As the number of spacecraft in simultaneous operation continues to grow, there is an increased dependency on ground-based navigation support. The current baseline system for deep space navigation utilizes Earth-based radiometric tracking, which requires long duration, often global, observations to perform orbit determination and generate a state update. The age, complexity, and high utilization of the assets that make up the Deep Space Network (DSN) pose a risk to spacecraft navigation performance. With increasingly complex mission operations, such as automated asteroid rendezvous or pinpoint planetary landing, the need for high accuracy and autonomous navigation capability is further reinforced. The Network-Based Navigation (NNAV) method developed in this research takes advantage of the growing inter-spacecraft communication network infrastructure to allow for autonomous state measurement. By embedding navigation headers into the data packets transmitted between nodes in the communication network, it is possible to provide an additional source of navigation capability. Simulation results indicate that as NNAV is implemented across the deep space network, the state estimation capability continues to improve, providing an embedded navigation network. To analyze the capabilities of NNAV, an analysis and simulation framework is designed that integrates navigation and communication analysis. Model-Based Systems Engineering (MBSE) and Agent-Based Modeling (ABM) techniques are utilized to foster a modular, expandable, and robust framework. This research has developed the Space Navigation Analysis and Performance Evaluation (SNAPE) framework. This framework allows for design, analysis, and optimization of deep space navigation and communication architectures. SNAPE captures high-level performance requirements and bridges them to specific functional requirements of the analytical implementation. The SNAPE framework is implemented in a representative prototype environment using the Python language and verified using industry standard packages. The capability of SNAPE is validated through a series of example test cases. These analyses focus on the performance of specific state measurements to state estimation performance, and demonstrate the core analytic functionality of the framework. Specific cases analyze the effects of initial error and measurement uncertainty on state estimation performance. The timing and frequency of state measurements are also investigated to show the need for frequent state measurements to minimize navigation errors. The dependence of navigation accuracy on timing stability and accuracy is also demonstrated. These test cases capture the functionality of the tool as well as validate its performance. The SNAPE framework is utilized to capture and analyze NNAV, both conceptually and analytically. Multiple evaluation cases are presented that focus on the Mars Science Laboratory's (MSL) Martian transfer mission phase. These evaluation cases validate NNAV and provide concrete evidence of its operational capability for this particular application. Improvement to onboard state estimation performance and reduced reliance on Earth-based assets is demonstrated through simulation of the MSL spacecraft utilizing NNAV processes and embedded packets within a limited network containing DSN and MRO. From the demonstrated state estimation performance, NNAV is shown to be a capable and viable method of deep space navigation. Through its implementation as a state augmentation method, the concept integrates with traditional measurements and reduces the dependence on Earth-based updates. Future development of this concept focuses on a growing network of assets and spacecraft, which allows for improved operational flexibility and accuracy in spacecraft state estimation capability and a growing solar system-wide navigation network.

Over the past few decades, not only have systems continued to increase in complexity, but they are expected to be delivered in the same timeframe and cost range. Technology has advanced us into what some refer to as the 4th Industrial Revolution. Digital is becoming the expectation in all areas of people's lives. Model-Based Systems Engineering (MBSE) represents the transition of systems into this new digital age, promising many improvements over the previous Document-Based Systems Engineering. This transition, however, is not simple. MBSE is a major paradigm shift for systems engineers, especially for those who have been in this field for many years. In order to work as intended, MBSE requires the participation of many different disciplines and functionalities in an organization. Gaining this level of organizational collaboration, however, is no easy task. Organizational structure and culture have intuitively been believed to be critical barriers to the successful adoption of MBSE, but little work has been done to discover what the impacts of these organizational factors are. The purpose of this research is to further explore the MBSE adoption process in the context of the organization. There were three research objectives designed to address the research question: how does organizational structure influence the adoption and implementation of MBSE? Research objective one was: relate organizational structure characteristics to MBSE adoption and implementation measures. Research objective two was: discover how organizational factors contribute to decisions made and other aspects of the MBSE adoption process. Research objective three was: connect different organizational structure and adoption variables together to derive critical variables in the adoption process. Research objective one was carried out using a survey as the instrument. The objective of the survey was to examine what the effects of organizational structure are on MBSE adoption and implementation. Organizational structure was represented by seven variables: Size, Formalization, Centralization, Specialization, Vertical Differentiation, Flexibility, and Interconnectedness. These are different characteristics of organizational structure that can be measured on a scale. MBSE adoption and implementation was represented by one adoption and three implementation variables. These include Adoption Process, Maturity of MBSE, Use of MBSE, and Influence on organizational outcomes. A total of 51 survey responses were received that met the inclusion criteria. Factor analysis was done for variables with multi-item measures. The factors were then analyzed using pairwise correlations to determine which relationships were significant. Formalization, Flexibility, and Interconnectedness were found to have positive correlations with adoption and implementation variables. Size and Vertical Differentiation had a negative correlation with Use of MBSE (implementation). Centralization was found to have negative correlations with adoption and implementation. Specialization did not have any significant correlations. Research objective two utilized semi-structured interviews as the main instrument. Survey participants had the opportunity to provide more detailed explanations of their organizations' experiences in the form of follow-up interviews. Eighteen survey participants agreed to this follow-up interview focused on MBSE adoption. Two of the participants shared failed adoption experiences, with the rest were at various stages of the adoption process. One of the most emergent themes out of the interviews was the idea of integration. Integration needs to occur at the organizational level, and the technical level. The technical level refers to the fact that tools, models, and/or data repositories need to be linked together in some way. Integration also has to occur at the organizational level, because a lot of different functional areas need to come together for MBSE. The way that organizations can address the issue of integration is through coordination mechanisms. The ultimate goal is to achieve implicit coordination through the use of connected models, but getting to that point will require coordination between different subunits. Interview responses were evaluated for coordination mechanisms, or situations that showed a distinct lack of a coordination mechanism. The lack of coordination mechanisms largely consists of a lack of standardization, lack of communication between subunits, and issues of authority. The final research objective of this work was carried out through a causal analysis using the data obtained from the survey and interviews. The purpose of this analysis was to visualize and better understand the adoption process. According to the calculated measures of centrality, the important nodes in this model are Improved organizational outcomes, Coordination between subunits, Projects use tools/methods, and People willing to use tools. Improved organizational outcomes is part of a key loop in the causal model. Improved organizational outcomes contributes to leaders and employees' willingness to support and use MBSE methods and tools, which contribute to actual use of tools and methods. This creates more Improved organizational outcomes, completing the loop. The survey results showed that Formalization, Decentralization, Flexibility, and Interconnectedness all have positive correlations with the Influence on organizational outcomes. So these organizational structure components are external factors that can be used to positively impact the adoption loop. Overall, this work provided several contributions to the field regarding the MBSE adoption process in an organizational setting. Organizational structure was shown to have significant correlations with adoption and implementation of MBSE. Coordination mechanisms were identified as a method to achieve integration across different functional areas of the organization. Improved organizational outcomes was shown to be a critical variable in the adoption process as an avenue for organizational structure factors to have a positive effect on the adoption process. / Doctor of Philosophy / Over the past few decades, not only have systems continued to increase in complexity, but they are expected to be delivered in the same timeframe and cost range. Technology has advanced us into what some refer to as the 4th Industrial Revolution. Digital is becoming the expectation in all areas of people's lives. Model-Based Systems Engineering (MBSE) represents the transition of systems into this new digital age, promising many improvements over the previous Document-Based Systems Engineering. This transition, however, is not simple. MBSE is a major mindset change for systems engineers, especially for those who have been in this field for many years. In order to work as intended, MBSE requires the participation of many different disciplines and functionalities in an organization. Gaining this level of organizational collaboration, however, is no easy task. Organizational structure and culture have intuitively been believed to be critical barriers to the successful adoption of MBSE, but little work has been done to discover what the impacts of these organizational factors are. This research looks into how organizational structure may have an impact on MBSE adoption and implementation. This research was carried out with the use of three different methods: an online survey, semi-structured interviews, and a causal analysis. The data obtained from the survey and interviews was used to construct a causal model depicting the MBSE adoption process. Overall, this work provided several contributions to the field regarding the MBSE adoption process in an organizational setting. Organizational structural variables were shown to have significant correlations with adoption and implementation of MBSE. Formalization, Flexibility, and Interconnectedness were found to have positive correlations with adoption and implementation variables, while Centralization had negative correlations with adoption and implementation. Coordination mechanisms were identified as a method to achieve integration across different functional areas of the organization. Interview responses were evaluated for coordination mechanisms, or situations that showed a distinct lack of a coordination mechanism. The lack of coordination mechanisms largely consists of a lack of standardization, lack of communication between subunits, and issues of authority. The causal analysis showed that Improved organizational outcomes, Coordination between subunits, Projects use tools/methods, and People willing to use tools were the critical variables in the MBSE adoption process.

Les systèmes actuels tendent à être intégrés les uns avec les autres. Mais cette intégration n'est pas forcément prévue à l'origine du système. Cette tendance créée des systèmes de systèmes. Un système de système de systèmes est un système constitué de systèmes qui sont gérés par des équipes indépendantes, qui sont fonctionnellement indépendants, qui collaborent, qui évoluent et qui sont géographiquement distribués. La communication entre les différentes équipes facilite la conception d'un système de systèmes. Cette communication peut être réalisée par l'utilisation de modèles et de simulation. Cependant, la modélisation du système de systèmes et la modélisation des simulations ne reposent pas sur les mêmes langages. Pour assurer la cohérence des modèles, il faut pouvoir créer les modèles de simulation à partir des modèles système. Cependant, il faut tenir compte des contraintes liées aux propriétés des systèmes de systèmes. Il faut être capable de manipuler des modèles systèmes réalisés dans des langages différents, de réaliser des simulations de natures différentes et suivre les évolutions des langages de modélisation et des outils de simulation. Pour répondre à ces problématiques, nous avons défini l'environnement Role4AII pour la manipulation de modèles systèmes réalisés dans des langages hétérogènes. Role4AII est basé sur la notion de rôles. Les rôles permettent de créer des simulations en accédant aux informations contenues dans des éléments de modèles indépendamment de leur type. Role4AII est capable de prendre en entrée des modèles sérialisés par différents outils grâce à l'utilisation de parsers combinateurs. Ces derniers apportent modularité et extensibilité aux fonctionnalités d'import. L'environnement Role4AII a été utilisé sur un exemple de système de systèmes : l'observatoire sous-marin MeDON. / Current Systems tend to become integrated with each others. However, this intégration may not be designed for the System. This trend raises the concept of System of Systems. A System of Systems is a System made of Systems which are managed independently, functionaly independent, collaborating, evolving and geographically distributed. The communication among the different teams eases the design of the System of Systems. This communication may be made through the use of models and simulation.However, System of Systems models and simulation models do not rely on the same modeling languages. In order to ensure coherency between the two types of models, simulation models should be obtained from System models. But this approach should take into account the constraints coming from the properties of System of Systems. System models made in different modeling languages should be handled, simulation of different kinds should be generated and the evolution of both modeling languages and simulation tools should be managed.In order to tackle these issues, we defined the Role4AII environment to manipulate System models made in heterogeneous modeling languages. Role4AII is based on the concept of rôles. Rôles enable to create simulations by accessing to information stored in model éléments despite their types differences. Role4AII is able to take as input serialized models from different modeling tools by using parser combinators. Parser combinators bring modularity and extensibility to the import features. Role4AII has been used on a System of System example: the MeDON seafloor observatory.

Given that achieving nominal (all dimensions are theoretically perfect) geometry is challenging during building construction, understanding and anticipating sources of geometric variation through tolerances modeling and allocation is critical. However, existing building modeling environments lack the ability to support coordinated, incremental and systematic specification of manufacturing and construction requirements. This issue becomes evident when adding multi-material systems produced off site by different vendors during building erection. Current practices to improve this situation include costly and time-consuming operations that challenge the relationship among the stakeholders of a project. As one means to overcome this issue, this research proposes the development of a knowledge-aided modeling framework that integrates a parametric CAD tool with a system modeling application to assess variability in building construction. The CAD tool provides robust geometric modeling capabilities, while System Modeling allows for the specification of feature-based manufacturing requirements aligned with construction standards and construction processes know-how. The system facilitates the identification of conflicting interactions between tolerances and manufacturing specifications of building material systems. The expected contributions of this project are the representation of manufacturing knowledge and tolerances interaction across off-site building subsystems to identify conflicting manufacturing requirements and minimize costly construction errors. The proposed approach will store and allocate manufacturing knowledge as Model-Based Systems Engineering (MBSE) design specifications for both single and multiple material systems. Also, as new techniques in building design and construction are beginning to overlap with engineering methods and standards (e.g. in-factory prefabrication), this project seeks to create collaborative scenarios between MBSE and Building Information Modeling (BIM) based on parametric, simultaneous, software integration to reduce human-to-data translation errors, improving model consistency among domains. Important sub-stages of this project include the comprehensive review of modeling and allocation of tolerances and geometric deviations in design, construction and engineering; an approach for model integration among System Engineering models, mathematical engines and BIM (CAD) models; and finally, a demonstration computational implementation of a System-level tolerances modeling and allocation approach.

An efficient system development needs reuse, traceability and understanding. Today, specifications are usually written in text documents. Reuse means a copy and paste of suitable specifications. Traceability is the textual note that references to affected requirements. Achieving a full context understanding requires reading hundreds of pages in a variety of documents. Changing one textual requirement in complex systems can be very time-consuming. Model-based systems engineering (MBSE) addresses these issues. There, an integrated system model is used for the design, analysis, communication and system specification and shall contribute to handling the system complexity. This paper shows aspects of this approach in the development of a wheel loader\'s attachment system. Customer requirements will be used to derive a specification model. Based on this, the author introduces the system and software architecture. The connection between requirement and architecture leads to a traceable system design and produces the huge advantage of MBSE.

Building Information Modeling, or BIM, is a digital representation of physical and functional characteristics of a facility that serves as a shared resource for information for decision-making throughout the project lifecycle (National Institute of Building Sciences, 2007). The masonry construction industry currently suffers from the lack of BIM integration. Where other industries and trades have increased productivity by implementing standards for software-enhanced workflows, masonry construction has failed to adopt information tools and processes. New information technology and process modeling tools have grown in popularity and their use is helping to understand and improve construction processes. The Systems Modeling Language, or SysML, is one of the process modeling tools we can use to model and analyze the various processes and workflows. In this research, a case study methodology was applied to analyze the masonry construction industry to understand the current state of masonry construction processes and workflows. This thesis reviews these concepts and the applied case studies which are necessary to move forward with the implementation of BIM for masonry.

An efficient system development needs reuse, traceability and understanding. Today, specifications are usually written in text documents. Reuse means a copy and paste of suitable specifications. Traceability is the textual note that references to affected requirements. Achieving a full context understanding requires reading hundreds of pages in a variety of documents. Changing one textual requirement in complex systems can be very time-consuming. Model-based systems engineering (MBSE) addresses these issues. There, an integrated system model is used for the design, analysis, communication and system specification and shall contribute to handling the system complexity. This paper shows aspects of this approach in the development of a wheel loader\'s attachment system. Customer requirements will be used to derive a specification model. Based on this, the author introduces the system and software architecture. The connection between requirement and architecture leads to a traceable system design and produces the huge advantage of MBSE.

Increased use of data is influencing the existing practices in the engineering domain,including that of systems engineering. Complex products and systems (CoPS), along with its predominant methodology of development, Model-based systems engineering(MBSE), is no exception to this. This thesis explores the possible integration of the emerging data driven methods and the established model-based methods in the context of CoPS development. It also explores what the implications of such an integration could be for the organizations building such systems, the system integrators. To analyse the current state of the art in CoPS development and model based methods as well as the emerging trends in data driven methods, this research employs an integrative literature review method. The literature search concluded in 71 selected articles to be reviewed. These articles where divided over three main categories, CoPS, Model-based systems engineering (MBSE) and data driven methods.The results of the analysis suggest that data driven methods and the model-based methods complement rather than compete throughout the innovation life cycle of CoPS. The findings indicate that an integration of the methods is beneficial to the architectural, systemic, and component level innovation in CoPS. MBSE and data driven methods could however have different levels of influence in these three types of innovation. The findings indicate that MBSE could have more influence in architectural innovations, while data driven methods could be more influential in systemic and component innovation. The continuous innovation in the use phase of system is also seen to be improved by this integration. The system integrators benefit from the improved project to project learning resulting from the integration which enhances their economy of repetition. An integrated method could also increase the speed of which decisions can be made while still maintaining reliability in the system. The results indicate that the number of iterations could increase due to the increased feedback of data and the learnings gained from it, which could pose some challenge to the existing project management methods. Further research is needed to find out what are the full benefits of an integrated method and identify other potential conflicts.

The need to secure critical infrastructure (CI) systems against attacks is a topic that has been discussed recently in literature. Many examples of attacks against CI exist, such as the physical attack on the Pacific Gas and Electric Metcalf substation in 2013 that caused millions of dollars in damage or the Stuxnet cyber-attack which was identified in 2010 that caused damage to Iran’s nuclear program and alerted the world to the existence of cyber weapons. As a result of these types of events in which vulnerabilities in CI are exploited, it is important to have a comprehensive systems approach for assessing the vulnerabilities in CI systems. This dissertation seeks to provide a method for engineers to use system and human architectures to perform vulnerability assessment (VA) and decision analysis to enable decision makers to make tradeoffs on how to use their resources to protect CI against attacks.There are several gaps in literature in how to use system and human architectures to perform VA to protect CI from damage. First, no method exists that uses a model based approach and human and system architectures to perform a comprehensive analysis of CI to develop decision analysis models to aid decision makers in determining the most effective use of security resources to secure their CI systems. It is important that such models be comprehensive by including industry standards, system and human architectures, attack scenarios, subject matter expert opinion and models for analysis to help decision makers determine the best security investments. Second, there is not an established method to develop detailed mathematical models from an operational activity diagram that represents an attack scenario. This is important because the translation from architecture to high fidelity models will enable CI asset owners to make tradeoffs on security resource use. Finally, there is no method to evaluate the role of humans in a CI VA based on human views of the system. This dissertation provides an approach to use human and system architectures to perform VA and decision analysis to fill these gaps.

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