Convergent Set-Based Design in Integrated Analysis of Alternatives: Designing Engineered Resilient Systems

This thesis presents a comprehensive package for understanding and expanding set-based design quantification through the definition and demonstration of Convergent set-based design (SBD). Convergent SBD is a technique developed for the Engineered Resilient Systems program sponsored by the Department of Defense. Convergent SBD contributes a repeatable methodology with the goal of mathematically eliminating inefficient sets. The study of Convergent SBD led to the development of dominance identification criteria equations using comparison of statistical means. The demonstration of Convergent SBD also illustrates the effect of mission resilience in the tradespace and the impact mission resilience has on preference. Finally, Convergent SBD contributes to mathematical identification of the previously heuristic based set drivers and set modifiers and discusses additional decision analyst uses for this information. Presented as a complete thesis, Convergent SBD provides a foundational mathematical technique for eliminating sets and a method for converging to an efficient, affordable solution or group of solutions

Multi-Attribute Tradespace Exploration for Survivability

Multi-Attribute Tradespace Exploration for Survivability is a system design and analysis methodology that incorporates survivability considerations into the tradespace exploration process (i.e., a solution-generating and decision-making framework that applies decision theory to model-based design). During the concept generation phase of tradespace exploration, the methodology applies seventeen empirically validated survivability design principles spanning susceptibility reduction, vulnerability reduction, and resilience enhancement. During subsequent concept evaluation, the methodology adds value-based survivability metrics to traditional architectural evaluation criteria of mission utility and lifecycle cost. Applied to a satellite radar mission, the methodology allowed operational survivability to be statistically evaluated across representative distributions of naturally occurring disturbances in the space environment and for survivability to be incorporated as a decision factor earlier in the design process. Constellations in the illustrative example are shown to be the most survivable, mitigating disturbances architecturally, rather than through additive features.Massachusetts Institute of Technology (Systems Engineering Advancement Research Initiative (SEAri))Massachusetts Institute of Technology. Program on Emerging Technologie

Multi-attributes tradespace exploration for survivability: Application to satellite radar

Multi-Attribute Tradespace Exploration (MATE) for Survivability is introduced as a general methodology for survivability analysis and demonstrated through an application to a satellite radar system. MATE for Survivability applies decision theory to the parametric modeling of thousands of design alternatives across representative distributions of disturbance environments. Survivability considerations are incorporated into the existing MATE process (i.e., a solution-generating and decision-making framework that applies decision theory to model-based design) by applying empirically-validated survivability design principles and value-based survivability metrics to concept generation and concept evaluation activities, respectively. MATE for Survivability consists of eight iterative phases: (1) define system value proposition, (2) generate concepts, (3) specify disturbances, (4) apply survivability principles, (5) model baseline system performance, (6) model impact of disturbances on dynamic system performance, (7) apply survivability metrics, and (8) select designs for further analysis. The application of MATE for Survivability to satellite radar demonstrates the importance of incorporating survivability considerations into conceptual design for identifying inherently survivable architectures that efficiently balance competing performance metrics of lifecycle cost, mission utility, and operational survivability

Quantitative Set-Based Design for Complex System Development

This dissertation comprises a body of research facilitating decision-making and complex system development with quantitative set-based design (SBD). SBD is concurrent product development methodology, which develops and analyzes many design alternatives for longer time periods enabling design maturation and uncertainty reduction. SBD improves design space exploration, facilitating the identification of resilient and affordable systems. The literature contains numerous qualitative descriptions and quantitative methodologies describing limited aspects of the SBD process. However, there exist no methodologies enabling the quantitative management of SBD programs throughout the entire product development cycle. This research addresses this knowledge gap by developing the process framework and supporting methodologies guiding product development from initial system concepts to a final design solution. This research provides several new research contributions. First, we provide a comprehensive SBD state-of-practice assessment identifying key knowledge and methodology gaps. Second, we demonstrate the physical implementation of the integrated analytics framework in a model-based engineering environment. Third, we develop a quantitative methodology enabling program management decision making in SBD. Fourth, we describe a supporting uncertainty reduction methodology using multiobjective value of information analysis to assess design set maturity and higher-resolution model usefulness. Finally, we describe a quantitative SBD process framework enabling sequential design maturation and uncertainty reduction decisions. Using an unmanned aerial vehicle case study, we demonstrate our methodology’s ability to resolve uncertainty and converge a complex design space onto a set of resilient and affordable design solutions

Optimizing UAS Mission Training Needs Through Tradespace Analysis

The Gray Eagle unmanned aircraft systems (UAS) training program requires the reallocation of multiple fully operational UAS from the operational environment to facilitate training. The UAS Project Management Office (PM UAS) is concerned that this practice lacks efficiency. This study sought to: (1) conduct a comprehensive analysis for resource optimization with respect to achieving essential training tasks across multiple UAS, (2) conduct comprehensive cost-benefit analysis to assess the value of allocating a full-time and Gray Eagle platforms to accomplish training versus part-task trainers, and (3) define and quantify measures of performance and effectiveness. To achieve these objectives, this study implemented a tradespace analysis methodology to produce a discrete-event simulation model and a resource optimization tool. The impacts of this project will result in substantial cost savings per fiscal year, allow the client to forecast the resource needs of the organization effectively, and allow for the proper allocation of these resources

Computational Tradespace Exploration, Analysis, and Decision-Making: A Proposed Framework for Organizational Self-Assessment

The ability to assess technical feasibility, project risk, technical readiness, and realistic performance expectations in early-phase conceptual design is a challenging mission-critical task for large procurement projects. At present, there is not a well-defined framework for evaluating current practices of organizations performing computational trade studies. One such organization is the US Army Ground Vehicle Systems Center (GVSC). When defining requirements and priorities for the next-generation autonomy-enabled ground vehicle system, GVSC is faced with the challenge of an increasingly complex programmatic tradespace due to emerging complexities of ground vehicle systems. This thesis aims to document and evaluate tradespace processes, methods, and tools within GVSC. A systematic review of the literature was conducted to investigate existing gaps, limitations, and potential growth opportunities related to tradespace activities reflecting the greater body of knowledge observed in the literature. Following this review, an interview-based study was developed through which a series of interviews with GVSC personnel was conducted and subsequently benchmarked against the baseline established in the literature. In addition to characterizing the current practices of tradespace exploration and analysis within GVSC, the analysis of the collected interview data revealed current capability gaps, areas of excellence, and potential avenues for improvement within GVSC. Through this thesis, other organizations can perform similar self-assessments to improve internal capabilities with respect to tradespace studies

System Qualities Ontology, Tradespace and Affordability (SQOTA) Project – Phase 4

This task was proposed and established as a result of a pair of 2012 workshops sponsored by the DoD Engineered Resilient Systems technology priority area and by the SERC. The workshops focused on how best to strengthen DoD’s capabilities in dealing with its systems’ non-functional requirements, often also called system qualities, properties, levels of service, and –ilities. The term –ilities was often used during the workshops, and became the title of the resulting SERC research task: “ilities Tradespace and Affordability Project (iTAP).” As the project progressed, the term “ilities” often became a source of confusion, as in “Do your results include considerations of safety, security, resilience, etc., which don’t have “ility” in their names?” Also, as our ontology, methods, processes, and tools became of interest across the DoD and across international and standards communities, we found that the term “System Qualities” was most often used. As a result, we are changing the name of the project to “System Qualities Ontology, Tradespace, and Affordability (SQOTA).” Some of this year’s university reports still refer to the project as “iTAP.”This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant of Defense for Research and Engineering (ASD(R&E)) under Contract HQ0034-13-D-0004.This material is based upon work supported, in whole or in part, by the U.S. Department of Defense through the Office of the Assistant of Defense for Research and Engineering (ASD(R&E)) under Contract HQ0034-13-D-0004

Technology Decisions Under Architectural Uncertainty: Informing Investment Decisions Through Tradespace Exploration

Although NASA has yet to choose an architecture for human spaceflight beyond Earth orbit, they must pursue near-term investment in the enabling technologies that will be required for these future systems. Given this architectural uncertainty, it is difficult to define the value proposition of technology investments. This paper proposes a method for evaluating technology across a tradespace defined by architectural decisions. Main effects analysis is taken from design of experiments to quantify the influence that a technology has on the system being considered. This analysis also identifies couplings between technologies that are mutually exclusive or mutually beneficial. This method is applied to the architecture tradespace of transportation for future human exploration at Mars with a set of possible propellant, propulsion, and aerobraking technologies. The paper demonstrates that the evaluation of technologies against an individual reference architecture is flawed when the range of architectures being pursued remains diverse. Furthermore, it is shown that comparisons between fuzzy Pareto optimal architectures and heavily dominated architectures will distort the evaluated benefit of a technology. The resulting tradespace can be structured as the sequence in which technology decisions should be made, in order of their impact on the tradespace and their coupling to other decisions.United States. National Aeronautics and Space Administration (Massachusetts Institute of Technology Research Grant