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<div>The internal and external interactions between the complex structural and behavioral characteristics of the system of interest and the surrounding environment result in unpredictable emergent behaviors. These emergent behaviors are not well understood, especially when modeled using the traditional top-down systems engineering approach. The intrinsic nature of current complex systems has called for an elegant solution that provides an integrated framework in Model-Based Systems Engineering. A considerable gap exists to integrate system engineering activities and engineering analysis, which results in high risk and cost. This thesis presents a framework that incorporates indefinite and definite modeling aspects that are developed to determine the complexity that arises during the development phases of the system. This framework provides a workflow for modeling complex systems using Systems Modeling Language (SysML) that captures the system’s requirements, behavior, structure, and analytical aspects at both problem definition and solution levels. This research introduces a new level/dimension to the framework to support engineering analysis integrated with the system architecture model using FMI standards. A workflow is provided that provides the enabling methodological capabilities. It starts with a statement of need and ends with system requirement verification. Detailed traceability is established that glues system engineering and engineering analysis together. Besides, a method is proposed for predicting the system’s complexity by calculating the complexity index that can be used to assess the complexity of the existing system and guide the design and development of a new system. To test and demonstrate this framework, a case study consisting of a complex district cooling system is implemented. The case study shows the framework’s capabilities in enabling the successful modeling of a complex district cooling system. The system architecture model was developed using SysML and the engineering analysis model using Modelica. The proposed framework supports system requirements verification activity. The analysis results show that the district chiller model developed using Modelica produces chilled water below 6.6 degrees Celsius, which satisfies the system requirement for the district chiller system captured in the SysML tool. Similarly, many such requirement verification capabilities using dynamic simulation integration with the high-level model provides the ability to perform continuous analysis and simulation during the system development process. The systems architecture complexity index is measured for the district cooling case study from the black-box and white box-perspective. The measured complexity index showed that the system architecture’s behavioral aspect increases exponentially compared to the structural aspect. The systems architecture’s complexity index at black-box and white-box was 4.998 and 67.3927, respectively.</div>

Indiana University-Purdue University Indianapolis (IUPUI) / The internal and external interactions between the complex structural and behavioral characteristics of the system of interest and the surrounding environment result in unpredictable emergent behaviors. These emergent behaviors are not well understood, especially when modeled using the traditional top-down systems engineering approach. The intrinsic nature of current complex systems has called for an elegant solution that provides an integrated framework in Model-Based Systems Engineering. A considerable gap exists to integrate system engineering activities and engineering analysis, which results in high risk and cost. This thesis presents a framework that incorporates indefinite and definite modeling aspects that are developed to determine the complexity that arises during the development phases of the system. This framework provides a workflow for modeling complex systems using Systems Modeling Language (SysML) that captures the system’s requirements, behavior, structure, and analytical aspects at both problem definition and solution levels. This research introduces a new level/dimension to the framework to support engineering analysis integrated with the system architecture model using FMI standards. A workflow is provided that provides the enabling methodological capabilities. It starts with a statement of need and ends with system requirement verification. Detailed traceability is established that glues system engineering and engineering analysis together. Besides, a method is proposed for predicting the system’s complexity by calculating the complexity index that can be used to assess the complexity of the existing system and guide the design and development of a new system. To test and demonstrate this framework, a case study consisting of a complex district cooling system is implemented. The case study shows the framework’s capabilities in enabling the successful modeling of a complex district cooling system. The system architecture model was developed using SysML and the engineering analysis model using Modelica. The proposed framework supports system requirements verification activity. The analysis results show that the district chiller model developed using Modelica produces chilled water below 6.6 degrees Celsius, which satisfies the system requirement for the district chiller system captured in the SysML tool. Similarly, many such requirement verification capabilities using dynamic simulation integration with the high-level model provides the ability to perform continuous analysis and simulation during the system development process. The systems architecture complexity index is measured for the district cooling case study from the black-box and white box-perspective. The measured complexity index showed that the system architecture’s behavioral aspect increases exponentially compared to the structural aspect. The systems architecture’s complexity index at black-box and white-box was 4.998 and 67.3927, respectively.

<p>The accelerating digital transformation in the aerospace and defense sectors underlines a pressing need for refined, standardized methodologies for integrating digital tools into mission engineering (ME).  Existing literature reveals a distinct gap in the practical application of Digital Engineering (DE) and Model-Based Systems Engineering (MBSE) within ME, particularly due to the lack of clear guidelines for implementation. Recognizing DE and MBSE's potential to manage complex systems, this thesis puts forth a novel DE framework specifically designed for ME to streamline digital tool integration.</p> <p><br></p> <p>The research approach includes a thorough literature review, the development and application of a unique framework for an active debris remediation mission, and the examination of case studies. The findings inform the ongoing digital transformation efforts in the defense and aerospace sectors, offering valuable insights into the real-world application of DE and MBSE within ME. This study focuses on the early-stage, holistic-view design processes of mission and system-of-systems, aiming to establish a robust foundation for detailed lower-level engineering design.</p> <p><br></p> <p>By exploring how to leverage current practices to develop a framework that supports digital artifacts and tools, assessing the framework's improvements, and exploring possible quantification of its impact, this thesis charts a path toward the successful execution of complex mission objectives in the digital era.</p>

<p>Is North America vulnerable to widespread electrical blackout from natural or man-made disasters? Yes. Are electric utilities and critical infrastructure (CI) operators prepared to maintain CI operations such as, hospitals, sewage lift stations, food, water, police stations etc., after electrical blackout to maintain National security and sustainability? No. Why? Requirements to prioritize electrical restoration to CI do not exist as a requirement or regulation for electrical distribution operators. Thus, the CI operators cannot maintain services to the public without electricity that provides power for the critical services to function. The problem is that electric utilities are not required to develop or deploy a prioritized systematic plan or procedure to decrease the duration of electrical outage, commonly referred to as blackout. The consequence of local blackout to CI can be multi-billion-dollar financial losses and loss of life for a single outage event attributed to the duration of blackout. This study utilized the review of authoritative literature to answer the question: “Can a plan be developed to decrease the duration of electrical outage to critical infrastructure”. The literature revealed that electric utilities are not required to prioritize electrical restoration efforts and do not have plans available to deploy minimizing the duration of blackout to CI. Thus, this study developed a plan and subsequent model using Model Based System Engineering (MBSE) to decrease the duration of blackout by providing incremental electrical service to CI.</p>

Ein wesentliches Ziel während der Produktentwicklung ist die frühzeitige Absicherung der Produkteigenschaften auf Basis der definierten Produktmerkmale unter Beachtung der äußeren Randbedingungen. Digitale Modelle und Methoden unterstützen den Produktentwickler bei der frühzeitigen virtuellen Evaluation des Produktes. [...] In diesem Beitrag wird eine Methodik präsentiert, die mit Hilfe der SysML-Modelle eine Simulation in VR ermöglicht. Die SysML-Beschreibung wird als Kern der Simulation dienen und das gesamte Simulationsmodell steuern. Weiterhin wird erläutert, wie die SysML-Beschreibung mit einem VR-Tool und einem Physikberechnungstool verbunden werden kann. Die in CAVE und HMD durchgeführten Simulationen wurden im Rahmen von Usability Tests evaluiert. Aus diesen Tests werden Ergebnisse präsentiert, die sich mit Verwendungsschwerpunkten in VR und der Zufriedenheit bei der Beurteilung von Produkten in VR beschäftigt haben. Schlussendlich wird ein Beispiel-Simulationsszenario in der CAVE-VR und einem Head Mounted Display (HMD) diskutiert. [... aus der Einleitung]

Designing and developing complex engineering systems is a collaborative effort. In Model-Based Systems Engineering (MBSE), this collaboration is supported through the use of formal, computer-interpretable models, allowing stakeholders to address concerns using well-defined modeling languages. However, because concerns cannot be separated completely, implicit relationships and dependencies among the various models describing a system are unavoidable. Given that models are typically co-evolved and only weakly integrated, inconsistencies in the agglomeration of the information and knowledge encoded in the various models are frequently observed. The challenge is to identify such inconsistencies in an automated fashion. In this research, a probabilistic (Bayesian) approach to abductive reasoning about the existence of specific types of inconsistencies and, in the process, semantic overlaps (relationships and dependencies) in sets of heterogeneous models is presented. A prior belief about the manifestation of a particular type of inconsistency is updated with evidence, which is collected by extracting specific features from the models by means of pattern matching. Inference results are then utilized to improve future predictions by means of automated learning. The effectiveness and efficiency of the approach is evaluated through a theoretical complexity analysis of the underlying algorithms, and through application to a case study. Insights gained from the experiments conducted, as well as the results from a comparison to the state-of-the-art have demonstrated that the proposed method is a significant improvement over the status quo of inconsistency identification in MBSE.

Mechatronic design is an enigma. On the one hand, mechatronic products promise enhanced functionality, and better performance at reduced cost. On the other hand, optimizing mechatronic design concepts is a major challenge to overcome during the design process. In the past, less attention has been paid to the life phases of a mechatronic product, and it was assumed that modifications in electronics and software will ensure that the product performs to expectation throughout its life time. However it has been realized that introducing design changes in mechatronics is not easy, since it is difficult to assess the consequences of a design decision, both during the design process of a new product, and during a design modification. It is also realized that there is a strong need to consider the product's life phases during the early phases of product development. Furthermore, it is rather difficult to perform a design optimization since it requires introducing changes across different domains, which is not well supported by the methods and tools available today. This thesis investigates the topic of mechatronic design and attacks some of the major challenges that have been identified regarding the design of mechatronic products. The goal is to provide support to the designers to facilitate better understanding of the consequences of their design choices as early as possible. The work also aims to provide support for assessing alternative design concepts, and for optimizing a design concept based on requirements, constraints and designer preferences at the time of design. The thesis highlights three main challenges related to mechatronic product development: the need for a common language during conceptual design; the inadequate information transfer between engineering domains; and the difficulty in assessing the properties of competing mechatronic concepts. A model-based integration approach is presented, and these key challenges are considered in relation to an integrated modeling and design infrastructure. The approach is illustrated through the design of two mechatronic systems- a two degrees-of-freedom robot, and a hospital bed propulsion system. Initial results provide evidence of good potential for information transfer across mechatronic domains. Although SysML was used for the case studies, some important questions were raised about its suitability as a common language for mechatronics. Suggestions for future work are: to utilize the developed infrastructure and incorporate a capability to model and assess consequences of competing design concepts; provide support for optimizing these concepts; and evaluate the usefulness of the developed infrastructure in a real-world design setting. These efforts should provide ample information to the designer for making adequate decisions during the design process. / QC 20110629

Energy and environmental impacts are two factors that will influence urban region composition in the near future. One emerging issue is the effect on water usage resulting from changes in regional or urban transportation trends. With many regions experiencing stresses on water availability, transportation planners and users need to combine information on transportation-related water consumption for any region and assess potential impacts on local water resources from the expansion of alternative transportation modes. This thesis will focus on use-phase water consumption factors for multiple vehicle modes, energy and fuel pathways, roads, and vehicle infrastructure for a given transportation network. While there are studies examining life cycle impacts for energy generation and vehicle usage, few repeatable models exist for assessing overall water consumption across several transportation modes within urban regions. As such, the question is: is it possible to develop a traceable decision support model that combines and assesses water consumption from transportation modes and related mobility infrastructure for a given mobility network? Based on this, an object-oriented system model of transportation elements was developed using the Systems Modeling Language (SysML) and Model-Based Systems Engineering principles to compare water consumption across vehicle modes for assessing the resiliency of existing infrastructure and water resources. To demonstrate the intent of this model, daily network usage water consumption will be analyzed for current and alternative network scenarios projected by policies regarding the expansion of alternative energy. The model is expected to show variations in water consumption due to fluctuations in energy pathways, market shares, and driving conditions, from which the model should help determine the feasibility of expanding alterative vehicles and fuels in these networks. While spatially explicit data is limited compared to the national averages that are used as model inputs, the analytical framework within this model closely follows that of existing assessments and the reusable nature of SysML model elements allows for the future expansion of additional transportation modes and infrastructure as well as other environmental analyses.

Industrial revolutions bring dynamic change to industry through major technological advances (Freeman & Louca, 2002). People and companies must take advantage of industrial revolutions in order to reap its benefits (Bruland & Smith, 2013). Currently, the 4th industrial revolution, industry is transforming advanced manufacturing and engineering capabilities through digital transformation. Company X’s production system was investigated in the research. Detailed evaluation the production process revealed bottlenecks and inefficiency (Melton, 2005). Using the Digital Twin and Discrete Event Factory Simulation, the researcher gathered factory and production input data to simulate the process and provide a system level, holistic view of Company X’s production system to show how factory simulation enables process improvement. The National Academy of Engineering supports Discrete Event Factory Simulation as advancing Personalized Learning through its ability to meet the unique problem solving needs of engineering and manufacturing process through advanced simulation technology (National Academy of Engineering, 2018). The directed project applied two process optimization experiments to the production system through the simulation tool, 3DExperience wiht the DELMIA application from Dassualt Systemes (Dassault, 2018). The experiment resulted in a 10% improvement in production time and a 10% reduction in labor costs due to the optimization