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Completed TRL 1 (started at 1, targeting 3)
Eureka represents a commercially viable technology advancement for systems engineering that will also benefit upcoming Marshall Space Flight Center programs and projects. Eureka is a model based systems engineering approach for complex systems of systems decisions. This process will enable evaluation of space transportation architectures utilizing Artificial Intelligence (AI) to glean technology readiness level (TRL) based on the actual components of that architecture and allowing system level analysis tools to parametrically vary cost, schedule, operational dependencies, and risk assessments as a function of TRL. NASA has used TRL as a measurement for fielding technology since the 1970s and government and commercial industries use modified TRL definitions to perform technological assessments. However, with the emergence of more complex systems and system of systems, it has been increasingly recognized by industry and government analysts and recorded in technical journals and presentations that TRL assessments have limitations, especially when considering the integration of complex systems and the vast body of knowledge accessible today.
With Eureka, we propose to specify, plan and develop, a complete but modular solution that will establish a Digital Thread (DT) architecture and knowledge framework that can be deployed for space architectures (full lifecycle) for the planning, development, production, sustainment and disposal of a system. It will be configurable for any research groupings including decentralized teams, internal NASA only and extensible to public data sources and projects.
The technical goal is to capture documented information and infuse that information into the System Engineering process to enhance the decision making process. Topic mapping provides an ability to group or cluster documents and paragraphs into categories that enable creation of enriched index/linking for DT. Our Digital Thread/Knowledge extension (DT/Ke) solution will provide the infrastructure to support the building of information containers and Knowledge Networks.
Eureka is focused on the parametric capability that will benefit space architecture evaluations of complex systems of systems and quickly provide:
Eureka is a Model Based Systems Engineering (MBSE) approach that uses computer models as an integral part of the technical baseline. The digital thread linking models that have been historically separate is enhanced by the use of intelligent agent systems in the earliest phases for knowledge capture and extraction that allows for expert guidance in system of systems design, analysis of alternatives, and risk-informed decision-making leading to efficient requirement definition.
Our approach to enhancing the functionality of Eureka engages disparate teams across academia. By allowing other partner institutions to imagine and define new space architectures, the Eureka team will be independently tested to indicate best of class architectural concepts based on the limited information defined by four distinct teams. Eureka will advance the state of MBSE in a platform-independent manner. Our intent is to focus on the tools, establish their credibility with disparate data sources, and continue to enhance the applicability of the MBSE environment.
The evaluation of space transportation architectures involves many complex decisions on elements and systems. To enhance system of systems decision-making and mitigate the risk of suboptimal element decisions Eureka employs Artificial Intelligence (AI) to gleam technology readiness level (TRL) based on the actual components of that architecture and allowing system level analysis tools to parametrically vary cost, schedule, operational dependencies, and risk assessments as a function of TRL. NASA has used TRL as a measurement for fielding technology since the 1970s and government and commercial industries use modified TRL definitions to perform technological assessments. However, with the emergence of more complex systems and system of systems, it has been increasingly recognized that TRL assessments have limitations, especially when considering the integration of complex systems and the vast body of knowledge accessible today. Eureka is focused on the parametric capability that will benefit space architecture evaluations of complex systems of systems and quickly provide:
This proposal combines aspects of two previous distinct Cooperative Agreement Notice (CAN) partners. VCSI recognized an innovative connection between the ai-one/ISC “NASA Topic- Mapper” effort and the Purdue University “Characterizing Mission and System Architectures via Analysis of Operation and Developmental Interdependencies” effort. The decomposition of the system elements in the overall architecture and cross-referencing to the 15 NASA Technology Area roadmaps befit the evaluation of the TRL of each element, which, in turn contributes to the speed and accuracy of the Purdue assessment techniques.
Cognitive agents, developed by ai-one/ISC, search pertinent databases to identify and analyze the research applicable to each of the 354 Technology Area Breakdown Structure (TABS) elements. The metadata associated with each agent combined with the amount, quality and trending of the research provides for a quick assessment of an initial TRL of each technology area for each element. The metadata also provides for the identification of the key NASA subject matter experts for each TABS facilitating validation of the TRL assessment where needed. These agents are autonomous software that learn, adapt, and react to the environments, ideas, and results, from databases to be quickly assembled and documented.
Introducing the data recovered by ai-one and validated through Subject Matter Experts (SME) the Purdue University Analytic Work Bench (AWB) for modeling and simulation can then quickly and more accurately determine an iterative architecture solution set or sets. The AWB includes the Systems Operational Dependency Analysis (SODA) and the Systems Development Dependency Analysis (SDDA) tools SODA is a method to analyze the result of the possible cascading effect of dependencies between systems on the overall operability, in case of disruptions, and to support design decision-making. This provides a parametric model of the behavior of the system. SDDA is a method to assess the impact of partial developmental dependencies between components in a complex system, or between systems in a system of systems. Subject Matter Experts (SME) can assist in the parametric ranges and associated impacts to the cost, risk, operational dependencies, and schedule components of the architecture. The data can then be simplified to a dashboard depiction for quick analysis.
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