Thesis (Ph. D.)--University of Texas at Austin, 2004. / Supervisor: Mary F. Wheeler. Vita. Includes bibliographical references. Also available from UMI.

Engineering complex systems and New Product Development (NPD) are major challenges for contemporary engineering design and must be studied at three levels of: Products, Processes and Organizations (PPO). The science of complexity indicates that complex systems share a common characteristic: they are robust yet fragile. Complex and large scale systems are robust in the face of many uncertainties and variations; however, they can collapse, when facing certain conditions. This is so since complex systems embody many subtle, intricate and nonlinear interactions. If formal modelling exercises with available computational approaches are not able to assist designers to arrive at accurate predictions, then how can we immunize our large scale and complex systems against sudden catastrophic collapse? This thesis is an investigation into complex product design. We tackle the issue first by introducing a template and/or design methodology for complex product design. This template is an integrated product design scheme which embodies and combines elements of both design theory and organization theory; in particular distributed (spatial and temporal) problem solving and adaptive team formation are brought together. This design methodology harnesses emergence and innovation through the incorporation of massive amount of numerical simulations which determines the problem structure as well as the solution space characteristics. Within the context of this design methodology three design methods based on measures of complexity are presented. Complexity measures generally reflect holistic structural characteristics of systems. At the levels of PPO, correspondingly, the Immunity Index (global modal robustness) as an objective function for solutions, the real complexity of decompositions, and the cognitive complexity of a design system are introduced These three measures are helpful in immunizing the complex PPO from chaos and catastrophic failure. In the end, a conceptual decision support system (DSS) for complex NPD based on the presented design template and the complexity measures is introduced. This support system (IMMUNE) is represented by a Multi Agent Blackboard System, and has the dual characteristic of the distributed problem solving environments and yet reflecting the centralized viewpoint to process monitoring. In other words IMMUNE advocates autonomous problem solving (design) agents that is the necessary attribute of innovative design organizations and/or innovation networks; and at the same time it promotes coherence in the design system that is usually seen in centralized systems.

Engineering complex systems and New Product Development (NPD) are major challenges for contemporary engineering design and must be studied at three levels of: Products, Processes and Organizations (PPO). The science of complexity indicates that complex systems share a common characteristic: they are robust yet fragile. Complex and large scale systems are robust in the face of many uncertainties and variations; however, they can collapse, when facing certain conditions. This is so since complex systems embody many subtle, intricate and nonlinear interactions. If formal modelling exercises with available computational approaches are not able to assist designers to arrive at accurate predictions, then how can we immunize our large scale and complex systems against sudden catastrophic collapse? This thesis is an investigation into complex product design. We tackle the issue first by introducing a template and/or design methodology for complex product design. This template is an integrated product design scheme which embodies and combines elements of both design theory and organization theory; in particular distributed (spatial and temporal) problem solving and adaptive team formation are brought together. This design methodology harnesses emergence and innovation through the incorporation of massive amount of numerical simulations which determines the problem structure as well as the solution space characteristics. Within the context of this design methodology three design methods based on measures of complexity are presented. Complexity measures generally reflect holistic structural characteristics of systems. At the levels of PPO, correspondingly, the Immunity Index (global modal robustness) as an objective function for solutions, the real complexity of decompositions, and the cognitive complexity of a design system are introduced These three measures are helpful in immunizing the complex PPO from chaos and catastrophic failure. In the end, a conceptual decision support system (DSS) for complex NPD based on the presented design template and the complexity measures is introduced. This support system (IMMUNE) is represented by a Multi Agent Blackboard System, and has the dual characteristic of the distributed problem solving environments and yet reflecting the centralized viewpoint to process monitoring. In other words IMMUNE advocates autonomous problem solving (design) agents that is the necessary attribute of innovative design organizations and/or innovation networks; and at the same time it promotes coherence in the design system that is usually seen in centralized systems.

<p dir="ltr">Deep-space extraterrestrial missions require operating, supporting, and maintaining complex habitat systems at light minutes from Earth.</p><p dir="ltr">These habitation systems operate in harsh, unforgiving environments, will be sparsely crewed, and must be more autonomous than current space habitats, as communication delays will severely constrain Earth-based support.</p><p dir="ltr">Long-duration missions, limited knowledge of the extraterrestrial environment, and the need for self-sufficiency make these habitats vulnerable to a wide range of risks and failures, many of which are impossible to premeditate.</p><p dir="ltr">Therefore, it is necessary to design these systems to be resilient to faults and failures, thoughtfully designed to be situationally aware of their operational state and engage control mechanisms that maintain safe operations when migrating towards unsafe regions of operation.</p><p dir="ltr">Resilience-oriented design of such systems requires a holistic systems approach that represents the system's dynamic behavior, its control-oriented behaviors, and the interactions between them as it navigates through regions of safe and unsafe operations.</p><p dir="ltr">Only through this integrated approach can we fully understand how the system will behave under various conditions and design controls to prevent performance loss and ensure resilient operations.</p><p dir="ltr">Systems health management (SHM) is a key component for the resilience-oriented design of extraterrestrial habitats.</p><p dir="ltr">SHM capabilities enable intelligent autonomous control capabilities that can:</p><p dir="ltr">a) sense, diagnose, and isolate the root causes of anomalies,</p><p dir="ltr">b) predict how the system's behavior may evolve, and</p><p dir="ltr">c) select and execute recovery actions to restore system performance when appropriate.</p><p dir="ltr">Modern SHM technologies increasingly rely on intelligent autonomous control capabilities to manage system health and adapt behavior to maintain system performance.</p><p dir="ltr">This is achieved through complex nonlinear informational dependencies and control feedback loops that are difficult to design and verify using traditional risk assessment and resilience engineering methods.</p><p dir="ltr">This research contributes to enhancing the conceptual and preliminary design phases for developing resilient complex systems with embedded intelligent control-oriented behaviors.</p><p dir="ltr">It presents the required systems engineering tools and frameworks, enabling us to study the dynamic behavior of systems as they approach and recover from unsafe operations.</p><p dir="ltr">Further, it demonstrates how these tools and frameworks can quantify and gain insights into system resilience and support engineering decisions.</p><p dir="ltr">The work is contextualized within the broader systems engineering approach for designing complex, resilient extraterrestrial habitation systems.</p>