Theses and Dissertations

Permanent URI for this collectionhttps://hdl.handle.net/10217/199889

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Open Access
The application of Agile to large-scale, safety-critical, cyber-physical systems
(Colorado State University. Libraries, 2025) Yeman, Robin, author; Malaiya, Yashwant, advisor; Adams, Jim, committee member; Simske, Steve, committee member; Herber, Daniel, committee member; Arneson, Erin, committee member
The increasing complexity of large-scale, safety-critical cyber-physical (LS/SC/CP) systems, characterized by interconnected physical and computational components that must meet stringent safety and regulatory requirements, presents significant challenges to traditional development approaches. Traditional development approaches, such as the waterfall methodology, often struggle to meet adaptability, speed, and continuous assurance demands. This dissertation explores the feasibility of applying and adapting Agile methodologies to LS/SC/CP systems, focusing on challenges like regulatory compliance and rigorous verification, while intending to prove benefits such as improved risk management and faster development cycles. Through case studies and simulations, this research provides empirical validation of Agile's effectiveness in this domain, contributing a framework for adapting Agile practices to meet the unique demands of LS/SC/CP systems. Employing a mixed-methods approach, the research comprises five key components. First, a systematic literature review (SLR) was conducted to assess the current state of Agile adoption in LS/SC/CP environments. Second, a comparative analysis of the top 10 Agile scaling frameworks was performed to evaluate their suitability for LS/SC/CP system development. Third, a survey of 56 respondents provided both quantitative and qualitative insights into industry trends, adoption patterns, and Agile's impact on LS/SC/CPs. Fourth, 25 one-on-one interviews with industry practitioners further explored the challenges, benefits, and enablers of Agile adoption in these environments. Finally, lifecycle modeling (LML) using Innoslate was utilized to develop a fictional case study, modeling the development of a mid-size low Earth orbit (LEO) satellite using both NASA's Waterfall approach (Phase A-D) and an Agile approach with a series of Minimum Viable Products (MVPs). Findings reveal that Agile methodologies, when adapted for LS/SC/CP systems, enable accelerated development cycles, reducing development time by a factor of 2.5 compared to Waterfall while maintaining safety and regulatory compliance. A key contribution of this study is the introduction of a Continuous Assurance Plugin, which integrates continuous validation within Agile's iterative processes, effectively addressing compliance and safety requirements traditionally managed through phase-gated reviews in Waterfall. Additionally, this research provides: 1. Empirical validation of Agile Scaling Frameworks and their suitability for delivering LS/SC/CP systems. 2. Quantitative and qualitative analysis of Agile's current state and impact in LS/SC/CP environments. 3. Evaluation of key enabling technologies such as Model-Based Systems Engineering (MBSE), Digital Twins, and Continuous Integration/Continuous Deployment (CI/CD) that facilitate Agile adoption for LS/SC/CP systems. This dissertation advances the understanding of Agile's role in LS/SC/CP system development, providing actionable insights and practical adaptations for organizations seeking to implement Agile in complex, safety-critical domains.
Open Access
Safeguarding sensitive data: prompt engineering for Gen AI
(Colorado State University. Libraries, 2025) Giang, Jennifer, author; Simske, Steven J., advisor; Marzolf, Gregory, committee member; Gallegos, Erika, committee member; Ray, Indrajit, committee member
Generative Artificial Intelligence (GenAI) represents a transformative advancement in technology with capabilities to autonomously generate diverse content, such as text, images, simulations, and beyond. While GenAI offers significant operational benefits it also introduces risks, particularly in mission-critical industries such as national defense and space. The emergence of GenAI is similar to the invention of the internet, electricity, spacecraft, and nuclear weapons. A major risk with GenAI is the potential for data reconstruction, where AI systems can inadvertently regenerate or infer sensitive mission data, even from anonymized or fragmented inputs. This is relevant today because we are in an AI arms race against our adversaries much like the race to the moon and development of nuclear weapons. Such vulnerabilities pose profound threats to data security, privacy, and the integrity of mission operations with consequences to national security, societal safety and stability. This dissertation investigates the role of prompt engineering as a strategic intervention to mitigate GenAI's data reconstruction risks. By systematically exploring how tailored prompting techniques can influence AI outputs, this research aims to develop a robust framework for secure GenAI deployment in sensitive environments. Grounded in systems engineering principles, the study integrates theoretical models with experimental analyses, assessing the efficacy of various prompt engineering strategies in reducing data leakage, bias, and confabulation. The research also aligns with AI governance frameworks, including the NIST AI Risk Management Framework (RMF) 600-1, addressing policy directives such as Executive Order 14110 on the safe, secure, and trustworthy development of AI. Through mixed-methods experimentation and stakeholder interviews within defense and space industries, this work identifies key vulnerabilities and proposes actionable mitigations. The findings demonstrate that prompt engineering, when applied systematically, can significantly reduce the risks of data reconstruction while enhancing AI system reliability and ethical alignment. This dissertation contributes to the broader discourse on Responsible AI (RAI), offering practical guidelines for integrating GenAI into mission-critical operations without compromising data security. This underscores the imperative of balancing GenAI's transformative potential with the societal need for robust safeguards against its inherent risks.
Open Access
Analysis of a cybersecurity architecture for satellites using model-based systems engineering (MBSE) approaches
(Colorado State University. Libraries, 2025) Johnson, Daniel, author; Bradley, Thomas, advisor; Poturalski, Heidi, committee member; Adams, Jim, committee member; Herber, Daniel, committee member; Reising, Steve, committee member
Historically, satellites have been relatively isolated from cybersecurity threats. However, during the 2020s, cyberattacks on critical ground-based infrastructure became more common and prevalent, and with the increase in technological advancement of peer adversaries, the United States government has come to recognize and define an increasing level of vulnerability in space-based assets as well. This doctoral research seeks to understand and address cybersecurity vulnerabilities inherent in commercial small-scale satellite architectures by demonstrating how model-based systems engineering (MBSE) can enable the design and analysis of a cyber-secure satellite architecture. To determine the cybersecurity vulnerabilities applicable to satellites, a scholarly review of literature on cybersecurity threats and mitigation techniques was performed and applied to satellite systems. The result of this scholarly review is an assessment of the cybersecurity threats applicable to satellites with a particular focus on small satellite architectures, and an understanding of current cybersecurity threat agents and the categories of cyber threats applicable to such satellites. Common architectures and satellite components were analyzed to determine vulnerabilities that could be exploited. The next phase of research then evaluated how industry has applied cybersecurity practices to satellite systems. We were able to determine the gaps which industry currently faces and recommended a set of generic requirements that could help create a cyber-secure satellite from early in the program lifecycle. The final phase of research synthesized the findings from the first two phases to build an MBSE model that integrates cybersecurity engineering and satellite architecture into a singular design process. We also analyzed the benefits to a company of applying the MBSE architectural process, paying particular attention to reusability of the model, cost, and human-centered benefits of committing to MBSE for multiple programs. A finding of this research is that the cybersecurity vulnerabilities for satellites are due to two main factors. First, as technology has advanced and become more available, there is a changing threat landscape where satellites launch is more accessible, increasing the risk that threat actors can compromise unprotected satellites. Second, space technology has lagged behind terrestrial information and cyber technology in its ability to adapt and overcome cybersecurity threats, creating vulnerabilities in satellite architectures. Another revelation is the disconnect between traditional software engineers and their cyber engineer counterparts, leading to a lack of understanding of key cyber-vulnerabilities during the design process. This leads to a consequential need to build cyber-protections into the design process from program initialization. Finally, the cyber tools in use today are also disconnected from the other traditional architectural design tools, leading to our conclusion that all of the tools must be integrated together under an MBSE design process, furthering the evolution of systems engineering while also encouraging the industry to incorporate cybersecurity into satellite programs from the beginning. Upon completion of this research project, the contributions are a scholarly review of the literature on cybersecurity threats and mitigation techniques in space and satellite systems, an evaluation of a set of cybersecurity requirements for satellite systems application, an MBSE example case for a cyber-security embedded satellite system, and an evaluation of the costs and benefits of an MBSE-enabled architecting process as applied to an industrial satellite system architecting process. The combination of this research represents novel contributions to the state of the field by defining the cybersecurity vulnerabilities for Space Systems and exhibiting how MBSE can aid in a cyber-secure architecting process.
Open Access
Vision based artificial intelligence for optimizing e-commerce experiences in virtual reality
(Colorado State University. Libraries, 2025) Alipour, Panteha, author; Gallegos, Erika, advisor; Bradley, Thomas, committee member; Vans, Marie, committee member; Arefin, Mohammed, committee member
Advancements in artificial intelligence (AI) and digital technologies have deeply reshaped consumer behavior and marketing strategies, demanding innovative approaches to decoding and optimizing customer engagement. This dissertation explores the potential of vision deep neural networks, generative AI, and virtual reality (VR) to analyze emotional and behavioral responses and enhance strategic business insights in digital commerce. This research focuses on convolutional neural network (CNN) architectures and evaluates their effectiveness in predicting consumer engagement through facial emotion recognition (FER). The dissertation addresses limitations in FER datasets by integrating synthetic data generated using generative adversarial networks (GANs) and real-world open data extracted from social media. This hybrid approach enhances model generalizability across diverse demographics and advertisement categories. The dissertation further investigates the role of immersive VR environments in influencing consumer engagement and purchase intent. By leveraging multi-modal causal analysis, it examines the interplay between VR design complexity, exposure sequencing, and emotional responses, providing actionable insights for optimizing e-commerce experiences. Ethical considerations are central to this research, which address biases, privacy concerns, and transparency in AI-driven decision-making. The findings contribute to the development of robust, inclusive, and scalable frameworks for personalized commerce, offering a transformative approach to understanding consumer behavior in digital environments. Through a systematic integration of vision deep learning, generative AI, and VR technologies, this dissertation bridges critical gaps in systems engineering research and business applications; advancing both theoretical understandings and practical applications in consumer engagement optimization.
Open Access
Eliciting cybersecurity goals for cyber-physical system conceptual design
(Colorado State University. Libraries, 2025) Span, Martin "Trae", author; Daily, Jeremy, advisor; Bradley, Thomas, committee member; Simske, Steve, committee member; Wise, Dan, committee member
This research contributes to the systems engineering body of knowledge by advancing security by design for Cyber-Physical Systems (CPS). It leverages Systems Thinking and Model-Based Systems Engineering (MBSE) methodologies to address both organizational and technical challenges in early-stage secure system development. The research is structured around two primary themes: (1) What recommendations can improve CPS Design Teams with respect to security? and (2) Proving secure system design be improved through early system security goal elicitation. To address the first research question, a systematic analysis utilizing Systems Thinking tools, such as iceberg models, causal loop diagrams, and system modeling, is conducted. These analyses identify the root causes of weak security design within CPS development teams, revealing systemic organizational challenges, ineffective mental models, and gaps in team member knowledge skills and abilities. The research presents targeted recommendations to enhance security considerations within design teams by implementing Systems Thinking principles, refining organizational structures, and prioritizing security training. However, findings indicate that training alone is insufficient for achieving secure CPS design, necessitating a more structured approach to security design consideration elicitation in early system development. The second research question is answered with the development of Eliciting Goals for Requirement Engineering of Secure Systems (EGRESS), a novel methodology designed to facilitate system security goal elicitation during the conceptual design phase of CPS. By addressing a critical gap in current systems engineering practices, EGRESS provides a structured and traceable approach to defining security goals before an architecture is established. This method incorporates best practices from Systems Thinking, loss-driven engineering analysis, and MBSE to ensure security is foundational in CPS design rather than an afterthought. Furthermore, the research evaluates the applicability of the Risk Analysis and Assessment Modeling Language (RAAML) standard for cybersecurity and proposes refinements to enhance its utility for security analysis in CPS design. The key contribution of this work utilizes Popper's falsification principle to evaluate the hypothesis that secure system design can be improved through early security goal elicitation. Given the lack of long-term operational data proving increased security over a system's lifecycle, falsification serves as a rigorous alternative by testing for refutation rather than statistical verification. The research demonstrates that EGRESS cannot be falsified, supporting its validity in improving secure system design. This claim is further reinforced through peer-reviewed evaluations and expert discussions within the system engineering and security communities, where, through publication, the methodology's utility was recognized and endorsed. Beyond methodology development, this research contributes to the broader systems engineering body of knowledge by addressing the distinction between requirements and security-focused system goals. It also explores the balance between common and custom SysML profiles to improve security goal elicitation. These contributions collectively support the advancement of more resilient and secure CPS architectures, aligning with the broader vision of integrating security as a fundamental design consideration alongside functionality and safety.
Open Access
Navigating the maze: the effectiveness of manufacturer support in applying user-controlled security and privacy features
(Colorado State University. Libraries, 2025) Shorts, Kelvin R., author; Simske, Steve, advisor; Daily, Jeremy, committee member; Vans, Marie, committee member; Reisfeld, Brad, committee member
Internet of Things (IoT) technologies have reshaped the home computer environment by offering extraordinary levels of convenience, automation, and efficiency. With technologies ranging from thermostats that adjust for cost savings to water leak detectors that protect homes from costly water damage, IoT devices in the residential space are here to stay. Collectively, these interconnected devices targeted for the consumer home environment are commonly referred to as a "smart home". Despite the many capabilities that smart home IoT technologies offer, many consumers/end-users are still struggling with effectively securing their internet-connected devices, safeguarding personal data, and ensuring that their smart home network remains secure from potential threats. The responsibility for safeguarding smart home IoT devices is shared by both manufacturers and consumers/end-users; however, the extent to which manufacturers are providing clear, comprehensive, and accessible guidance to assist consumers/end-users with safeguarding IoT devices remains unclear. This research study explores the level of support provided by smart home IoT manufacturers in applying user-controlled security and privacy features. User-controlled security and privacy features are settings within an IoT device that only the end-user can adjust (e.g. passwords, multi-factor authentication, device permissions, data backup, etc.). A systems engineering–focused, mixed-methods approach was adopted to evaluate how effectively smart home IoT manufacturers guide and assist consumers in understanding, implementing, and maintaining user-controlled security and privacy features in their smart home IoT devices and systems. The study unfolds across four systems engineering phases: (1) Requirements Analysis, (2) Usability Testing, (3) Focus Group Technical Deep Dive, and (4) Recommendations and Future Implementations. A review of smart home IoT device manuals, online resources, and other manufacturer-provided materials established a baseline for how well the reference material aligned with cybersecurity industry standards, best practices, and recommendations. Through structured surveys, proficiency tests, and qualitative focus group technical deep dive feedback, the study identified gaps in smart home IoT manufacturers' guidance that compromise users' ability to configure essential security settings. Employing systems engineering principles, this research study underscored the importance of user-centric design and comprehensive security and privacy guidance to help bridge the gap between cybersecurity best practices and a diverse consumer/end- user skill base.
Open Access
Improving test case diversity for functional testing in computer vision-based systems
(Colorado State University. Libraries, 2025) Reyna Pena, Ricardo, author; Simske, Steve, advisor; Troxell, Wade, committee member; Conrad, Steven, committee member; Cleary, Anne, committee member
Artificial Intelligence (AI) can serve as a powerful tool to enhance software testing within the Software Development Life Cycle (SDLC). By leveraging AI-driven testing, Quality Assurance Engineers (QAEs) can automate both simple and complex tasks, improving overall task accuracy and significantly accelerating the testing process. Traditionally, software testing is performed either manually or through automation. Manual testing, however, can be time-consuming and tedious, as QAEs must thoroughly review user stories with complex requirements, then translate them into comprehensive test cases. The challenge lies in ensuring that no critical steps are missed and that a sufficient number of test cases are created to fully meet all the requirements of the user story. Automation testing takes a different approach to that of manual testing. It involves creating scripts that can be applied to various types of software testing. However, building these scripts requires an experienced QAE who can translate test cases into programming classes, each containing multiple functions that cover the steps of the test case. Coding plays a crucial role in developing automation scripts that require updating, as well. This can lead to additional time and costs, making it essential to have the right resources to ensure a smooth deployment and maintain customer satisfaction. While both manual and automation testing are necessary tools for testing new software, they often demand more resources than smaller or even larger QAE teams can easily allocate. With advancements in AI, we can integrate computer vision (CV), a subfield of AI, to enhance automation testing by enabling navigation through websites in both mobile and desktop views. CV can be used to extract key information from applications, which is then utilized to automatically generate test cases. To further refine and complete the test case descriptions, a Large Language Model (LLM) is employed, providing more detailed and accurate documentation. In this dissertation, we introduce a novel concept designed to assist stakeholders across the SDLC during the testing phase. Additionally, we aim to evaluate the effectiveness of our approach by addressing key research questions that will guide us in determining whether using CV and LLMs to generate test cases offers broader test coverage and requires less maintenance compared to traditional manual and automated testing methods. The system is built on a supervised learning approach, utilizing 2000 labeled images from websites and mobile applications which combine represent 26 classes of UI components. These images were trained using two different CV algorithms: YOLOv8 and Detectron2, with a recommendation to explore AWS Rekognition in future research. To enhance the system's adaptability, robustness, and efficiency, we applied the Predictive Selection with Secondary engines pattern to further optimize its design. The detection results are leveraged to generate test cases, with ChatGPT, an LLM, assisting in the creation of detailed descriptions for each test case. The performance of YOLOv8 is evaluated using metrics such as mAP, precision, F1-score, and others across various YOLOv8 models trained for 100 epochs. Similarly, results for Detectron2 are evaluated over 20 epochs using two different models: R101 (RetinaNet) and X101-FPN (Faster R-CNN). ChatGPT successfully generated comprehensive test case descriptions, and various evaluation techniques, such as A/B testing, were implemented to analyze the quality of the generated text. Once the test cases were created, they were compared to both manually and automatically generated test cases to determine which areas of functional testing were covered. The primary goal of this research is to provide QAEs with an additional tool or approach (that is, a process) to enhance their software testing efforts, ultimately improving testing efficiency and coverage.
Open Access
Evaluation of a model-based approach to accrediting United States government information technology systems following the authorization to operate process
(Colorado State University. Libraries, 2025) Sanchez, Edan Christopher, author; Bradley, Thomas H., advisor; Borky, John M., committee member; Sega, Ronald, committee member; Zhao, Jianguo, committee member
This research project explores Model-Based Systems Engineering (MBSE) methodology as a modernized, alternative strategy to improve the United States Government's (USG) accreditation processes and procedures for accepting new/updated information systems. While the primary goal is to significantly accelerate the transition of advanced technology to operational environments, it is imperative that we take advantage of the potential benefits realized through the implementation of a model-based process. While this dissertation primarily focuses on defense systems within the USG domain, the principles discussed are applicable in a broader context. This research focuses on the application of MBSE to defense Information Technology (IT) systems, or simply Information Systems (IS) that requires an Authorization to Operate (ATO). Currently, the security accreditation process for obtaining an ATO for Government systems is primarily document-centric. This approach often leads to frequent schedule overruns, significantly increasing costs and negatively impacting stakeholders. This issue is particularly pronounced for large, software- and data-intensive systems, such as those utilized by the Department of Defense (DoD), Intelligence, and command and control (C2) operations. The complexity of authorization is significantly magnified when systems incorporate third-party applications requiring independent accreditation, creating cascading dependencies that impact overall system security and deployment timelines, as well as for real-time systems that must meet stringent cybersecurity requirements while adhering to strict process deadlines. Mission effectiveness is compromised when operators and end users experience delays in accessing essential tools. The trend toward implementing these types of IT systems is accelerating, highlighting the urgent need to enhance their authorization processes. The proposed approach aims to capture the existing ATO process using a formal Systems Modeling Language (SysML) model. This model will facilitate an analysis to identify bottlenecks, redundant activities, missing interfaces, and other areas of concern. Once the model is developed and analyzed, corrective actions and proposed improvements will be introduced to enhance the process model. The potential benefits will be quantified in terms of speed-to-operations, particularly regarding schedules, as well as improvements in consistency and efficiency throughout the end-to-end process, ultimately leading to a potential reduction in overall system costs. Furthermore, the anticipated gains will be validated through modeling and analysis of the enhanced process as applied to a representative IT system, also represented in SysML. This modeled IT system will reflect the cloud-centric environments currently found in operational contexts, utilizing approved tools and technologies available to development contractors. This research will assess the impact of MBSE on the ATO. It aims to measure MBSE's effectiveness in mitigating inconsistencies, streamlining system deployment timelines, enhancing quality, reducing costs, and delivering other advantages in this practical context. The conclusions drawn from this study will establish a framework for investing in the modernization of the ATO towards a systems-engineered, model-based approach, particularly within the realm of USG systems development. The model-based ATO process will facilitate integration with the federal Digital Engineering (DE) transformation as DE continues to broaden its presence within the federal systems engineering landscape.
Open Access
System design analysis for replacement of coal power plants with small nuclear reactors
(Colorado State University. Libraries, 2025) Pope, Jason, author; Bradley, Thomas H., advisor; Coburn, Timothy, advisor; Young, Peter, committee member; Ellison, Nicole, committee member
Coal power plants are the predominant energy generation technology in many countries and are a major global source of greenhouse gas emissions. A variety of policy and economic pressures are driving the replacement of this legacy technology, but the electricity generated by retiring coal power plants must be replaced and the generation capacity must be increased to meet projected electricity demand growth. A diversity of alternatives to coal generation, including nuclear power, are key components of nearly-every study of a future sustainable electricity sector. At present, small nuclear reactors have been proposed and planned by researchers, utilities, and governments to enable on-site replacement of existing coal power generators. By directly replacing coal power plants with nuclear reactors, these programs seek to develop zero-emissions, high-reliability electricity generation, at lower cost than would be possible in green-field developments. In order to assess the role that proposed coal-to-nuclear conversions could play in adoption of small nuclear generators, a variety of problems would need to be addressed. First, an improved understanding of the value of existing infrastructure at coal power plant sites is needed to assess the economic value of coal-to-nuclear conversion. Second, the schedule efficiencies available from the coal-to-nuclear conversion concept is, to date, unknown. The extent to which regional emissions trajectories would be accelerated or delayed by coal-to-nuclear conversion is unknown. Existing research has investigated the possibility of reusing operating infrastructure at coal power plants, including buildings and improvements, turbine plant equipment, electric plant equipment, and condenser and heat rejection systems. These studies hypothesize that costs, schedule, and overall emissions at reconstituted generation sites would be improved. However, detailed costing, scheduling and electricity sector transformation modeling evidence has not been generated, particularly at both the local and the global scale. To address these research gaps, this investigation specifically pursues two streams of research: (1) assessing the economic value and schedule efficiencies associated with retaining the available grid connection and cooling water at potential coal-to-nuclear sites across the United States (U.S.), and (2) assessing the global decarbonization potential associated with coal-to-nuclear conversion, with a key emphasis on modeling this transition within the U.S. and India, as these are the countries having the world's largest coal-fired power generation capacity outside of China. Results from the first area of research indicate that, when installing a single 300 MW nuclear reactor, the use of the existing cooling water source and method can save $6.8M - $23.5M annually depending on the cooling method chosen at the new site, with expected savings of $13.3M. Expected savings across the fleet are valued at $1.7B - $4.5B annually, depending on the number of reactors installed. The value of existing electrical grid connections for the 300 MW reactor can range from $25M to $53.6M, depending on geographic location, with existing grid connections across the fleet valued from $5.3B – $10.1B, again depending on the number of reactors installed. In the second area of research, schedule and timelines for coal-to-nuclear conversion are assessed, and the resulting emissions reductions are determined to help better understand the impact that a fleet-scale nuclear conversion campaign could have on decarbonization goals in both the U.S. and India. Results indicate that, while the U.S. and India presently have similar installed coal generation capacity and annual emissions, India's remaining committed emissions are approximately five times greater than those of the U.S. for both a base case and a very high-rate (46-plants across the U.S. by 2038) conversion case. Converting coal power plants to nuclear plants do realize reductions in committed GHG emissions, but the degree of national impact relies heavily on fleet composition. Nations with older generation fleets (such as the U.S.) realize annual emissions reductions from both retirements and conversions, but their committed emissions reductions are dominated by reductions due to retirements. For nations with younger fleets, coal-to-nuclear conversions have a much greater impact on committed emissions, indicating the potential of coal-to-nuclear conversion to realize global emission reductions, because the global coal fleet is relatively young (compared to the U.S. coal fleet). Collectively, these findings suggest that while U.S. decarbonization potential resulting from coal-to-nuclear conversions is limited, existing electrical grid connections and cooling water availability at existing coal power sites represent economic value that should be considered, along with other factors, by entities considering siting alternatives for small nuclear reactor installation. Both potential emissions reductions and the economic value of repowering the site, along with other factors, should be considered in the coal-to-nuclear adoption decision.
Open Access
Dynamic emissions modeling for sustainable algal carbon nanofiber production
(Colorado State University. Libraries, 2025) Whiting, Katelyn, author; Conrad, Steve, advisor; Quinn, Jason, committee member; Toman, Elizabeth, committee member
The demand for sustainable materials continues to grow, along with the need for accurate environmental impact assessments. Dynamic life cycle assessments (DLCA) provide a more comprehensive sustainability evaluation by incorporating temporal and spatial variability into traditional impact assessments. This thesis uses a dynamic life cycle assessment (DLCA) to evaluate the environmental impact of algal carbon nanofibers (ACNF), including CO₂ emissions, material use, and water consumption. A process-based model estimated industrial-scale energy use, which was converted into emissions using a dynamic normalization factor (DNF). The approach assessed four future energy mix scenarios within the RMPA sub-grid. Results suggest strong system resilience for algal-based products, particularly with advanced cultivation methods like photobioreactors. ACNF production is highly resource-intensive, with significant CO₂ emissions and upstream water use driven by the large quantities of solvent required. Mitigation strategies were explored and offer a foundation for future research. Incorporating dynamic LCA elements provided clear improvements by more accurately capturing evolving energy system impacts. This study reinforces the importance of adaptive environmental assessments when evaluating novel materials with sustainability potential. Future research should further validate this framework with industrial-scale data, expand regional applicability, and explore additional life cycle impact categories to improve the accuracy and relevance of sustainability assessments.
Open Access
Addressing low-cost methane sensor calibration shortcomings with machine learning
(Colorado State University. Libraries, 2025) Kiplimo, Elijah, author; Rainwater, Bryan, advisor; Zimmerle, Daniel J., advisor; Bradley, Thomas, committee member; Reza, Nazemi, committee member; Riddick, Stuart, committee member
Quantifying methane emissions is essential for meeting near-term climate goals and is typically done using methane concentrations measured downwind of the source. One major source of methane important to observe and remediate is fugitive emissions from oil and gas productions sites; however, installing methane sensors at thousands of sites within a production basin can be prohibitively expensive. In recent years, relatively inexpensive metal oxide sensors have been used to measure methane concentrations at production sites. Current methods used to calibrate metal oxide sensors have been shown to have significant shortcomings, resulting in limited confidence in methane concentrations generated by these sensors. To address this, we investigate using a machine learning (ML) model to convert metal oxide sensor output to methane mixing ratios. To generate data to train this model, two metal oxide sensors, TGS2600 and TGS2611, were collocated with a trace methane analyzer downwind of controlled methane releases. A comparison of histograms generated using the analyzer and metal oxide sensors mixing ratios show overlap coefficients of 0.95 and 0.94 for the TGS2600 and TGS2611, respectively. Overall, our results showed there was good agreement between the ML derived metal oxide sensors' mixing ratios and those generated using the more accurate trace gas analyzer. This suggests that the response of lower-cost sensors calibrated using ML could be used to generate mixing ratios with higher precision and accuracy, thereby reducing the cost of sensor deployments, and allowing for timely and accurate tracking of methane emissions.
Open Access
Factors influencing driver response toward an instrument cluster cyberattack: experience, awareness, and training
(Colorado State University. Libraries, 2025) Lanigan, Trevor F., author; Gallegos, Erika, advisor; Daily, Jeremy, committee member; Nelson, Niccole, committee member
Commercial Motor Vehicles (CMVs) and the trucking industry are often referred to as the backbone to the supply chain in the United States. With this has come efforts to modernize heavy vehicles just like their passenger vehicle counterparts in order to improve the safety, performance, and efficiency of the transportation of goods and materials. However, the introduction of advanced cyber-physical systems in heavy vehicles makes available a new vulnerability not previously encountered: cyberattacks. The objective of this thesis is to (1) evaluate drivers' responses to an unexpected cyberattack, (2) evaluate how awareness of the cybersecurity threat on their vehicle influences driver behavior, and (3) evaluate how the provision of a cyberattack response protocol influences driver performance. An on-road driving study with 50 participants was conducted to measure drivers' response to an unexpected cyberattack while operating a medium heavy-duty vehicle (GVWR 26,000lbs; Class 6). Each participant was randomly assigned to one of three experimental groups which received varying levels of information prior to the start of the drive. The Control group received no information regarding a possible cyberattack threat on their vehicle. The Aware group received a warning regarding a possible cyberattack threat on their vehicle. The Aware + Protocol group received the same warning as the Aware group along with a basic cyberattack response protocol. Within each group, six to seven of the participants were professional drivers (e.g., commercial truck driver, firefighter, bus driver), while the remaining 10 to 11 participants in each group were standard licensed drivers. Each of the participants experienced the same driving route and cyberattack scenario with regard to type, location, timing, and execution. Participant driving responses were measured using data collected from the vehicle CAN bus, and Racelogic VBOX3i GNSS and IMU sensors. Participant physiological responses (heart rate and electrodermal activity) were measured using an Empatica E4 wearable. Additionally, participants completed a survey at the end of the experimental session to assess their driving experience, risk taking tendencies, and interpretation of the cyberattack. The findings highlight the essential role of awareness and response protocols in enhancing a driver's response to an unexpected vehicle cyberattack. The Aware + Protocol group achieved a 100\% stop rate among both Standard and Professional drivers, showcasing the transformative impact of awareness and clear response guidelines compared to the Control group stop rate of 9\% for Standard and 83\% for Professional drivers. The Aware + Protocol group also traveled the shortest distance during the cyberattack, with Standard drivers covering 224 meters (0.139 miles) and Professional drivers 254 meters (0.158 miles), compared to the Control group's 828 meters (0.514 miles) for Standard drivers and 520 meters (0.323 miles) for Professional drivers. Furthermore, the Aware + Protocol group demonstrated the shortest reaction times, averaging 7.53 seconds, versus 16.12 seconds in the Aware group and 30.29 seconds in the Control group. These results emphasize that awareness alone is insufficient; explicit instructions significantly enhance drivers' ability to respond promptly and effectively to cybersecurity threats. By informing drivers and providing response protocols, their ability to respond appropriately to cyberattacks can be significantly improved. This information can be applied in several practical ways, such as developing cyberattack response training programs for all drivers, especially those operating heavy vehicles. Additionally, public service announcements and in-vehicle alerts could be effective in increasing awareness of cyberattack vulnerabilities. Public service announcements broadcasted through various media channels can inform a wide audience about the risks of vehicle cyberattacks and inform drivers on how to recognize and respond to such threats. In-vehicle alerts can offer real-time information and instructions, guiding drivers on immediate actions to take when a cybersecurity threat is detected.
Embargo
Significant reductions in ethane emissions in the Denver-Julesburg Basin from 2015 to 2021 from oil and natural gas operations
(Colorado State University. Libraries, 2025) Ngulat, Mercy Chemutai, author; Santos, Arthur, advisor; Zimmerle, Daniel, advisor; Olsen, Daniel, committee member; Bradley, Thomas, committee member
Given the low or nonexistent ethane (C2H6) signature in biogenic emissions, top-down studies of methane (CH4) emissions use wellhead gas composition to determine the associated ethane-to-methane (C2/C1) ratio, which is then used to differentiate oil and natural gas (O&NG) and biogenic sources and to attribute the contribution of CH4 emissions from oil and gas (O&G) sites. However, this ratio may vary within and across different basins, emitting sources, and facility configurations. Understanding these variations is essential for accurately attributing CH4 emissions to different O&G sectors and sources and to subsequently inform policy decisions and emissions mitigation strategies. This study investigates stack test data from Fourier Transform Infrared Spectroscopy (FTIR) analysis of the exhaust gas for 10 four-stroke rich burn (4SRB) engines, 54 four-stroke lean burn (4SLB) with pre-chamber, and 104 4SLB without pre-chamber provided by past research studies and O&G operators. Stack tests are conducted to determine the compressor driver's (engine) compliance with emissions limits. Fuel gas is fed into natural gas (NG) fired engines for compression purposes and stack tests determine the amount of specific pollutants in the exhaust gas. Fuel gas composition and stack test data are used to calculate the fuel and exhaust gas C2/C1 ratios and the CH4 and C2H6 destruction efficiencies for these engine categories. The results show that engines preferentially combust heavier hydrocarbons over CH4, evidenced by consistently higher destruction efficiency (DE) for C2H6 than CH4 across all engine types. Additionally, recent design modifications in O&G production sites, including tankless facility configurations, may have led to a reduction in CH4 emissions and a more pronounced decrease in C2H6 emissions. In the Denver-Julesberg (DJ) basin in Colorado, estimated emissions from 2015 to 2021 show a 73.9% increase in NG production, stable CH4 emissions, and a 54.2% reduction in C2H6 emissions. The analysis also reveals a shift in emissions from the production sector to the midstream sector and suggests that when the wellhead C2/C1 ratio was used 22 to attribute CH4 emissions from the oil and gas sector, top-down methods may have overestimated CH4 emissions by an average of 159.65% in 2015, and by 10.86 % in 2021.
Open Access
Engineering and scaling cement-based carbon storage systems
(Colorado State University. Libraries, 2024) Winters, Dahl, author; Simske, Steven, advisor; Bradley, Thomas, committee member; Arabi, Mazdak, committee member; Troxell, Wade, committee member; Goemans, Christopher, committee member
This work is a contribution to the body of knowledge surrounding cement-based carbon storage systems, their engineering, and their scaling to meet the requirements of global sustainability in a relevant timeframe. Concrete is the most produced material by weight per year, surpassing water and all biomass we use per year, thus requiring by virtue of its total mass the largest share of total energy produced. Today, it is a source of net greenhouse gas emissions and environmental damage because of our appropriation of natural resources for its use in construction. However, it could serve as our largest land-based engineered sink for such emissions. Such potential is the focus of this work, addressed not only by experiments to improve the engineering of cement-based carbon storage systems, but also by suggested practices to achieve scale for such systems to have a beneficial impact on our economy and environment. The ubiquity of concrete means that cement-based carbon storage can also be ubiquitous, offering continued opportunities for carbon removal and sequestration within built materials. To engineer and scale the world's largest product into its largest engineered carbon sink, this research focuses on the use of biochar and calcium carbonate within structural and non-structural concrete uses, such as tetrapods: structures offering the benefits of reduced sand mining, protections against sea level rise, and enabling cement industry decarbonization. The results demonstrated that 4 wt% biochar with 1.5 wt% CaCO3 can replace cement for carbon storage while maintaining sufficient compressive strength. Along with the use of 30 wt% biochar as aggregate, 100,000 10-tonne tetrapods could sequester 1 million tonnes of CO2. Over a year of global emissions, 40 Gt CO2, could be stored in such stacked tetrapods within a land area smaller than Kuwait, 17,400 km2. Thus, this work contributes to the engineering of systems with industrial significance capable of countering the effects of global warming at meaningful scales.
Open Access
Enhancing flight testing leveraging software testing techniques implemented in model-based systems engineering
(Colorado State University. Libraries, 2024) Alvarado, Jose L., Jr., author; Bradley, Thomas H., advisor; Herber, Daniel, committee member; Simske, Steven, committee member; Windom, Bret, committee member
The Department of Defense (DoD) is significantly shifting toward digital engineering across all systems engineering lifecycle phases. A vital aspect of this transformation is the adoption of model-based testing methodologies within the Test and Evaluation (T&E) processes. This dissertation investigates a grey box Model-Driven Test Design (MDTD) approach that leverages model-based systems engineering (MBSE) artifacts to create flight test scenarios and plans and compares this novel approach to the traditional document-centric methods. The study utilizes the Systems Modeling Language (SysML) to represent artifacts, enabling a comparative analysis between traditional and MDTD processes. Through a case study involving a training system used by the Air Force Operational Test and Evaluation Center (AFOTEC), the dissertation evaluates the MDTD process's effectiveness in generating validated test scenarios and plans that align with established methods. Two additional case studies demonstrate the reuse of SysML elements across different systems under test (SUT), highlighting the benefits, costs, and practical applications of this approach in operational flight testing. The findings include metrics such as Model Reuse Percentage (MR%), Reuse Value Added (RVA), and System Usability Survey (SUS) scores, which measure the reusability of model artifacts and the usability and effectiveness of the "AFOTEC Methodology" model approach in generating flight test plans. This research underscores the importance of model-based testing in operational flight testing and supports the DoD T&E community's ongoing move toward a fully integrated digital engineering ecosystem.
Open Access
Geographically-resolved life cycle assessment and techno-economic analysis of engineered climate solutions with an innovative framework for decision support
(Colorado State University. Libraries, 2024) Greene, Jonah Michael, author; Quinn, Jason C., advisor; Reardon, Kenneth, committee member; Coburn, Tim, committee member; Baker, Daniel, committee member
The urgent challenge of addressing climate change requires a thorough evaluation of engineered solutions to ensure they are both economically viable and environmentally sustainable. This dissertation performs a comprehensive assessment of two key climate technologies: microalgae biorefineries for biofuel production and anaerobic digestion (AD) systems for reducing greenhouse gas (GHG) emissions on dairy farms. Using high-resolution life cycle assessment (LCA) and techno-economic analysis (TEA), it provides detailed insights into the sustainability performance of these technologies. In addition, this work goes further by introducing a decision-support framework that improves the interpretation of LCA and TEA results, enhancing decision-makers' ability to form sustainable policies and implement actionable outcomes that drive the transition to green energy solutions. The first segment of this dissertation integrates high-resolution thermal and biological modeling with LCA and TEA to evaluate and compare two different microalgae biorefinery configurations targeting renewable diesel (RD) and sustainable aviation fuel (SAF) production in the United States. A dynamic engineering process model captures mass and energy balances for biomass growth, storage, dewatering, and conversion with hourly resolution. These configurations support facilities in remote areas and cultivation on marginal lands, enabling large-scale biofuel production. The two pathways under examination share identical biomass production and harvesting assumptions but differ in their conversion processes. The first pathway evaluates hydrothermal liquefaction (HTL) to produce RD, while the second explores the Hydroprocessed Esters and Fatty Acids (HEFA) process to produce SAF. Results indicate that the Minimum Fuel Selling Price (MFSP) for RD could decrease from $3.70-$7.30 to $1.50-$4.10 per liter of gasoline equivalent, and for SAF from $9.90-$19.60 to $2.20-$7.30 per liter under future scenarios with increased lipid content and reduced CO2 delivery costs. Optimization analyses reveal pathways to achieve an MFSP of $0.75 per liter and 70% GHG emissions reductions compared to petroleum fuels for both pathways. Additional analysis covers the water footprint, land-use change emissions, and other environmental impacts, with a focus on strategic research and development investments to reduce production costs and environmental burdens from microalgae biofuels. Beyond renewable transportation fuels, achieving a sustainable energy future will require innovations in the circular economy, such as waste-to-energy systems that reduce GHG emissions while simultaneously producing renewable energy. Accordingly, the second segment of this dissertation examines the GHG reduction potential of adopting AD technology on large-scale dairy farms across the contiguous United States. Regional and national GHG reduction estimates were developed through a robust life cycle modeling framework paired with sensitivity and uncertainty analyses. Twenty dairy configurations were modeled to capture key differences in housing and manure management practices, applicable AD technologies, regional climates, storage cleanout schedules, and land application methods. Monte Carlo uncertainty bounds suggest that AD adoption could reduce GHG emissions from the large-scale dairy industry by 2.45-3.52 million metric tons (MMT) of CO2-equivalent (CO2-eq) per year when biogas is used solely in renewable natural gas programs, and as much as 4.53-6.46 MMT of CO2-eq per year when combined heat and power is implemented as an additional biogas use case. At the farm level, AD technology may reduce GHG emissions from manure management systems by 58.1-79.8%, depending on the region. The study highlights the regional variations in GHG emissions from manure management strategies, alongside the challenges and opportunities surrounding broader AD adoption. It is vital to confirm that engineered climate solutions offer real improvements and to identify key enhancements needed to replace existing technologies. This process hinges on effective policy and decision-making. To address these challenges, the final segment of this dissertation introduces the Environmental Comparison and Optimization Stakeholder Tool for Evaluating and Prioritizing Solutions (ECO-STEPS). ECO-STEPS offers a decision-support framework that utilizes outputs from LCA and TEA to help decision-makers evaluate and prioritize engineered climate solutions based on economic viability, environmental impacts, and resource use. The tool's framework combines stakeholder rankings for key sustainability criteria with diverse statistical weighting methods, offering decision support aligned with long-term sustainability goals across various technology sectors. Applied to a biofuels case study, ECO-STEPS compares algae-based RD, soybean biodiesel (BD), corn ethanol, and petroleum diesel, using an expert survey to determine criteria rankings. Results indicate that soybean BD is a strong near-term solution for the biofuels sector, given its economic viability and relatively low environmental impacts. In contrast, corn ethanol, while economically competitive, demonstrates poor environmental performance across multiple sustainability themes. Algae-based RD emerges as a promising long-term option as ongoing research and development reduce costs. The results of this case study illustrate that ECO-STEPS provides a flexible and comprehensive framework for stakeholders to navigate complex decision-making processes in the pursuit of sustainable climate solutions. In conclusion, the integration of high-resolution LCA, TEA, and a stakeholder-driven decision-support framework in this dissertation presents a comprehensive approach to evaluating engineered climate solutions. The results from these studies provide geographically resolved insights into the sustainability performance of key climate technologies, offering actionable pathways for optimizing biofuel production, reducing GHG emissions, and supporting sustainable decision-making to advance the transition to a green economy.
Open Access
Qualitative comparative analysis of software development practices translated from scene to screen using the real-to-real method for inter-industry learning
(Colorado State University. Libraries, 2024) Hawkey, Barry, author; Vans, Marie, advisor; Simske, Steve, committee member; Gallegos, Erika, committee member; Rodgers, Tim, committee member
Many projectized industries, in fields as diverse as healthcare, live theater, and construction, have developed sets of specific project management practices that are consistently associated with success. These practices – assignable activities, tasks, processes, and methods – have been acquired through decades of lessons painfully learned by project teams. Well-known, existing processes allow project teams to capture and disseminate these best practices and lessons learned between projects and across organizations, allowing new teams to benefit from previous efforts. Although overall progress may at times seem fitful, these knowledge-sharing processes have allowed each industry to improve their project management methodologies over time. Unfortunately, the specificity required to make a practice actionable, assignable, and beneficial within the domain of one industry also renders it difficult to apply in another. There is no formal method, or method in widespread use, for the translation of specific project management practices across the boundaries of industry and knowledge domains. As a result, most of the benefits of these learnings - each industry's collective knowledge of best practices – are restricted to their original domain, providing little guidance to project teams in other industries. This research examines several previous attempts to apply project management practices across multiple domains and synthesizes a novel method for such inter-industry learning. The Real-to-Real method presented here begins by identifying potential barriers to project success within a target industry. Next, an industry that has developed different approaches to similar challenges is chosen as a source of inspiration. After holistically examining project management practices within that source industry, a set of evident principles is synthesized through an iterative process of inductive reasoning which explain that industry's approach to project management and these shared challenges. Using these principles as a transformative intermediary, a set of specific practices suitable for the domain of the target industry can then be identified or developed, mirroring or paralleling practices used in the source industry. These practices may lead to improved project outcomes when used in target industry projects that have characteristics similar to those found in the source industry. This method may allow for the translation and practical application of hard-won project management expertise across many projectized industries, potentially improving project outcomes in multiple fields. To provide an illustrative example of the Real-to-Real method in use, the software development industry is selected as an example target, and barriers to project success in that domain are examined. A review of the existing literature finds that the lack of simple, heuristic guidance on tailoring existing practices to better support hedonic requirements, which specify the intended emotional response of the user, may be a significant source of risk within the target industry, although the effect of hedonic requirements on project outcomes has not yet been empirically determined. With this potential source of risk in mind, the film industry is selected as a source of inspiration, as projects there share many similarities with software development projects and must routinely consider hedonic requirements. A holistic evaluation of film production project management practices suggests four evident, explanatory principles guiding that industry's approach to managing projects. This research then identifies and proposes a set of specific practices, suitable for software development projects, which also support or adhere to these same principles, thus mirroring practices used in film production projects. To support these findings, the identified software development practices are situated within existing theory, and potential mechanisms by which they may consistently lead to improved project outcomes when used in projects with high levels of hedonic requirements are discussed. A series of semi-structured interviews with experienced practitioners in the film industry are then conducted to verify an accurate understanding of film production project management practices, the synthesized explanatory principles, and the pairing of each principle to a set of related practices through. Next, a second series of interviews with experienced practitioners in the software development industry is used to verify the selection of software development practices supporting these principles. To empirically validate these findings, and to determine the effect of hedonic requirements on project outcomes, a practitioner survey is then conducted, measuring project success, use of the identified practices, and the level of hedonic requirements in 307 software development project cases in five culturally similar countries. First, the perceived criticality of hedonic requirements is compared to five measures of project success, to determine the impact of such requirements on project outcomes. Then, using Qualitative Comparative Analysis, causal recipes of the identified practices that consistently resulted in project success, across these same measures, are identified for projects with varying levels of hedonic requirements. These results validate the benefits of the identified principles and practices to projects with high levels of hedonic requirements, and provide simple, heuristic guidance to software development project teams on how to quickly and effectively tailor their management practices to better support individual projects based on the criticality of such requirements. This guidance may serve to significantly improve outcomes in software development projects with high levels of hedonic requirements. These results also help to validate the Real-to-Real method of translating management practices across industry and knowledge domains, potentially enabling additional opportunities for valuable inter-industry learning.
Open Access
Merging systems engineering methodologies with the Agile Scrum framework for Department of Defense software projects
(Colorado State University. Libraries, 2024) Rosson, Dallas, author; Bradley, Thomas, advisor; Batchelor, Ann, advisor; Coleman, David, committee member; Eftekhari Shahroudi, Kamran, committee member; Wise, Dan, committee member
Only large-scale Department of Defense (DoD) software projects executed under the direction of the DoD Instruction 5000.2, Operation of the Adaptive Acquisition Framework, are required to follow rigorous systems engineering methods. Many software projects lack the benefits of established systems engineering methodologies and good engineering rigor and fail to meet customer needs and expectations. Software developers trained in the use of the various Agile frameworks are frequently strongly opposed to any development methodology that could be viewed as infringing on the principles of the Agile Manifesto. Agile projects, by their nature, embrace the concept of change, but uncontrolled change leads to project failures whereas controlled change can lead to sustained and innovative forward progress. In order to improve the results of these vital software projects, Department of Defense (DoD) software projects require a methodology to implement systems engineering rigor while still employing Agile software practices. The Agile Scrum framework alone is not rigorous enough to fully document customer needs as User Stories are written tracking only who, what, and why at a non-atomic level and commonly never looked at again after development needs are met. Systems engineering methods alone are not flexible enough to take advantage of the inherent nature to change capability required in software projects, which require flexibility in schedule and requirements. A new methodology, the Systems Engineering Focused Agile Development method, takes a rigor-flexibility-rigor approach to development and makes use of the strengths of the Agile Scrum framework with the best practices of systems engineering methodologies resulting in a common language that better allows cross-functional teams to communicate project needs while also allowing software developers to maintain flexibility in the execution of software projects. This research has determined that the thoughtful blending of Agile systems engineering and modern systems engineering methods has the potential to provide DoD software projects with benefits to cost, schedule, and performance.
Open Access
A new automotive system architecture for minimizing rear-end collisions
(Colorado State University. Libraries, 2024) Rictor, Andrew, author; Chandrasekaran, Venkatachalam, advisor; Cheney, Margaret, committee member; Herber, Daniel, committee member; Simske, Steven, committee member
Advanced Driver Assistance Systems, more frequently referred to as ADAS, are intelligent systems integrated into newer automotive vehicles to improve safety and minimize accidents. These systems utilize radar, sonar, lidar and camera sensors mounted around the vehicle to maintain situational awareness of the vehicle and the surrounding environment. The majority of ADAS that focus on collision avoidance modify the host vehicle's operation. Some existing ADAS will stop the vehicle, sound an audible alert, initiate internal warning lights or dash warning messages, and prevent lane change operations. The ADAS proposed and detailed here focuses on enabling the host vehicle to communicate with the inbound vehicle's driver via the brake lights so that the driver has the opportunity to modify the inbound vehicle's operation before a collision occurs. This is called the Aft Collision Assist (ACA). This work presents the Model Based System Engineering (MBSE) diagrams, SIMULINK models and simulation of the ACA, data derivation utilized in the simulations, validation with empirical data, and future work for optimizing the ACA's algorithms.
Open Access
Relational and technological process concept utilizing a human-in-the-loop-centered methodology for USSOCOM
(Colorado State University. Libraries, 2024) Corl, Kenneth Casselbury, author; Gallegos, Erika, advisor; Bradley, Thomas, committee member; Simske, Steve, committee member; Mumford, Troy, committee member; Crocker, Jerry, committee member
The Department of Defense (DoD) employs broad human factors requirements across various applications, resulting in a universal application of the same standards to a multitude of DoD acquisition systems. In unconventional warfare, specifically within missions conducted by US Special Operations Command (USSOCOM), operators face intensified workloads and domain-specific challenges that current human factors considerations do not adequately address. The objective of this dissertation aims to introduce and validate the Relational and Technological Capstone (RTC), which expands upon existing human factors requirements through both architectural and behavioral diagrams in a well-defined set of methodology-driven process steps. In referencing the system lifecycles as defined by the Defense Acquisition University (DAU) and the International Council on Systems Engineering (INCOSE), the objective is to diversify and enhance the consideration of Human Systems Integration (HSI) requirements in USSOCOM platforms by addressing the unique challenges posed by intensified workloads and domain-specific ontologies. The RTC employs a methodology-driven approach utilizing architectural, behavioral, and parametric diagrams. It integrates with Model Based Systems Engineering (MBSE) and the Systems Modeling Language (SysML) to improve the design of human-system interactions, incorporating a Special Operations Task List and Performance Shaping Factors (PSFs) into aggregated performance values. The results of this dissertation demonstrate the efficacy of RTC within MBSE, showcasing its value through improved design processes and as a foundation for new programs. The RTC can integrate existing models to further benefit customer needs through initiatives like Engineering Change Proposals (ECPs) as well as assist starter models for new programs and projects. The containment tree format aids in developing USSOCOM MBSE and opens possibilities for automation tools as well as an easily transferrable modeling package for future use on all complex systems. Continual use of RTC contributes to the maturity of MBSE models and diagrams, fostering the evolution of a federation-of-models and Program of Record standards. This not only benefits subsequent SOCOM programs and projects but also facilitates the emerging field and methodology of mission engineering to realize and forecast capability gaps before a system reaches the implementation and integration phase. The ultimate goal is to center the RTC around the operator, ensuring man-machine compatibility and optimization throughout special operation acquisitions.