Department of Systems Engineering

Permanent URI for this communityhttps://hdl.handle.net/10217/199888

This digital collection includes faculty/student publications, theses, dissertations, and datasets from the Department of Systems Engineering.

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Open Access
A balance of design methodology for enterprise quality attribute consideration in System-of-Systems architecting
(Colorado State University. Libraries, 2019) Nelson, Travis J., author; Borky, John M., advisor; Sega, Ronald M., advisor; Bradley, Thomas K., committee member; Roberts, Nicholas H., committee member
An objective of System-of-Systems (SoS) engineering work in the Defense community is to ensure optimal delivery of operational capabilities to warfighters in the face of finite resources and constantly changing conditions. Assurance of enterprise-level capabilities for operational users in the Defense community presents a challenge for acquisitions in balancing multiple SoS architectures versus the more traditional system-based optimization. The problem is exacerbated by the complexity of SoS being realized by multiple, heterogeneous, independently-managed systems that interact to provide these capabilities. Furthermore, the comparison of candidate SoS architectures for selection of the design that satisfies the most enterprise-level objectives and how such decisions affect the future solution space lead to additional challenges in applying existing frameworks. As a result of the enormous challenge associated with enterprise capability development, this research proposes an enterprise architecting methodology leveraging SoS architecture data in the context of multiple enterprise-level objectives to enable the definition of candidate architectures for comparison and decision-making. In this context, architecture-based quality attributes of the enterprise (e.g., resilience, agility, changeability) must be considered. This research builds and extends previous SoS engineering work in the Department of Defense (DoD) to develop a process framework that can improve the analysis of architectural attributes within an enterprise. Certain system attributes of interest are quantified using selected Quality Attributes (QAts). The proposed process framework enables the identification of the quality attributes of interest as the desired characteristics to be balanced against performance measures. QAts are used to derive operational activities as well as design techniques for employment against an as-is SoS architecture. These activities and techniques are then mapped to metrics used to compare alternative architectures. These alternatives enable an SoS-based balance of design for performance and quality attribute optimization while employing a capability model to provide a comparison of available alternatives against overarching preferences. Approaches are then examined to analyze performance of the alternatives in meeting the enterprise capability objectives. These results are synthesized to enable an analysis of alternatives (AoA) to produce a "should-be" architecture vector based on a selected "to-be" architecture. A comparison of the vector trade space is discussed as a forward work in relation to the original enterprise level objectives for decision-making. The framework is illustrated using three case studies including a DoD Satellite Communications (SATCOM) case study; Position, Navigation, and Timing (PNT) case study; and a satellite operations "as-a-service" case study. For the SATCOM case study specifically, the question is considered of whether a certain QAt—resilience—can best be achieved through design alternatives of satellite disaggregation or diversification. The analysis shows that based on the metric mapping and design alternatives examined, diversification provides the greatest SATCOM capability improvement compared to the base architecture, while also enhancing resilience. These three separate cases studies show the framework can be extended to address multiple similar issues with system characteristics and SoS architecture questions for a wide range of enterprises.
Open Access
A combined classification and queuing system optimization approach for enhanced battery system maintainability
(Colorado State University. Libraries, 2022) Pirani, Badruddin, author; Cale, James, advisor; Simske, Steven, committee member; Miller, Erika, committee member; Keller, Josh, committee member
Battery systems are used as critical power sources in a wide variety of advanced platforms (e.g., ships, submersibles, aircraft). These platforms undergo unique and extreme mission profiles that necessitate high reliability and maintainability. Battery system failures and non-optimal maintenance strategies have a significant impact on total fleet lifecycle costs and operational capability. Previous research has applied various approaches to improve battery system reliability and maintainability. Machine learning methodologies have applied data-driven and physics-based approaches to model battery decay and predict battery state-of-health, estimation of battery state-of-charge, and prediction of future performance. Queuing theory has been used to optimize battery charging resources ensure service and minimize cost. However, these approaches do not focus on pre-acceptance reliability improvements or platform operational requirements. This research introduces a two-faceted approach for enhancing the overall maintainability of platforms with battery systems as critical components. The first facet is the implementation of an advanced inspection and classification methodology for automating the acceptance/rejection decision for batteries prior to entering service. The purpose of this "pre-screening" step is to increase the reliability of batteries in service prior to deployment. The second facet of the proposed approach is the optimization of several critical maintenance plan design attributes for battery systems. Together, the approach seeks to simultaneously enhance both aspects of maintainability (inherent reliability and cost-effectiveness) for battery systems, with the goal of decreasing total lifecycle cost and increasing operational availability.
Open Access
A graph-based, systems approach for detecting violent extremist radicalization trajectories and other latent behaviors
(Colorado State University. Libraries, 2017) Hung, Benjamin W. K., author; Jayasumana, Anura P., advisor; Chong, Edwin K. P., committee member; Ray, Indrajit, committee member; Sega, Ronald M., committee member
The number and lethality of violent extremist plots motivated by the Salafi-jihadist ideology have been growing for nearly the last decade in both the U.S and Western Europe. While detecting the radicalization of violent extremists is a key component in preventing future terrorist attacks, it remains a significant challenge to law enforcement due to the issues of both scale and dynamics. Recent terrorist attack successes highlight the real possibility of missed signals from, or continued radicalization by, individuals whom the authorities had formerly investigated and even interviewed. Additionally, beyond considering just the behavioral dynamics of a person of interest is the need for investigators to consider the behaviors and activities of social ties vis-à-vis the person of interest. We undertake a fundamentally systems approach in addressing these challenges by investigating the need and feasibility of a radicalization detection system, a risk assessment assistance technology for law enforcement and intelligence agencies. The proposed system first mines public data and government databases for individuals who exhibit risk indicators for extremist violence, and then enables law enforcement to monitor those individuals at the scope and scale that is lawful, and account for the dynamic indicative behaviors of the individuals and their associates rigorously and automatically. In this thesis, we first identify the operational deficiencies of current law enforcement and intelligence agency efforts, investigate the environmental conditions and stakeholders most salient to the development and operation of the proposed system, and address both programmatic and technical risks with several initial mitigating strategies. We codify this large effort into a radicalization detection system framework. The main thrust of this effort is the investigation of the technological opportunities for the identification of individuals matching a radicalization pattern of behaviors in the proposed radicalization detection system. We frame our technical approach as a unique dynamic graph pattern matching problem, and develop a technology called INSiGHT (Investigative Search for Graph Trajectories) to help identify individuals or small groups with conforming subgraphs to a radicalization query pattern, and follow the match trajectories over time. INSiGHT is aimed at assisting law enforcement and intelligence agencies in monitoring and screening for those individuals whose behaviors indicate a significant risk for violence, and allow for the better prioritization of limited investigative resources. We demonstrated the performance of INSiGHT on a variety of datasets, to include small synthetic radicalization-specific data sets, a real behavioral dataset of time-stamped radicalization indicators of recent U.S. violent extremists, and a large, real-world BlogCatalog dataset serving as a proxy for the type of intelligence or law enforcement data networks that could be utilized to track the radicalization of violent extremists. We also extended INSiGHT by developing a non-combinatorial neighbor matching technique to enable analysts to maintain visibility of potential collective threats and conspiracies and account for the role close social ties have in an individual's radicalization. This enhancement was validated on small, synthetic radicalization-specific datasets as well as the large BlogCatalog dataset with real social network connections and tagging behaviors for over 80K accounts. The results showed that our algorithm returned whole and partial subgraph matches that enabled analysts to gain and maintain visibility on neighbors' activities. Overall, INSiGHT led to consistent, informed, and reliable assessments about those who pose a significant risk for some latent behavior in a variety of settings. Based upon these results, we maintain that INSiGHT is a feasible and useful supporting technology with the potential to optimize law enforcement investigative efforts and ultimately enable the prevention of individuals from carrying out extremist violence. Although the prime motivation of this research is the detection of violent extremist radicalization, we found that INSiGHT is applicable in detecting latent behaviors in other domains such as on-line student assessment and consumer analytics. This utility was demonstrated through experiments with real data. For on-line student assessment, we tested INSiGHT on a MOOC dataset of students and time-stamped on-line course activities to predict those students who persisted in the course. For consumer analytics, we tested the performance on a real, large proprietary consumer activities dataset from a home improvement retailer. Lastly, motivated by the desire to validate INSiGHT as a screening technology when ground truth is known, we developed a synthetic data generator of large population, time-stamped, individual-level consumer activities data consistent with an a priori project set designation (latent behavior). This contribution also sets the stage for future work in developing an analogous synthetic data generator for radicalization indicators to serve as a testbed for INSiGHT and other data mining algorithms.
Open Access
A modeling toolkit for comparing AC vs. DC electrical distribution efficiency in buildings
(Colorado State University. Libraries, 2021) Othee, Avpreet, author; Cale, James, advisor; Young, Peter, committee member; Herber, Daniel, committee member; Jia, Gaofeng, committee member
An increasing proportion of electrical devices in residential and commercial buildings operate from direct current (DC) power sources. In addition, distributed power generation systems such as solar photovoltaic (PV) and energy storage natively produce DC power. However, traditional power distribution is based on an alternating current (AC) model. Performing the necessary conversions between AC and DC power to make DC devices compatible with AC distribution results in energy losses. For these reasons, DC distribution may offer energy efficiency advantages in comparison to AC distribution. However, reasonably fast computation and comparison of electrical efficiencies of AC-only, DC-only, and hybrid AC/DC distributions systems is challenging because DC devices are typically (nonlinear) power-electronic converters that produce harmonic content. While detailed time-domain modeling can be used to simulate these harmonics, it is not computationally efficient or practical for many building designers. To address this need, this research describes a toolkit for computation of harmonic spectra and energy efficiency in mixed AC and DC electrical distribution systems, using a Harmonic Power Flow (HPF) methodology. The toolkit includes a library of two-port linear and nonlinear device models which can be used to construct and simulate an electrical distribution system. This dissertation includes a description of the mathematical theory and framework underlying the toolkit, development and fitting of linear and nonlinear device models, software implementation in Modelica, verification of the toolkit with laboratory measurements, and discussion of ongoing and future work to employ the toolkit to a variety of building designs.
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
A systems engineering approach to community microgrid electrification and sustainable development in Papua New Guinea
(Colorado State University. Libraries, 2019) Anderson, Alexander A., author; Suryanarayanan, Siddharth, advisor; Cale, James, committee member; Zimmerle, Dan, committee member; Chen, Suren, committee member
Electrification of remote communities worldwide represents a key necessity for sustainable development and advancement of the 17 United Nations Sustainable Development Goals (SDGs). With over 1 billion people still lacking access to electricity, finding new methods to provide safe, clean, reliable, and affordable energy to off-grid communities represents an increasingly dynamic area of research. However, traditional approaches to power system design focused exclusively on traditional metrics of cost and reliability do not provide a sufficiently broad view of the profound impact of electrification. Installation of a single microgrid is a life-changing experience for thousands of people, including both residents who receive direct electricity service and numerous others who benefit from better education, new economic opportunities, incidental job creation, and other critical infrastructure systems enabled by electricity. Moreover, an electrification microgrid must directly satisfy community needs, be sensitive to local environmental constraints, mitigate possible risks, and plan for at least a decade of sustainable operations and maintenance. These considerations extend beyond the technical and optimization problems typically addressed in microgrid design. An enterprise system-of-systems framework for microgrid planning considering technical, economic, environmental, and social criteria is developed in response to the need for a comprehensive methodology for planning of community electrification projects. This framework spans the entire systems engineering discipline and incorporates elements from project management, risk management, enterprise architecture, numerical optimization, and multi-criteria decision-making, and sustainable development theory. To support the creation of the systems engineering framework, a comprehensive survey of multi-objective optimization formulations for planning and dispatch of islanded microgrids was conducted to form a baseline for further discussion. This survey identifies that all optimizations studies of islanded microgrids are based on formulations selecting a combination of 16 possible objective functions, 14 constraints, and 13 control variables. A sufficient group of decision-making elicitees are formed from the group of nearly 250 publications surveyed to create a comprehensive optimization framework based on technical, economic, environmental, and social attributes of islanded microgrids. This baseline enables the formulation of a flexible, computationally lightweight methodology for microgrid planning in consideration of multiple conflicting objectives using the simple multi-attribute ranking technique exploiting ranks (SMARTER). Simultaneously, the identified technical, economic, environmental, and social decision criteria form a network of functional, operational, and performance requirements in an enterprise system-of-systems structure that considers all stakeholders and actors in the development of community electrification microgrids. This framework considers community capacity building and sustainable development theory as a hierarchical structure, where each layer of the hierarchy is mapped both to a set of organizational, financial, and physical subsystems and to a corresponding subset of the 17 SDGs. The structure presents the opportunity not only to integrate classical project management and risk management tools, but also to create a new lifecycle for planning, funding, executing, and monitoring multi-phase community infrastructure projects. Throughout the research, a case study of the Madan Community in Jiwaka Province, Papua New Guinea is used to demonstrate the systems engineering concepts and tools developed by the research. The community is the center of multi-phase community capacity building project addressing critical needs of the deep rural community, including electricity, education, water, sanitation, healthcare, and economic opportunities. The researcher has been involved as a pro-bono consultant for the project since 2013 and helped raise over $1M USD in infrastructure materials, equipment, and consulting. The structure of the community-based organization and numerical optimization of a series of islanded microgrids are used to illustrate both the system-of-systems hierarchy and microgrid planning techniques based on both single-objective optimization using linear programming and the SMARTER methodology for consideration of multiple qualitative and quantitative decision criteria.
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
Advancing medium- and heavy-duty electric vehicle adoption models with novel natural language processing metrics
(Colorado State University. Libraries, 2024) Ouren, Fletcher, author; Bradley, Thomas H., advisor; Coburn, Timothy, committee member; Windom, Bret, committee member
The transportation sector must rapidly decarbonize to meet its emissions reduction targets. Medium- and heavy-duty decarbonization is lagging behind the light-duty industry due to technical and operational challenges and the choices made by medium- and heavy-duty fleet operators. Research investigating the procurement considerations of fleets has relied heavily on interviews and surveys, but many of these studies need higher participation rates and are difficult to generalize. To model fleet operators' decision-making priorities, this thesis applies a robust text analysis approach based on latent Dirichlet allocation and Bi-directional Encoder Representations of Transformers to two broad corpora of fleet adoption literature from academia and industry. Based on a newly developed metric, this thesis finds that the academic corpus emphasizes the importance of suitability, familiarity, norms, and brand image. These perception rankings are then passed to an agent-based model to determine how differences in perception affect adoption predictions. The results show a forecast of accelerated medium- and heavy-duty electric vehicle adoption when using the findings from the academic corpus versus the industry corpus.
Open Access
Aircraft survivability modeling, evaluation, and optimization for multi-UAV operational scenarios
(Colorado State University. Libraries, 2021) Lunsford, Ian, author; Bradley, Thomas, advisor; Borky, John, committee member; Shahroudi, Kamran, committee member; Arabi, Mazdak, committee member
The unmanned aerial vehicle (UAV) has become a prominent aircraft design throughout aerospace applications including commercial, civilian, and military. A UAV is preferred in some missions and applications due to its unique abilities compared to manned aircraft. This dissertation aims to define an improved understanding of the concepts and modeling of aircraft survivability, as applied to UAVs. Traditionally, survivability as a field has defined and considered survivability primarily in the context of manned aircraft, and single aircraft. With UAV's increasing importance in multi-UAV operational scenarios, it has become increasingly important to understand aircraft survivability for singles and groups of UAVs. This research effort has been structured into three research questions defining contributions in survivability modeling, validation, and UAV aircraft design. Research Question 1 seeks to demonstrate the feasibility of a parametric model of UAV survivability. The result is a UAV survivability model and simulation which illustrates key tradeoffs within UAV survivability. The effects on survivability on UAV design characteristics (speed, wing area, drag and lift coefficients) is quantified specific to the detailed lethal envelope simulation method. Research Question 2 aims to verify and validate the UAV survivability simulation, providing evidence of the predictive capability of the survivability simulation results. Evidence is presented for verification and validation through comparison to previous modeling efforts, through solicitation of expert opinion, and through parameter variability and sensitivity analysis. Lastly, Research Question 3 seeks to apply the simulation results to multi-UAV tactical evaluation and single aircraft design. The results illustrate the level of improvement that can be realized through UAV design including armoring (a 25% survivability improvement through 1000kg of armoring), speed increases (a 100 mph increase in cruise speed realizes a 14% decrease in killability), and other relevant design variables. Results also demonstrate that multi-UAV tactics can improve the survivability of UAVs in combat. Loyal wingman tactics are simulated to increase the survivability of a C-130J (equivalent UAV) from 19.8% to 40.0%. Other single UAV tactics such as fuel dumping, afterburners are evaluated under the same framework for their relative effectiveness. This dissertation answers the described research questions by presenting an aircraft survivability evaluation approach that relates survivability with modern UAV applications, emerging threats, multi-UAV tactics, and UAV design. Aircraft survivability encounters with modern UAV countermeasures are considered and simulated. UAV metrics of performance are modeled and simulated to describe aircraft design parameters sensitive to improving aircraft survivability. By evaluating aircraft survivability with a modern multi-UAV tactical perspective, this study seeks to provide the UAV designer with more complete vision of survivability-derived design criteria.
Open Access
Algorithm parallelism for improved extractive summarization
(Colorado State University. Libraries, 2023-08-22) Villanueva, Arturo N., Jr., author; Simske, Steven J., author; ACM, publisher
While much work on abstractive summarization has been conducted in recent years, including state-of-the-art summarizations from GPT-4, extractive summarization's lossless nature continues to provide advantages, preserving the style and often key phrases of the original text as meant by the author. Libraries for extractive summarization abound, with a wide range of efficacy. Some do not perform much better or perform even worse than random sampling of sentences extracted from the original text. This study breathes new life to using classical algorithms by proposing parallelism through an implementation of a second order meta-algorithm in the form of the Tessellation and Recombination with Expert Decisioner (T&R) pattern, taking advantage of the abundance of already-existing algorithms and dissociating their individual performance from the implementer's biases. Resulting summaries obtained using T&R are better than any of the component algorithms.
Open Access
An analysis of the costs and performance of vehicles fueled by alternative energy carriers
(Colorado State University. Libraries, 2024) Lynch, Alexander, author; Bradley, Thomas, advisor; Coburn, Tim, committee member; Olsen, Daniel B., committee member
The transportation sector stands at the crossroads of new challenges and opportunities, driven by the pressing need to mitigate environmental impacts, enhance energy efficiency, and ensure sustainable mobility solutions. This transition will occur across diverse transportation modes, each with distinct characteristics and challenges. From light duty vehicles embracing electrification to maritime transport adopting alternative fuel engines, the push for low-carbon technology is reshaping the landscape of transportation. In this context, it is necessary to conduct a review and assessment of technologies, environmental benefits, and costs of alternative fuels and powertrains across a broad set of applications in the transportation sector. This study seeks to perform this assessment by combining bottom-up cost analysis, environmental assessments, and reviews of the literature to examine the techno-economic aspects of various fuel and powertrain options in the transportation sector. This approach involves detailed evaluations of individual components and systems to model the cost structures and efficiency profiles of vehicles. The results illustrated in this thesis will be embedded into adoption models to enable governments, utilities, private fleets, and other shareholders to make informed transportation planning decisions.
Open Access
An enterprise system engineering analysis of KC-46A maintenance program decision-making
(Colorado State University. Libraries, 2023) Blond, Kyle E., author; Bradley, Thomas, advisor; Ender, Tommer, committee member; Conrad, Steven, committee member; Herber, Daniel, committee member; Ozbek, Mehmet, committee member
The KC-46A Pegasus is a United States Air Force (USAF) tanker, transport, and medical evacuation commercial derivative aircraft based on the Boeing 767. It is a top acquisition priority to modernize the USAF's refueling capabilities and is governed by a lifecycle sustainment strategy directed by USAF commercial variant policies aligned to Federal Aviation Administration (FAA) policy. While this strategy provides robust mechanisms to manage the KC-46A's performance during its operations and support phase, opportunity exists for the KC-46A sustainment enterprise to better achieve reliability, availability, maintainability, and cost (RAM C) objectives through enhancing KC-46A maintenance program decision making in the context of USAF and FAA policies. This research characterizes the KC-46A maintenance program as an industrial enterprise system governing the maintenance, repair, overhaul, and modification of KC-46A aircraft. Upon this basis, enterprise systems engineering (ESE) characterizes the KC-46A maintenance program and identifies decision making improvement opportunities in its management. Canonical ESE viewpoints are tailored to abstract the organizations, processes, and information composing KC-46A maintenance program decision making and model how decision support methods can better achieve KC-46A sustainment enterprise objectives. A decision making framework then evaluates the RAM C performance of KC-46A maintenance tasks as part of the KC-46A Continued Analysis and Surveillance System (CASS) program. The framework's heuristics classify the compliance, effectiveness, and optimality of a maintenance task to prescribe KC-46A CASS responses. A rule based expert system applies this framework and serves as the knowledge engine for the KC-46A CASS decision support system referred to as the "Pegasus Fleet Management Tool." A focus group of KC-46A sustainment experts evaluated the framework and produced consensus that it advances the state of the art in KC-46A maintenance program decision making. A business case analysis roadmaps the programmatic and technical activities required to implement the framework in PFMT and improve KC-46A sustainment.
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
Appendix A
(Colorado State University. Libraries, 2025-05) Shaw, Sarah G., author
Appendix A is part of the paper entitled "Understanding Organizational Factors in the Aerospace Industry's Transition to Model-Based Systems Engineering," submitted for publication to the INCOSE "Journal of Systems Engineering," May, 2025.
Open Access
Application of systems engineering principles in the analysis, modeling, and development of a DoD data processing system
(Colorado State University. Libraries, 2023) Fenton, Kevin P., author; Simske, Steven J., advisor; Bradley, Thomas, committee member; Carlson, Ken, committee member; Atadero, Rebecca, committee member
In support of over 1000 military installations worldwide, the Department of Defense (DoD) has procured contracts with thousands of vendors that supply the military with hazardous materials constituting billions of dollars of defense expenses in support of facility and asset maintenance. These materials are used for a variety of purposes ranging from weapon system maintenance to industrial and facility operations. In order to comply with environmental, health, and safety (EHS) regulations, the vendors are contractually obligated to provide Safety Data Sheets (SDSs) listing EHS concerns compliant with the requirements set forth by the United Nations Globally Harmonized System of Classification and Labeling of Chemicals (GHS). Each year chemical vendors provide over 100 thousand SDSs in a PDF or hard copy format. These SDSs are then entered manually by data stewards into the DoD centralized SDS repository – the Hazardous Material Management Information System (HMIRS). In addition, the majority of these SDS are also loaded separately by separate data stewards into downstream environmental compliance systems that support specific military branches. The association between the vendor-provided SDSs and the materials themselves was then lost until the material reaches an installation at which point personnel must select the SDS associated to the hazardous material within the service-specific hazardous material tracking system. This research applied systems engineering principles in the analysis, modeling, and development of a DoD data processing system that could be used to increase efficiency, reduce costs, and provide an automated solution not only to data entry reduction but in transitioning and modernizing the hazard communication and data transfer towards a standardized approach. Research for the processing system covered a spectrum of modern analytics and data extraction techniques including optical character recognition, artificial neural networks, and meta-algorithmic processes. Additionally, the research covered potential integration into existing DoD framework and optimization to solve many long-standing chemical management problems. While the long-term focus was for chemical manufacturers to provide SDS data in a standardized machine-encoded format, this system is designed to act as a transitionary tool to reduce manual data entry and costs of over $3 million each year while also enhancing system features to address other major obstacles in the hazard communication process. Complexities involved with the data processing of SDSs included multi-lingual translation needs, image and text recognition, periodic use of tables, and while SDSs are structured with 16 distinct sections – a general lack of standardization on how these sections were formatted. These complexities have been addressed using a patent-pending meta-algorithmic approach to produce higher data extraction yields than what an artificial neural network can produce alone while also providing SDS-specific data validation and calculation of SDS-derived data points. As the research progressed, this system functionality was communicated throughout the DoD and became part of a larger conceptual digital hazard communication transformation effort currently underway by the Office of the Secretary of Defense and the Defense Logistics Agency. This research led to five publications, a pending patent, an award for $280,000 for prototype development, and a project for the development of this system to be used as one of the potential systems in a larger DoD effort for full chemical disclosure and proactive management of not only hazardous chemicals but potentially all DoD-procured products.
Open Access
Application of systems engineering to complex systems and system of systems
(Colorado State University. Libraries, 2017) Sturdivant, Rick L., author; Chong, Edwin K. P., advisor; Sega, Ronald M., committee member; Jayasumana, Anura P., committee member; Atadero, Rebecca, committee member
This dissertation is an investigation of system of systems (SoS). It begins with an analysis to define, with some rigor, the similarities and differences between complex systems and SoS. With this foundation, the baseline concept is development for several different types of systems and they are used as a practical approach to compare and contrast complex systems versus SoS. The method is to use a progression from simple to more complex systems. Specifically, a pico hydro electric power generation system, a hybrid renewable electric power generation system, a LEO satellites system, and Molniya orbit satellite system are investigated. In each of these examples, systems engineering methods are applied for the development of a baseline solution. While these examples are complex, they do not rise to the level of a SoS. In contrast, a multi-spectral drone detection system for protection of airports is investigated and a baseline concept for it is generated. The baseline is shown to meet the minimum requirements to be considered a SoS. The system combines multiple sensor types to distinguish drones as targets. The characteristics of the drone detection system which make it a SoS are discussed. Since emergence is considered by some to be a characteristic of a SoS, it is investigated. A solution to the problem of determining if system properties are emergent is presented and necessary and sufficient conditions for emergence are developed. Finally, this work concludes with a summary and suggestions for additional work.
Open Access
Applying model-based systems engineering in search of quality by design
(Colorado State University. Libraries, 2022) Miller, Andrew R., author; Herber, Daniel R., advisor; Bradley, Thomas, committee member; Miller, Erika, committee member; Simske, Steve, committee member; Yalin, Azer P., committee member
Model-Based System Engineering (MBSE) and Model-Based Engineering (MBE) techniques have been successfully introduced into the design process of many different types of systems. The application of these techniques can be reflected in the modeling of requirements, functions, behavior, and many other aspects. The modeled design provides a digital representation of a system and the supporting development data architecture and functional requirements associated with that architecture through modeling system aspects. Various levels of the system and the corresponding data architecture fidelity can be represented within MBSE environment tools. Typically, the level of fidelity is driven by crucial systems engineering constraints such as cost, schedule, performance, and quality. Systems engineering uses many methods to develop system and data architecture to provide a representative system that meets costs within schedule with sufficient quality while maintaining the customer performance needs. The most complex and elusive constraints on systems engineering are defining system requirements focusing on quality, given a certain set of system level requirements, which is the likelihood that those requirements will be correctly and accurately found in the final system design. The focus of this research will investigate specifically the Department of Defense Architecture Framework (DoDAF) in use today to establish and then assess the relationship between the system, data architecture, and requirements in terms of Quality By Design (QbD). QbD was first coined in 1992, Quality by Design: The New Steps for Planning Quality into Goods and Services [1]. This research investigates and proposes a means to: contextualize high-level quality terms within the MBSE functional area, provide an outline for a conceptual but functional quality framework as it pertains to the MBSE DoDAF, provides tailored quality metrics with improved definitions, and then tests this improved quality framework by assessing two corresponding case studies analysis evaluations within the MBSE functional area to interrogate model architectures and assess quality of system design. Developed in the early 2000s, the Department of Defense Architecture Framework (DoDAF) is still in use today, and its system description methodologies continue to impact subsequent system description approaches [2]. Two case studies were analyzed to show proposed QbD evaluation to analyze DoDAF CONOP architecture quality. The first case study addresses the analysis of DoDAF CONOP of the National Aeronautics and Space Administration (NASA) Joint Polar Satellite System (JPSS) ground system for National Oceanic and Atmospheric Administration (NOAA) satellite system with particular focus on the Stored Mission Data (SMD) mission thread. The second case study addresses the analysis of DoDAF CONOP of the Search and Rescue (SAR) navel rescue operation network System of Systems (SoS) with particular focus on the Command and Control signaling mission thread. The case studies help to demonstrate a new DoDAF Quality Conceptual Framework (DQCF) as a means to investigate quality of DoDAF architecture in depth to include the application of DoDAF standard, the UML/SysML standards, requirement architecture instantiation, as well as modularity to understand architecture reusability and complexity. By providing a renewed focus on a quality-based systems engineering process when applying the DoDAF, improved trust in the system and data architecture of the completed models can be achieved. The results of the case study analyses reveal how a quality-focused systems engineering process can be used during development to provide a product design that better meets the customer's intent and ultimately provides the potential for the best quality product.
Open Access
Applying model-based systems engineering to architecture optimization and selection during system acquisition
(Colorado State University. Libraries, 2018) LaSorda, Michael, author; Sega, Ronald M., advisor; Borky, Mike, advisor; Bradley, Tom, committee member; Quinn, Jason, committee member
The architecture selection process early in a major system acquisition is a critical step in determining the overall affordability and technical performance success of a program. There are recognized deficiencies that frequently occur in this step such as poor transparency into the final selection decision and excessive focus on lowest cost, which is not necessarily the best value for all of the stakeholders. This research investigates improvements to the architecture selection process by integrating Model-Based Systems Engineering (MBSE) techniques, enforcing rigorous, quantitative evaluation metrics with a corresponding understanding of uncertainties, and stakeholder feedback in order to generate an architecture that is more optimized and trusted to provide better value for the stakeholders. Three case studies were analyzed to demonstrate this proposed process. The first focused on a satellite communications System of Systems (SoS) acquisition to demonstrate the overall feasibility and applicability of the process. The second investigated an electro-optical remote sensing satellite system to compare this proposed process to a current architecture selection process typified by the United States Department of Defense (U.S. DoD) Analysis of Alternatives (AoA). The third case study analyzed the evaluation of a service-oriented architecture (SOA) providing satellite command and control with cyber security protections in order to demonstrate rigorous accounting of uncertainty through the architecture evaluation and selection. These case studies serve to define and demonstrate a new, more transparent and trusted architecture selection process that consistently provides better value for the stakeholders of a major system acquisition. While the examples in this research focused on U.S. DoD and other major acquisitions, the methodology developed is broadly applicable to other domains where this is a need for optimization of enterprise architectures as the basis for effective system acquisition. The results from the three case studies showed the new process outperformed the current methodology for conducting architecture evaluations in nearly all criteria considered and in particular selects architectures of better value, provides greater visibility into the actual decision making, and improves trust in the decision through a robust understanding of uncertainty. The primary contribution of this research then is improved information support to an architecture selection in the early phases of a system acquisition program. The proposed methodology presents a decision authority with an integrated assessment of each alternative, traceable to the concerns of the system's stakeholders, and thus enables a more informed and objective selection of the preferred alternative. It is recommended that the methodology proposed in this work is considered for future architecture evaluations.
Open Access
Artificial intelligence powered personalized agriculture
(Colorado State University. Libraries, 2023) Tetala, Satya Surya Dattatreya Reddy, author; Simske, Steven, advisor; Conrad, Steve, committee member; Gaines, Todd, committee member; Nalam, Vamsi, committee member
The integration of Artificial Intelligence (AI) in agriculture has shown the potential to improve crop selection and enhance sustainability practices. In this study, we aim to investigate the benefits and feasibility of using AI-powered personalized recommendations for crop selection and sustainability practices in the context of agroecology. We propose to lay the foundation for an agricultural recommendation engine that considers several parameters that influence yield and presents the best crop(s) to sow based on the model's output. We aim to examine this recommendation engine's impact on agriculture's sustainability and to evaluate its effectiveness and accuracy. Our ultimate goal is to provide a comprehensive understanding of the potential benefits and challenges of using AI-powered recommendations in agriculture and to lay the foundation for the development of a practical, effective, and user-friendly recommendation engine that can help farmers make informed decisions about their crops and improve the long-term sustainability of agriculture.
Open Access
Autonomous trucks as a scalable system of systems: development, constituent systems communication protocols and cybersecurity
(Colorado State University. Libraries, 2024) Elhadeedy, Ahmed, author; Daily, Jeremy, advisor; Chong, Edwin, committee member; Papadopoulos, Christos, committee member; Luo, Jie, committee member
Driverless vehicles are complex to develop due to the number of systems required for safe and secure autonomous operation. Autonomous vehicles embody the definition of a system of systems as they incorporate several systems to enable functions like perception, decision-making, vehicle controls, and external communication. Constituent systems are often developed by different vendors globally which introduces challenges during the development process. Additionally, as the fleet of autonomous vehicles scales, optimization of onboard and off-board communication between the constituent systems becomes critical. Autonomous truck and trailer configurations face challenges when operating in reverse due to the lack of sensing on the trailer. It is anticipated that sensor packages will be installed on existing trailers to extend autonomous operations while operating in reverse in uncontrolled environments, like a customer's loading dock. Power Line Communication (PLC) between the trailer and the tractor cannot support high bandwidth and low latency communication. Legacy communications use powerline carrier communications at 9600 baud, so upfitting existing trailers for autonomous operations will require adopting technologies like Ethernet or a wireless harness between the truck and the trailer. This would require additional security measures and architecture, especially when pairing a tractor with a trailer. We proposed tailoring the system of systems Model for autonomous vehicles. The model serves as the governing framework for the development of constituent systems. It's essential for the SoS model to accommodate various development approaches that are used for hardware, and software such as Agile, or Vee models. Additionally, a queuing model for certificates authentication compares the named certificate approach with the traditional approach. The model shows the potential benefits of named certificates when the autonomous vehicles are scaled. We also proposed using named J1939 signals to reduce complexities and integration efforts when multiple on-board or off-board systems request vehicle signals. We discuss the current challenges and threats on autonomous truck-trailer communication when Ethernet or a wireless harness is used, and the impact on the Electronic Control Unit (ECU) lifecycle. In addition to using Named Data Networking (NDN) to secure in-vehicle and cloud communication. Named Data Networking can reduce the complexity of the security of the in-vehicle communication networks where it provides a networking solution with security by design.