Master of Science, The Ohio State University, 2012, Industrial and Systems Engineering

This paper explores business continuity and resilience engineering by examining exercises one socio-technical organization used to assess the consequences of breakdowns in with vital digital infrastructure. The first exercise, which is implementation of disaster recovery plans, was conducted with practitioners and technologists at the sharp end of the organizational hierarchy. The second exercise, a risk-based scenario to test large-scale business continuity (aggregate execution of multiple disaster recovery plans), was conducted with administrators and business leaders at the blunt end of the organizational hierarchy. These two exercises together compose the organization's processes to build resilience. The results contrast how an organization implements disaster recovery tests at smaller, more manageable scales with the use of risk-based simulations at larger scales to prepare to respond to challenge events. The results also demonstrate how a company can build resilience by preparing to manage disruptions to minimize downtime and quickly restore normal business operations.

Committee: David Woods (Advisor); Phil Smith (Committee Member) Subjects: Engineering; Industrial Engineering; Systems Design

Doctor of Philosophy, The Ohio State University, 2020, Industrial and Systems Engineering

Responding to anomalies in the critical digital services domain involves coordination across a distributed system of automated subsystems and multiple human roles (Allspaw, 2015; Grayson, 2019). Exploring the costs of this joint activity is an underexamined area of study (Woods, 2017) but has important implications for managing complex systems across distributed teams and for tool design and use. It is understood that anomaly recognition is a shared activity between the users of the service, the automated monitoring systems, and the practitioners responsible for developing and operating the service (Allspaw, 2015). In addition, multiple, diverse perspectives are needed for their different views of the system and its behavior and their ability to recognize unexpected and abnormal conditions. While the collaborative interplay and synchronization of roles is critical in anomaly response (Patterson et al, 1999; Patterson & Woods, 2001), the cognitive costs for practitioners (Klein et al, 2005; Klinger & Klein, 1999; Klein, 2006) can be substantial. The choreography of this joint activity is shown to be a subtle and highly integrated into the technical efforts of dynamic fault management. This work uses process tracing to take a detailed look at a corpus of five cases involving software engineers coping with unexpected service outages of varying difficulty. In doing so, it is noted that the practices of incident management work very differently than domain models suggest and the tooling designed to aid coordination incurs a cognitive cost for practitioners. Adding to the literature on coordination in ambiguous, time pressured and non co-located groups, this study shows that adaptive choreography enables practitioners to cope with dynamic events – and dynamic coordination demands. These demands can also be a function of the coordination strategies of others – in particular when they shift costs of coordination across time and organizational boundaries.

Committee: David Woods (Advisor); Michael Rayo (Committee Member); Philip Smith (Committee Member) Subjects: Computer Science; Engineering

Master of Science, The Ohio State University, 2019, Mechanical Engineering

The Ohio State University has participated in Advanced Vehicle Technology Competitions (AVTCs) for over 21 years. These competitions challenge universities throughout North American to reengineer a vehicle with technologies advancing the automotive market. This work explores the use of systems engineering practices during the eleventh iteration of the AVTC program, the EcoCAR 3 competition. The document presents the systems engineering process and two case studies implementing the process. The systems engineering process presented is a simplification of the “Vee” and “Agile” systems engineering processes applicable to a high-cost, long-term, prototype program. The process is broken into five stages: Concept Creation and Refinement, Architecture and Metric Creation, Development, Verification, and Assessment and Validation. The two case studies present uses of the process at a low-level applied to a software algorithm and at a high-level applied to an entire project. The first case study reviews the development of a diagnostic algorithm for the automated manual transmission used in the EcoCAR 3 competition vehicle. The team automated a manual transmission and needed an algorithm to detect and isolate failures to components of the transmission system. The concept and requirements for this algorithm are detailed in Chapter 1 before continuing to discussion of development and testing. Testing of the algorithm utilizes a model-based environment. The second case study reviews the construction and execution of a behavioral study project evaluating driver performance during a vehicle to driver transition of an SAE Level 3 partially automated vehicle. Research was conducted in a model-based environment, simulating an autonomous vehicle by utilizing a driving simulator. The project requirements are derived from the applicable parent requirements, implemented, and tested.

Committee: Shawn Midlam-Mohler Ph.D. (Advisor); Giorgio Rizzoni Ph.D. (Advisor); Lisa Fiorentini Ph.D. (Committee Member); Sandra Metzler Ph.D. (Committee Member) Subjects: Electrical Engineering; Engineering; Mechanical Engineering

Master of Science, The Ohio State University, 2023, Industrial and Systems Engineering

As automation and technology has grown, machines are no longer tools and new human-machine architectures have emerged that move beyond the definition of a traditional team and are better defined through joint activity. Current design philosophies, such as Human-Centered Design (HCD), continue to look at systems as collections of components, which is insufficient to understand the interdependencies and complexities that are necessary to support these new work architectures. Joint activity design (JAD) has been introduced to fill this gap and explicitly design for joint activity. However, adoption of JAD and similar techniques has been slow, and it is unclear what factors are contributing to this lack of proliferation. In this study, we aimed to determine the challenges for implementation of JAD artifacts and what may have facilitated their use. By analyzing 5 case studies of design projects that span multiple organizations, we found that JAD artifacts were rarely used as intended due to a high cost and a high uncertainty on how to implement them. Whereas these artifacts were often used within smaller subunits, they were rarely used across the larger design function. Across the larger design function, the support was not sufficient to maintain common ground, which exacerbated many of the challenges that resulted in sacrificing the final design's ability to support joint activity. In the future it would be valuable to get a larger selection of cases from other organizations and more evidence needs to be collected on the value of JAD methods over HCD methods.

Committee: David Woods (Committee Member); Michael Rayo (Advisor) Subjects: Design; Engineering; Industrial Engineering; Systems Design

Doctor of Philosophy, The Ohio State University, 2023, Industrial and Systems Engineering

As the push for increased artificial intelligence (AI) and machine learning (ML) in safety-critical domains continues to grow stronger, calls for responsible AI have emphasized the need to mitigate the potential for automation to contribute to catastrophic accidents and inequitable outcomes. These conversations have illuminated a growing consensus that explainability is not just helpful but necessary for any automation deployed in high-stakes systems. However, conversations around responsible AI and explainable AI (XAI) imply a number of assumptions about the resultant joint human-machine cognitive system: (1) improved automation will generally perform better than people and, in the rare circumstances it does not, (2) people will be able to detect and correct automation mistakes, if they are sufficiently motivated and thorough; therefore, (3) focusing on improving the performance of automation alone will improve the performance of the joint system. This research explores these assumptions about modern AI-infused automation. 267 nursing students used various configurations of a patient data display to anticipate patient decompensation events (i.e., a rapid collapse of a patient's health) five minutes into the future. Components of the display included a complex patient data visualization, a logistic regression algorithm trained to predict decompensation, and custom annotations meant to provide a visual explanation of the algorithm prediction. Nursing students interacted with four different combinations of these components which emulated four technology archetypes: (A) representation aids, (B) black-box algorithms, (C) observable algorithms, and (D) salience algorithms. Students completed a randomized sequence of 10 patient cases (5 urgent, 5 non-urgent) and on each case reported their concern for the patient (on a scale of 0-10), an explanation of their concern, and what they thought the algorithm was “concerned” about. With only the base representation aid (n (open full item for complete abstract)

Committee: Michael Rayo (Advisor); David Woods (Committee Member); Samantha Krening (Committee Member); Mark Moritz (Committee Member) Subjects: Artificial Intelligence; Cognitive Psychology; Industrial Engineering; Systems Design

Master of Science, The Ohio State University, 2022, Industrial and Systems Engineering

Decades of cognitive systems engineering research has revealed that implementing human-machine teams into complex environments can consequently result in challenges that negatively impact human-machine teams. Such challenges and conflicts amongst team members can readily be observed in human-machine teams where agents are assigned heterogeneous tasks because the agents' individual goals may have a tendency to conflict and compete with one another in their shared environment. This conflict may also be magnified if the agents of our heterogeneously tasked human-machine team do not share a common goal and are not equipped with the resources to manage their differences. In our study, we set out to determine how the performance of our heterogeneously tasked agents in our simulated human-machine team was impacted in our full-motion video and intelligence analysis. By using joint-performance activity graphs, various statistical analyses, constant comparative analysis, and human-machine teaming heuristic analysis, we were able to determine that the performance of our human-machine team was not significantly different from the performance of our participants who worked alone. This led us to the conclusion that the machine agent insufficiently aided their human agent's decision making during the full motion video analysis and the design of the machine failed to adhere to known Human-Machine Teaming heuristics. Lastly, this holistic analysis revealed that the machine agent acted as if it did not have any knowledge of the ultimate goal of their human agent, and due to its limited capabilities, the machine was unable to contribute information in relation to the overarching goal. Even though the architecture of the human-machine team in this study failed to adhere to various human-machine teaming heuristics, failing to adhere to and implement the team so that both the agents' individual tasks meaningfully contributed the shared goal was determined to be the most criti (open full item for complete abstract)

Committee: Michael Rayo (Advisor); Samantha Krening (Committee Member); Michael Rayo (Committee Member) Subjects: Design; Engineering; Industrial Engineering; Systems Science

MS, University of Cincinnati, 2022, Engineering and Applied Science: Environmental Engineering

Managing modern microgrids for the 21st century will require looking beyond the intelligent control of complex microgrids and their coupling with centralized power grids. Incorporating multiple-objective optimization for sustainable decision-making purposes is a step toward providing end-users with reliable, cost-effective heating and power with minimum environmental impacts. A simulation model, based on nonlinear mathematical programming principles, is proposed to optimize a microgrid using economic and environmental impact objectives, using the University of Cincinnati's (UC) combined heat and power (CHP) microgrid as a case study. The economic objective focuses on minimizing operation costs, as subset of variable costs, that include electricity import and natural gas fuel costs to daily operate the microgrid's CHP process. The environmental impact objective concentrates on minimizing greenhouse gas (GHG) emissions (i.e., carbon dioxide, methane, and nitrous oxide) from the supply chain and microgrid system boundary on a cradle-to-grave lifecycle basis using a life cycle assessment (LCA) methodology. Three different analysis applications are investigated to optimize the simulation model of UC's CHP microgrid using the stated single and multiple objectives in MATLAB, harnessing MATLAB's Optimization Toolbox solvers to perform prescriptive analytics. The results of each analysis can assist operation managers with identifying economically or environmentally optimal operating conditions, commodity pricing thresholds and operational trends that can inform the development of optimal operation strategies, as well as Pareto optimal trade-off curves to reduce GHG emissions for the case study process. The fundamental conclusion taken from the multiple-objective optimization analysis is that managers can make sizable reductions (15-30% in the investigated examples) in greenhouse gas emissions while incurring smaller economic penalties (5-15% in the investigated examples) usi (open full item for complete abstract)

Committee: Margaret Kupferle Ph.D. (Committee Member); Stephen Thiel Ph.D. (Committee Member); Patrick Ray Ph.D. (Committee Member); Drew McAvoy Ph.D. (Committee Member) Subjects: Environmental Engineering

Master of Science (MS), Wright State University, 2021, Human Factors and Industrial/Organizational Psychology MS

Four interfaces were developed to factorially apply two principles of ecological interface design (EID; direct perception and direct manipulation) to a flexible manufacturing system (FMS). The theoretical foundation and concepts employed during their development, with findings related to more significant issues regarding interface design for complex socio-technical systems, are discussed. Key aspects of cognitive systems engineering (CSE) and EID are also discussed. An FMS synthetic task environment was developed, and an experiment was conducted to evaluate real-time decision support during supervisory operations. Participants used all four interfaces to supervise and maintain daily part production at systematically varied levels of difficulty across sessions. Significant results provide evidence that the incorporation of direct perception and direct manipulation in interface design produced an additive effect, allowing for greater support for the supervisory agents.

Committee: Kevin B. Bennett Ph.D. (Advisor); Scott Watamaniuk Ph.D. (Committee Member); John Flach Ph.D. (Committee Member) Subjects: Experimental Psychology; Psychology; Systems Design

Master of Science (M.S.), University of Dayton, 2018, Management Science

The overall goal of this project is to adopt and build on three of the Saudi vision 2030 “thriving economy” theme third-level objectives that include (1) Localize military industry, (2) Nurture and support the innovation and entrepreneurship culture, and (3) Grow Small and medium-sized enterprise (SME) contribution to the economy. One of the very important initiatives of the adopted thriving economy theme to the area of concentration is planning to grow the economy by manufacturing half of the defense needs within the Kingdom, with the intention to offset the economy, keep more resources in Saudi Arabia and to create more job opportunities for its citizens. The main research question explores: How can a conceptual model provide value to Saudi military spare parts manufacturing and contribute to the Saudi 2030 Vision? The research process includes creating a conceptual model intended to assist with the fit of low-complexity defense spare parts manufacturing industries, utilizing technology transfer and a business incubator. The model will include (1) adopt a Systems Approach to better understand the nature and the scope of the problem statement (2) Develop a conceptual model that can provide value to the military manufacturing industry. The value provided can be measured by two dimensions: valuable products and growth to the military manufacturing industry. (3) Fit the Saudi military spare parts manufacturing to the developed conceptual model along with the alignment of the adopted Saudi Vision 2030 at the Saudi manufacturing industry level. (4) Validate the conceptual model framework. This conceptual framework will address the need to develop a flexible model that contributes to the current and future challenges as there is a lack of an adequate model to guide Saudi government on how to develop the SMEs defense manufacturing industries in order to become aligned with their country's vision 2030. In summary, the developed model called “Seven Systems for Bu (open full item for complete abstract)

Committee: Sandy Furterer Ph.D. (Advisor); Kellie Schneider Ph.D. (Committee Member); Zalewski Daniel Ph.D. (Committee Member) Subjects: Management; Systematic; Systems Design; Systems Science; Technology

Doctor of Philosophy, The Ohio State University, 1961, Graduate School

Committee: Not Provided (Other) Subjects: Engineering

Doctor of Philosophy, Case Western Reserve University, 2016, Civil Engineering

This work studies the seismic analysis and design of systems with multiple support motions. Examples of such structures are nuclear safety - related piping, bridges with widely spaced piers, pipelines and tunnels. Dynamic analysis of such systems is more elaborate than analysis of other general structures such as buildings, because it must consider the relationships among various support motions, or explicitly, the correlations between them. Herein, a method that models such correlations is presented. A time domain space state Markov vector approach is used to define a target covariance matrix of a vector random process and this matrix is used as a target for an AR simulation of correlated acceleration time histories. To provide a facility for experimental testing of multiply supported structures, a four-table seismic simulation system was designed, fabricated and tuned to have the ability of executing correlated multiply supported excitations. Features, performance curves, control and implementation details of the system are given as a manual for operation and testing. A review of existing problems and methods for analysis of nuclear piping systems is provided. A set of experiments is performed to evaluate responses of “stiff” and more “flexible” piping designs to correlated, response-spectrum-compatible support motions. These preliminary tests show effects of correlation on piping responses. Guidance is given on more comprehensive experiments needed to evaluate piping designs that are more “flexible”. The system of shaking tables proved to be fully functional. Linearized analytical and numerical models of “stiff” and “flexible” piping are defined. Analytical and numerical eigenvalues are compared. The numerical models are used for predicting responses and comparing these responses to experimental results. Necessary features of a nonlinear model that may be capable of predicting geometrically non-linear behavior of a hanger pipe support (self-weight suppor (open full item for complete abstract)

Committee: Dario Gasparini PhD (Advisor); Brian Metrovich PhD (Committee Member); Wojbor Woyczynski PhD (Committee Member); Michael Pollino PhD (Committee Member) Subjects: Civil Engineering; Mechanical Engineering; Systems Design

Doctor of Philosophy, The Ohio State University, 2012, Industrial and Systems Engineering

Interest in the application of Unmanned Aerial Vehicles (UAV) has emerged as a result of the increased capacity to connect distant operators. No longer do pilots need to be collocated with the air vehicles they control. High-bandwidth communication channels afford the opportunity to remotely control these platforms. The anticipated advantages of this approach are obvious. First, removing the crew from the platform represents significant weight reductions that can be exploited in the form of greater range or bigger payloads. The elimination of the cockpit affords greater flexibility in design of the airframe that can enhance the low observability characteristics thereby increasing survivability. With no need to worry about the g-tolerance of a pilot, restrictions on maneuverability are relaxed. Finally and most notably is the removal of human from harm's way. UAVs can enter hostile airspace without the risk of friendly loss of human life. However, these potential benefits come with a heavy cost. Given the foreseen high potential benefits of UAVs, the challenge for developers has shifted from one of demonstrating their value on the battlefield to one of increasing the efficiency of their use. Given the increased demand for such assets, the using community must find ways to reverse the manning ratio for their use. Current concepts of operation require multiple operators for every unmanned system, a ratio will be difficult to sustain if the projected increase in use is to be believed. If this expanded use of UAVs is to be realized, a more desirable manning concept must be identified. However, coordinating control of multiple UAVs at a distance has proven difficult. Several human related issues remain. Research is ongoing addressing many of the traditional human integration problems such as workload, situation awareness, human-computer interface, human interaction with automation, etc. The current research investigates potential sources of adaptive capacity that could e (open full item for complete abstract)

Committee: David Woods PhD (Committee Chair); Philip Smith PhD (Committee Member); Emily Patterson PhD (Committee Member); Umit Catalyurek PhD (Committee Member) Subjects: Engineering

Doctor of Philosophy, The Ohio State University, 2008, Electrical and Computer Engineering

Hypersonic air-breathing vehicles are a promising and cost-efficient technology for launching low-earth-orbit satellites and providing rapid global-response capabilities. Modeling and control of such vehicles has been an active subject of research in recent years. A first-principle, physics-based model (FPM) of the vehicle's longitudinal dynamics has been developed at the Air Force Research Laboratory, and made available to the academic community for control systems design. This model, while suitable for simulation, is intractable for model-based control, thus requiring further control-oriented modeling. A typical control objective is to track a velocity and altitude reference while maintaining physical feasibility of the control input and the state. Two control strategies are presented in this work. The first is a linear time invariant (LTI) design based on a novel formulation of a robust servo-mechanism using singular perturbation arguments. This approach does not rely on state reconstruction but does require an analysis of a family of linearized models from the FPM. The second design relies on reduced-complexity modeling of the FPM. Intractable expressions of the forces and moment in the FPM are replaced with a curve-fit model (CFM). The CFM is expressed as a linear parameter varying (LPV) system, where the scheduling variables depend on the system output. A novel LPV regulator design methodology is developed, which explicitly addresses the case of over-actuated models (i.e., models with more inputs than performance outputs). This is a non-trivial extension of the analysis and design of output regulators for LTI systems. The LPV regulator separates the control problem into a steady-state controller and a stabilizing controller. The steady-state controller produces a non-unique approximate steady-state using receding horizon constrained optimization, while the stabilizer renders the steady-state attractive. The steady-state controller represents an approach to add (open full item for complete abstract)

Committee: Andrea Serrani PhD (Advisor); Stephen Yurkovich PhD (Committee Member); Kevin Passino PhD (Committee Member) Subjects: Electrical Engineering; Engineering

Master of Science (MS), Ohio University, 2007, Computer Science (Engineering)

This thesis constitutes an effort of verifying and validating a safety system designed for a specific research facility. An initial comprehensive review of the system design is presented, detailing all the relevant aspects of the system and investigating the way its design development interrelates to the formal "safety analysis" procedures proposed in the literature. The verification process includes the development of a complete formal specification for the system and the investigation of how well the original design follows its formal requirements. The validation process details the system's hardware and software implementations, discusses the testing approach, and evaluates the final outcomes. In summary, this work can be considered as an effort to prove that the operation of the laboratory in question, within the designed safety system's scope, is safe.

Committee: Frank Drews (Advisor) Subjects:

Master of Science in Industrial Engineering, Cleveland State University, 2011, Fenn College of Engineering

This research was done because today the Fuel Cell (FC) Industry is still in its infancy in spite over one-hundred years of development has transpired. Although hundreds of fuel cell developers, globally have been spawned, in the last ten to twenty years, only a very few are left struggling with their New Product Development (NPD). The entrepreneurs of this type of disruptive technology, as a whole, do not have a systems engineering ‘roadmap", or template, which could guide FC technology based power system development efforts to address a more environmentally friendly power generation. Hence their probability of achieving successful commercialization is generally, quite low. Three major problems plague the fuel cell industry preventing successful commercialization today. Because of the immaturity of FC technology and, the shortage of workers intimately knowledgeable in FC technology, and the lack of FC systems engineering, process developmental knowledge, the necessity for a commercialization process model becomes evident. This thesis presents a six-phase systems engineering developmental reference model for new product development of a Solid Oxide Fuel Cell (SOFC) Power System. For this work, a stationary SOFC Power System, the subject of this study, was defined and decomposed into a subsystems hierarchy using a Part Centric Top-Down, integrated approach to give those who are familiar with SOFC Technology a chance to learn systems engineering practices. In turn, the examination of the SOFC mock-up could gave those unfamiliar with SOFC Technology a chance to learn the basic, technical fundamentals of fuel cell development and operations. A detailed description of the first two early phases of the systems engineering approach to design and development provides the baseline system engineering process details to create a template reference model for the remaining four phases. The NPD reference template model's systems engineering process, philosophy and design tools (open full item for complete abstract)

Committee: L. Kenneth Keys PhD (Committee Chair); Paul P. Lin PhD (Committee Member); Walter Kocher PhD (Committee Member) Subjects: Alternative Energy; Business Costs; Business Education; Engineering; Environmental Engineering

Doctor of Philosophy, Case Western Reserve University, 2008, Computing and Information Science

User-configurable software systems present many challenges to software testers. These systems are created to address a large number of possible uses, each of which is based on specific configurations. Configurations are made with combinations of configurable elements and settings, leading to a huge number of possible combinations. Since it is infeasible to test all combinations at release, many latent defects remain in the software once deployed. An incremental testing approach is presented, where each customer configuration change requires impact analysis and retesting. This incremental approach involves cooperation and communications between the customer and the software vendor. The process for this approach is presented along with detailed examples of how it can be used on various user-configurable systems in the field. The overall efficiency and effectiveness of this method is shown by a set of empirical studies conducted with real customer configuration changes running on two separate commercially released ABB software systems. These two systems together contained ~3000 configurable elements and ~1.4 million Executable Lines of Code. In these five case studies, 460 failures reported by 100 different customers were analyzed. These empirical studies show that this incremental testing method is effective at detecting latent defects which are exposed by customer configuration changes in user-configurable systems.

Committee: Lee J. White PhD (Advisor); Andy Podgurski PhD (Committee Member); Vincenzo Liberatore PhD (Committee Member); Ken Loparo PhD (Committee Member) Subjects: Computer Science

Doctor of Philosophy, The Ohio State University, 2023, Biomedical Engineering

Cancer metastasis is a complex, systemic, and non-random process requiring tumor cells to both adapt to and manipulate a multitude of microenvironments. Given this complexity, traditional 2D cell culture models offer insufficient structural and biological relevance, while animal models face obstacles in real-time analysis, experimental control, and translational success. As an alternative to address these barriers, this dissertation discusses development of tissue engineered microfluidic device-based tumor-on-a-chip platforms to isolate phases of metastatic colonization and study premetastatic microenvironmental changes. In this dissertation, this hydrogel-based technology was applied in three different aspects of metastatic progression. First, a thyroid metastasis-on-a-chip model was developed to study metastasis suppressor gene RCAN1-4 and its impact on downstream lung colonization. Second, 3D hydrogel scaffolds were implemented to investigate colorectal cancer-induced collagen remodeling by stromal fibroblasts and pericytes during premetastatic niche development. Third, observations of cancer-induced collagen remodeling were used to inform design of a liver premetastatic niche-on-a-chip model to further interrogate immune-myofibroblast crosstalk in response to colorectal cancer signaling and establish the relationship between this crosstalk and metastatic colonization.

Committee: Aleksander Skardal (Advisor); Daniel Gallego-Perez (Committee Member); Jennifer Leight (Committee Member); Jonathan Song (Committee Member) Subjects: Biomedical Engineering; Biomedical Research; Oncology

MS, University of Cincinnati, 2023, Engineering and Applied Science: Aerospace Engineering

Refueling satellites is a relatively new concept that will only be first operational in the coming years. Aerial refueling of aircraft has taken place for decades. However, refueling satellites has one big challenge that refueling aircraft does not. Due to the stringent requirements, refueling satellites requires the use of intelligent autonomous systems which leads to the question of trust in the system. Trust needs to be built upon a comprehensive understanding of the system which in turn need to be integrated into the development of the system requirements. Model-based systems engineering (MBSE) is a proven solution to best capture the systems engineering approach increasing understandability. Furthermore, by providing a visual representation of a system, model-based systems engineering allows for better understanding, traceability, earlier detection of errors, and more. In this research, a model-based system engineering approach is used to show the process of refueling vehicles. The MBSE tool used in this thesis is the Capella workbench developed by Thales, a world leader in mission-critical aerospace systems. Capella, an open-source solution for MBSE provides the ability to graphically model systems, hardware and software architectures based on the Arcadia method. In addition, a comprehensive risk and cost benefit analysis is completed to show the benefits of refueling satellites compared to replacing satellites. The systems engineering approach used in this research helps to provide understandability with the goal of increasing trust in the system and its processes. In addition, mean-time between failures, maintenance levels, and space trusted autonomy readiness levels are discussed to show the accepted risks, what the system will do if a failure occurs, and other expectations of the system. The thesis identifies the life cycle of refueling satellites and provides an insight into the system level challenges.

Committee: Kelly Cohen Ph.D. (Committee Chair); Ou Ma Ph.D. (Committee Member); Manish Kumar Ph.D. (Committee Member); Donghoon Kim Ph.D. (Committee Member) Subjects: Aerospace Engineering

Master of Science, The Ohio State University, 2023, Chemical Engineering

Decision making and optimization are core aspects of many real-world engineering problems, ranging from process optimization to experimental design. Many of these systems are black-box and expensive to evaluate which causes many traditional optimization methods to be difficult to implement in these systems. Additionally, many systems experience heteroskedastic noise when collecting data which many optimization strategies can not account for. Bayesian optimization has successfully been able to use surrogate models to create an easier to optimize system which can be updated by introducing new samples. Bayesian optimization requires the use of surrogate models, most commonly Gaussian processes, to model the sampled data, and acquisition functions to find optimal locations for sampling new points. Traditional Gaussian processes have been unable to heteroskedastic noise in data which led to the development of the heteroskedastic Gaussian processes (HGP). These HGPs are capable of properly accounting for noise in the data and can make more accurate predictions on regions without samples. Acquisition functions however have difficulty handling noise, and the most capable of handling this noise, knowledge gradient, is difficult to optimize and evaluate. This thesis focuses on a new method for implementing knowledge gradient and using knowledge gradient for enhanced decision making. In order to ensure global optimality of the knowledge gradient function, grid based methods generally must be implemented which are inefficient and lead to gaps in the sampling space. A new method, neural network knowledge gradient (NNKG), uses randomly generated initial sampling data to more efficiently explore the sample space and interpolate between samples. This method when compared to the traditional method also allows for enhanced visualization of the knowledge gradient surface which allows for greater understanding in regions of value and enhanced decision making on where to sample next (open full item for complete abstract)

Committee: Bhavik Bakshi (Committee Member); Joel Paulson (Advisor) Subjects: Chemical Engineering

Master of Science, The Ohio State University, 2023, Industrial and Systems Engineering

Deploying new automated or autonomous capabilities has become increasingly prevalent, transforming everyday practices. With the transformation comes increased complexity and the need for appropriate safety evaluations of automated systems. The introduction of novel capabilities can have significant implications for the behavior of the automated system, ultimately adding to the system's complexity and making it challenging to foresee and evaluate potential safety issues. The goal of this work is to provide processes for designers/engineers to recognize when safety cases need to be revised. To achieve this objective, the thesis develops a visual representation and framework that supports determining whether safety cases are sufficient. The visual representation reveals several risks associated with automation and design considerations that support human-automation interactions. The framework will encompass resilience engineering concepts through the evaluation of evidence regarding the system's reliability, robustness, and resilience. This will assist in providing designers/engineers with the ability to recognize potential risks within the system and whether claims are sufficiently backed by evidence. The visual representation and framework are applied to an aviation scenario that exhibits the processes designers/engineers need to consider. Findings from this process can support further improvements in the design and safety of complex automated systems.

Committee: Martijn IJtsma (Advisor); David Woods (Committee Member) Subjects: Industrial Engineering; Systems Design