Software-based system architecture has been recognized as a foundation laying out the underpinnings that are critically important for successful engineering of large-scale complex systems. In recent years, architecting has played a more crucial role in engineering network-centric system of systems. The software paradigm has been shifting from treating software as a product (SaaP) to treating software as a service (SaaS). SaaS is also referred to as the Cloud Computing, where the term "cloud" is used as a metaphor for "network". As the complexity of the architecture of network-centric software-based system of systems has increased, the description of such architecture has posed significant technical challenges. The U.S. Department of Defense (DoD) has developed the DoD Architecture Framework [DoDAF 2009a, DoDAF 2009b] for describing system architectures. IEEE proposes a Recommended Practice for Architectural Description of Software-Intensive Systems [IEEE 2000]. SEI provides high-level guidelines for Documenting Software Architectures [Clements et al 2003]. However, all of the diagrams proposed by DoD, IEEE, and SEI are two-dimensional static graphical and textual representations that do not reveal the dynamic characteristics of a system architecture. This thesis presents a conceptual framework (CF) for specifying the architecture of a network-centric software-based system of systems. The developed CF provides the beginning part of a larger research effort. The main goal of the overall research is to employ the automation-based software paradigm and to automatically generate a visual simulation model of a system architecture, with which experiments can be conducted to assess the dynamic characteristics of that architecture. The CF, developed in the research described herein, enables the automatic generation of a visual simulation model representing a system architecture. The proposed CF is evaluated in half a dozen case studies to demonstrate that it provides the necessary elements for automatic generation of a simulation model as the description of a complex system of systems architecture. / Master of Science

Recently, the environmental impact of aircraft and rising fuel prices have become an increasing concern in the aviation industry. To address these problems, organizations such as NASA have set demanding goals for reducing aircraft emissions, fuel burn, and noise. In an effort to reach the goals, a movement toward more-electric aircraft and electric propulsion has emerged. With this movement, the number of critical electrical loads on an aircraft is increasing causing power system reliability to be a point of concern. Currently, power system reliability is maintained through the use of back-up power supplies such as batteries and ram-air-turbines (RATs). However, the increasing power requirements for critical loads will quickly outgrow the capacity of the emergency devices. Therefore, reliability needs to be addressed when designing the primary power distribution system. Power system reliability is a function of component reliability and redundancy. Component reliability is often not determined until detailed component design has occurred; however, the amount of redundancy in the system is often set during the system architecting phase. In order to meet the capacity and reliability requirements of future power distribution systems, a method for redundancy allocation during the system architecting phase is needed. This thesis presents an aircraft power system design methodology that is based upon the engineering decision process. The methodology provides a redundancy allocation strategy and quantitative trade-off environment to compare architecture and technology combinations based upon system capacity, weight, and reliability criteria. The methodology is demonstrated by architecting the power distribution system of an aircraft using turboelectric propulsion. The first step in the process is determining the design criteria which includes a 40 MW capacity requirement, a 20 MW capacity requirement for the an engine-out scenario, and a maximum catastrophic failure rate of one failure per billion flight hours. The next step is determining gaps between the performance of current power distribution systems and the requirements of the turboelectric system. A baseline architecture is analyzed by sizing the system using the turboelectric system power requirements and by calculating reliability using a stochastic flow network. To overcome the deficiencies discovered, new technologies and architectures are considered. Global optimization methods are used to find technology and architecture combinations that meet the system objectives and requirements. Lastly, a dynamic modeling environment is constructed to study the performance and stability of the candidate architectures. The combination of the optimization process and dynamic modeling facilitates the selection of a power system architecture that meets the system requirements and objectives.