Over the past few years, the aviation industry has been facing increasing pressure to reduce its environmental footprint amidst the rapid growth in global air travel. As such, this paper examines sustainable aviation using the ‘Four-Pillar Approach’ which consists of design technology, market-based measures, operational and infrastructural improvements and alternative fuels. Each of these four pillars is evaluated on its effectiveness in achieving a carbon-neutral growth, while identifying the stakeholders and highlighting their associated complexities. 

Following that, a detailed life cycle assessment (LCA) and life cycle cost (LCC) analyses was performed to compare sustainable aviation fuels (SAFs) with conventional aviation jet fuel in order to assess the environmental benefits and economic trade-offs of utilizing SAFs. The results show that while SAFs has much lower lifecycle emission values as compared to conventional aviation jet fuel, the lifecycle cost of SAFs is approximately 3 times more expensive than conventional aviation jet fuel, making it uneconomical to be adopted. 

Finally, an aircraft conceptual design and sizing study was performed for a strut[1]braced wing (SBW) aircraft to assess its performance improvements in terms of lifecycle emissions relative to a conventional tube-and-wing (TAW) aircraft configuration. The results show that utilizing SAFs and advanced aircraft designs can result up to a 75% reduction in carbon emissions as compared to conventional tube-and-wing (TAW) aircraft configuration.

Technological advances have transformed unmanned aerial systems (UAS) into smaller and cheaper systems that are harder to detect and deployable in large numbers. The UAS could be used to saturate defenses, threaten the battlefield logistical supply and disrupt the battlefront, highlighting the importance of credible counter-UAS (C-UAS) systems. 

Analysis on protection against UAS used a modelling and simulation (M&S) software called Swarm Commander Tactics, representing a virtual scenario of a cargo convoy, protected by C-UAS entities, under assault from adversarial suicide UAS. Factors that affected convoy survivability were identified and investigated, including total UAS quantity, initial UAS location, C-UAS weapon types and C-UAS positions. Statistical analysis using the software, Minitab, showed the UAS quantity and C-UAS weapon had a significant impact on convoy survival. These factors were used to scale and demonstrate a victory for the adversary, where the C-UAS platforms’ effectiveness relied on their ability to rapidly eliminate the incoming threat. 

Recommendations were proposed to enhance utilizing UAS as an adversary and to counter the effectiveness of UAS as defenders. Improvements to the modelling and simulation effort were proposed as future work to conduct further investigation into the dynamic interaction of factors influencing the UAS and C-UAS systems effectiveness. 

Advanced warfighting techniques of the modern age consist of a variety of new technology, especially in the field of unmanned systems. Unmanned Surface Vessels (USV) and Unmanned Aerial Systems (UASs) are an outstanding problem in current conflict, inflicting damage and destruction to the merchant and Military Sealift Command (MSC) fleets in the red sea. The project proposes alternative solutions for combatting such USV and UAS threats in threatened areas for defensive purposes. Through analysis informed by modeling and simulation, we propose an effective and cost considered implementation to the fleet. The analysis includes considerations of known current and future technology with the goal of maximising survivability and effectiveness. The results present an analysis of alternatives for defense of merchants and MSC ships under attack by unmanned systems. 

Drone swarms have emerged as a defining feature of modern warfare. This thesis uses agent-based simulation to examine how force composition, weapon technology, and decoy tactics shape the outcomes of swarm attacks. The model pits a layered defender equipped with a mix of weapon and sensor systems to protect an asset against an attacker who employs suppression drones to degrade defences, lethal drones to strike the target, and decoy drones to overwhelm defensive firepower. We study 23 factors–such as the number of anti-aircraft guns, the ability of sensors to distinguish lethal drones from decoy drones, and drone speed–using a nearly orthogonal and balanced design of experiments to identify which factors most influence mission success. Our analysis shows that the defender’s success depends less on the number of systems and more on their placement, rate of fire, and classification accuracy, while the attacker’s success depends on concentrated, lethal-heavy drone compositions supported by decoys rather than sheer swarm size.

This thesis evaluates hybrid propulsion systems for naval destroyers, integrating gas turbines with batteries or fuel cells to improve endurance, fuel efficiency, and operational flexibility. Physics-based modeling and mission scenario analysis are applied to compare performance against conventional propulsion. 

Findings can inform future naval ship design, support decarbonization efforts, reduce lifecycle fuel costs, and enhance mission readiness through optimized speed–power trade-offs and alternative refueling strategies. 

Upgrading armored vehicles while sustaining battlefield readiness remains one of the most complex challenges facing modern militaries. Upgrade activities inevitably withdraw vehicles from service, strain limited resources, and risk reducing operational availability at critical moments. This study develops and evaluates alternative upgrade strategies to address these challenges, focusing on minimizing upgrade duration and introducing higher-reliability components earlier in the fleet lifecycle. Using ExtendSim to develop a detailed simulation model calibrated with reliability, maintenance, and upgrade process data, three strategies – baseline sequential upgrades, parallel upgrades, and upgrade-during-maintenance were compared.

This thesis develops and evaluates a hybrid cost measurement framework that integrates a key performance indicator (KPI)–based Cost-Based Decision Support System (CBDSS) with Time-Driven Activity-Based Costing (TDABC) to improve real-time cost visibility and decision-making in smart manufacturing. The research follows a systems engineering Vee model approach, beginning with baseline models of CBDSS and TDABC and progressing toward a unified hybrid framework. All models are implemented in a spreadsheet environment using Excel and are based on a combination of operational data from a defence manufacturing context and supplementary synthetic inputs. The hybrid model is subjected to scenario-based testing, factorial design of experiments (DOE), and stochastic input variations to ensure robustness. The findings show that the hybrid approach enhances cost traceability, aligns performance metrics with financial outcomes, and provides managers with actionable insights for optimising production efficiency, resource allocation, and return on investment (ROI). The study contributes to the body of knowledge by addressing the limitations of single-framework costing approaches and offering a practical, KPI-driven solution for dynamic manufacturing environments.

Distributed simulations using the High-Level Architecture (HLA) face challenges when federates must combine predictable periodic behaviors with rapid responsiveness to asynchronous events, particularly in human-in-the-loop scenarios where rollback-based synchronization is impractical. Traditional Time Advance Request (TAR) mechanisms ensure strict periodicity, while Next Event Request (NER) supports responsiveness, but existing approaches typically assign these modes statically to different federates. This thesis investigates whether a single federate can achieve both objectives without violating HLA’s conservative synchronization rules. A hybrid synchronization scheme is designed and implemented in the open-source Portico Runtime Infrastructure (RTI), combining NER to react promptly to events with TAR catch-up to maintain exact periodic cadence. Simulation experiments evaluate reaction latencies, cadence accuracy, and causality preservation under mixed conditions. The findings demonstrate that adaptive behavior can be achieved within existing HLA services by alternating between NER and TAR at each step, delivering low-latency responses and zero cadence drift without rollback or RTI modification. 

Heating, ventilation, and air conditioning (HVAC) systems are industrial control systems that form the backbone of the working environment within modern buildings. They have historically been designed more for operational needs rather than with security in mind. As such, HVAC systems are vulnerable to cyberattacks which can cause significant damage, considering the massive and powerful structures that these systems often control. This research analyzed the vulnerabilities in a fan-coil unit (FCU) that used the industrial BACnet protocol used in many HVAC networks around the world. Additionally, it assessed vulnerabilities in the BACnet protocol and studied how it was implemented in one commercial building automation management product. Proof-of-concept exploits of discovered vulnerabilities were developed to demonstrate potential attacks. 

This research develops a machine learning–based framework for terrain analysis to support data-driven decision-making in military operations. It integrates open-source geospatial data, weapon effect simulations, and max–min network flow optimization to train supervised learning models capable of identifying and ranking Grounds of Tactical Importance (GTI) within a given Area of Operations (AO). The framework was validated through a case study on the 2022 Russia–Ukraine War, where predictions showed strong alignment with actual Ukrainian defensive strategies around Siversk. Elevation data was subsequently incorporated to capture vertical dominance and line-of-sight considerations that are critical in tactical defense. Looking ahead, the thesis proposes expanding the framework by introducing military-grade Geospatial Intelligence (GEOINT) and labels extracted from operational plans. This enhancement would align the model more closely with military logic, enabling it to function as a robust decision-support tool for commanders and analysts.