbine for Marine and Industrial Applications,â GE Energy, GER-4250, 2005. [Online]. Available: https://www.gevernova.com/content/dam/gepower-new/global/en_US/downloads/gas-new-site/resources/reference/ger-4250-ge-lm2500-g4-aero-gas-turbine-marine-industrial-applications.pdf [32] A. Daya and I. Lazakis, âComponent Criticality Analysis for Improved Ship Machinery Reliability,â Machines, vol. 11, no. 7, p. 737, Jul. 2023, doi: 10.3390/machines11070737. [33] M. Zhang, S. Liu, X. Hou, H. Dong, C. Cui, and Y. Li, âReliability Modeling and Analysis of a Diesel Engine Design Phase Based on 4F Integration Technology,â Applied Sciences, vol. 12, no. 13, p. 6513, Jun. 2022, doi: 10.3390/app12136513. [34] S. Fan, J. Zhang, E. Blanco-Davis, Z. Yang, and X. Yan, âMaritime accident prevention strategy formulation from a human factor perspective using Bayesian Networks and TOPSIS,â Ocean Engineering, vol. 210, p. 107544, Aug. 2020, doi: 10.1016/j.oceaneng.2020.107544. [35] D. Butler, A guide to ship repair estimates in man hours, Second edition. in Marine engineering. Amsterdam, [Netherlands]: Elsevier, Butterworth-Heinemann, 2012. [36] K. Alutaibi, A. Alsubaie, and J. Martí, âA fire management decision support systems to minimise economic losses: a case study in a petrochemical complex,â IJCIS, vol. 14, no. 2, p. 120, 2018, doi: 10.1504/IJCIS.2018.091933. [37] J. R. Martí, C. E. Ventura, J. A. Hollman, K. D. Srivastava, and H. Juárez, âI2Sim Modelling and Simulation Framework for Scenario Development, Training, and Real-Time Decision Support of Multiple Interdependent Critical Infrastructures during Large Emergencies,â presented at the RTO-MP-MSG-060, NATO Research and Technology Organisation (RTO), 2008, pp. 16-1-16â13. [Online]. Available: https://www.researchgate.net/publication/262560426_I2Sim_Modelling_and_Simulation_Framework_for_Scenario_Development_Training_and_Real-Time_Decision_Support_of_Multiple_Interdependent_Critical_Infrastructures_during_Large_Emergencies [38] José R. Martí and Mohammed Talat Khouj, âModeling Critical Infrastructure Interdependencies in Support of the Security Operations for the Vancouver 2010 Olympics,â 2010. [Online]. Available: https://www.researchgate.net/publication/263468006_Modeling_Critical_Infrastructure_Interdependencies_in_Support_of_the_Security_Operations_for_the_Vancouver_2010_Olympics [39] W. Leontief, Input-output economics, 2nd ed. New York: Oxford University Press, 1986. 70 [40] K. M. Morcos, A. T. J. Martí, and J. R. Martí, âResilience Analysis of Naval Vessels with i2SIM,â in Critical Information Infrastructures Security, vol. 15549, G. Oliva, S. Panzieri, B. Hämmerli, F. Pascucci, and L. Faramondi, Eds., in Lecture Notes in Computer Science, vol. 15549. , Cham: Springer Nature Switzerland, 2025, pp. 183â194. doi: 10.1007/978-3-031-84260-3_11. [41] H. Lee, âInfrastructure interdependencies simulation (I2Sim) system model and toolbox,â 2010, doi: 10.14288/1.0064885. [42] U.S. Army, âLeaderâs Guide to Crew Endurance,â U.S. Army Combat Readiness Center, 2016. [Online]. Available: https://safety.army.mil/Portals/0/Documents/ON-DUTY/AVIATION/Standard/Leaders_Guide_to_Crew_Endurance.pdf [43] International Maritime Organization (IMO) & International Labour Organization (ILO), âIMO/ILO Guidelines for the Development of Tables of Seafarersâ Shipboard Working Arrangements and Formats of Records of Seafarersâ Hours of Work or Hours of Rest.â International Maritime Organization, 1999. [Online]. Available: https://wwwcdn.imo.org/localresources/en/OurWork/HumanElement/Documents/ILO-IMO-Hours%20of%20rest_1.pdf [44] M. Eckstein, âNavy Stands Up Naval Surface Group Western Pacific To Train, Certify Forward-Deployed Surface Ships After Recent Collisions,â U.S. Naval Institute News, Sep. 19, 2017. [Online]. Available: https://news.usni.org/2017/09/19/navy-stands-naval-surface-group-western-pacific-train-certify-forward-deployed-surface-ships-recent-collisions [45] D. J. Dwyer, R. L. Oser, E. Salas, and J. E. Fowlkes, âPerformance Measurement in Distributed Environments: Initial Results and Implications for Training,â Military Psychology, vol. 11, no. 2, pp. 189â215, Jan. 1999, doi: 10.1207/s15327876mp1102_4. [46] U.S. Navy, âHandbook of Damage Control,â Washington, DC, NAVPERS 16191, 1945. [Online]. Available: https://maritime.org/doc/dc/index.php [47] T. Aven, âRisk assessment and risk management: Review of recent advances on their foundation,â European Journal of Operational Research, vol. 253, no. 1, pp. 1â13, Aug. 2016, doi: 10.1016/j.ejor.2015.12.023. 71 Appendices Appendix A A.1 I2SIM Operational Model 72 A.2 I2SIM Repair Model 73 Appendix B B.1 Operational Model HRTs Gas Turbine HRT: Power Output (kW) GT FP Operation (%) GT LS Operation (%) GT CS Operation (%) GT AIS Operation (%) GT EX Operation (%) Demanded Power Level (%) 30000.0 100.0 100.0 100.0 100.0 100.0 30000.0 22500.0 50.0 75.0 62.5 53.57 75.0 22500.0 21000.0 46.7 70.0 58.3 50.0 70.0 21000.0 18000.0 40.0 60.0 50.0 42.8 60.0 18000.0 15000.0 33.3 50.0 41.6 35.7 50.0 15000.0 12000.0 26.7 40.0 33.3 28.6 40.0 12000.0 0.0 0 0 0 0 0 0 Diesel Engine HRT: Power Output (kW) DE FI Operation (%) DE LS Operation (%) DE CS Operation (%) DE AIS Operation (%) DE EX Operation (%) Demanded Power Level (%) 10000.0 100.0 100.0 100.0 100.0 100.0 10000.0 7000.0 58.3 70.0 58.3 50.0 70.0 7000.0 6000.0 50.0 60.0 50.0 42.9 60.0 6000.0 5000.0 41.6 50.0 41.6 35.7 50.0 5000.0 4000.0 33.3 40.0 33.3 28.6 40.0 4000.0 0.0 0 0 0 0 0 0.0
Thesis/Dissertation
eng
Electrical and Computer Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Martí, José R.
Improving operational resilience of naval vessels through the modelling of subsystem interdependency
Text