Supporting Space Systems Design via Systems Dependency Analysis Methodology

The increasing size and complexity of space systems and space missions pose severe challenges to space systems engineers. When complex systems and Systems-of-Systems are involved, the behavior of the whole entity is not only due to that of the individual systems involved but also to the interactions and dependencies between the systems. Dependencies can be varied and complex, and designers usually do not perform analysis of the impact of dependencies at the level of complex systems, or this analysis involves excessive computational cost, or occurs at a later stage of the design process, after designers have already set detailed requirements, following a bottom-up approach. While classical systems engineering attempts to integrate the perspectives involved across the variety of engineering disciplines and the objectives of multiple stakeholders, there is still a need for more effective tools and methods capable to identify, analyze and quantify properties of the complex system as a whole and to model explicitly the effect of some of the features that characterize complex systems. This research describes the development and usage of Systems Operational Dependency Analysis and Systems Developmental Dependency Analysis, two methods based on parametric models of the behavior of complex systems, one in the operational domain and one in the developmental domain. The parameters of the developed models have intuitive meaning, are usable with subjective and quantitative data alike, and give direct insight into the causes of observed, and possibly emergent, behavior. The approach proposed in this dissertation combines models of one-to-one dependencies among systems and between systems and capabilities, to analyze and evaluate the impact of failures or delays on the outcome of the whole complex system. The analysis accounts for cascading effects, partial operational failures, multiple failures or delays, and partial developmental dependencies. The user of these methods can assess the behavior of each system based on its internal status and on the topology of its dependencies on systems connected to it. Designers and decision makers can therefore quickly analyze and explore the behavior of complex systems and evaluate different architectures under various working conditions. The methods support educated decision making both in the design and in the update process of systems architecture, reducing the need to execute extensive simulations. In particular, in the phase of concept generation and selection, the information given by the methods can be used to identify promising architectures to be further tested and improved, while discarding architectures that do not show the required level of global features. The methods, when used in conjunction with appropriate metrics, also allow for improved reliability and risk analysis, as well as for automatic scheduling and re-scheduling based on the features of the dependencies and on the accepted level of risk. This dissertation illustrates the use of the two methods in sample aerospace applications, both in the operational and in the developmental domain. The applications show how to use the developed methodology to evaluate the impact of failures, assess the criticality of systems, quantify metrics of interest, quantify the impact of delays, support informed decision making when scheduling the development of systems and evaluate the achievement of partial capabilities. A larger, well-framed case study illustrates how the Systems Operational Dependency Analysis method and the Systems Developmental Dependency Analysis method can support analysis and decision making, at the mid and high level, in the design process of architectures for the exploration of Mars. The case study also shows how the methods do not replace the classical systems engineering methodologies, but support and improve them

Space Architecture Assessment Using System-of-Systems Methodologies

As technologies in the space exploration community are further developed, mission complexity and the associated risks have become greater. Dozens of complicated system interactions may result in unexpected, potentially dangerous emergent behaviors. Early efforts are underway by NASA to map potential system architectures (collections of systems which fulfill design requirements) for future human space exploration missions. However, current mission complexity requires the determination of emergent behaviors, as well as time requirements, and safety levels of complicated space exploration architectures, which current analysis methods in use cannot address. To that end, a newer technique has been developed—System Operability Dependency Analysis (SODA). This technique uses a combination of expert input and past data analysis to create a model of system interactions, to properly complete the required study. By gathering a broad variety of data and opinion through literature survey and interaction with subject matter experts, and modeling interactions between systems, obtaining estimations for the feasibility and features of a variety of architectural variations becomes possible. This study compares a small set of architectures/variations to determine which best meet the requirement metrics designated by the user. The resultant data includes sets of feasibility data and specialized data plots which denote the relative feasibility of each architecture. The knowledge learned from this study is intended as an initial guide for the development of future human space exploration missions

A System-of-Systems Approach to Enterprise Analytics Design: Acquisition Support in the Age of Machine Learning and Artificial Intelligence

Excerpt from the Proceedings of the Nineteenth Annual Acquisition Research SymposiumSystem-of-Systems (SoS) capability emerges from the collaboration of multiple systems, which are acquired from independent organizations. Even though the systems contribute to and benefit from the larger SoS, the data analytics and decision-making about the independent system is rarely shared across the SoS stakeholders. The objective of the research presented in this paper is to identify how the sharing of datasets and the corresponding analytics among SoS stakeholders can lead to an improved SoS capability. Our objective is to characterize how appropriate use of data sets may lead to deployment of different predictive (predicting an outcome from data) and prescriptive (determining a preferred strategy) analytics and lead to better decision outcomes at the SoS level. We build and demonstrate a framework for this objective, based on extensive literature review, which utilizes appropriate predictive and prescriptive methodologies for SoS analysis. Additionally, we propose to utilize machine learning techniques to predict the achievable SoS capability and identify sources of uncertainty derived by sharing partial datasets. A case study demonstrates the use of the framework and prospects for future improvements.Approved for public release; distribution is unlimited

A System-of-Systems Approach to Enterprise Analytics Design: Acquisition Support in the Age of Machine Learning and Artificial Intelligence

Symposium PresentationApproved for public release; distribution is unlimited

A System-of-Systems Approach to Enterprise Analytics Design: Acquisition Support in the Age of Machine Learning and Artificial Intelligence

Acquisition Research Program Sponsored Report SeriesSponsored Acquisition Research & Technical ReportsSystem-of-Systems (SoS) capability emerges from the collaboration of multiple systems, which are acquired from independent organizations. Even though the systems contribute to and benefit from the larger SoS, the data analytics and decision-making about the independent system is rarely shared across the SoS stakeholders. The objective of this work is to identify how the sharing of datasets and the corresponding analytics among SoS stakeholders can lead to an improved SoS capability. Our objective is to characterize how the sharing of connected data sets may lead to deployment of different predictive (predicting an outcome from data) and prescriptive (determining a preferred strategy) analytics and lead to better decision outcomes at the SoS level. We build and demonstrate a framework for this objective based on extensive literature review and generating appropriate predictive and prescriptive methodologies that can be used for SoS analysis: Additionally, we propose to utilize machine learning techniques to predict the SoS capability achievable by sharing pertinent datasets and to prescribe the information links between systems to enable this sharing. Two case studies demonstrate the use of the framework and prospects for meeting the objective. Highlights of our study are summarized next.Approved for public release; distribution is unlimited.Approved for public release; distribution is unlimited

A System-of-Systems Approach to Enterprise Analytics Design: Acquisition Support in the Age of Machine Learning and Artificial Intelligence

Excerpt from the Proceedings of the Nineteenth Annual Acquisition Research SymposiumSystem-of-Systems (SoS) capability emerges from the collaboration of multiple systems, which are acquired from independent organizations. Even though the systems contribute to and benefit from the larger SoS, the data analytics and decision-making about the independent system is rarely shared across the SoS stakeholders. The objective of the research presented in this paper is to identify how the sharing of datasets and the corresponding analytics among SoS stakeholders can lead to an improved SoS capability. Our objective is to characterize how appropriate use of data sets may lead to deployment of different predictive (predicting an outcome from data) and prescriptive (determining a preferred strategy) analytics and lead to better decision outcomes at the SoS level. We build and demonstrate a framework for this objective, based on extensive literature review, which utilizes appropriate predictive and prescriptive methodologies for SoS analysis. Additionally, we propose to utilize machine learning techniques to predict the achievable SoS capability and identify sources of uncertainty derived by sharing partial datasets. A case study demonstrates the use of the framework and prospects for future improvements.Approved for public release; distribution is unlimited

System-of-Systems Tools for the Analysis of Technological Choices in Space Propulsion

Difficulties in space mission architecture design arise from many factors. Performance, cost, and risk constraints become less obvious due to complex interactions between the systems involved in the mission; decisions regarding long-term goals can heavily impact technological choices for short-term parts of the mission, while conversely decisions in the near future will impact the whole flexibility of long-term plans. Furthermore, the space community is broadening its borders, and space agencies from different countries are collaborating with industry and commercial partners towards large-scale endeavors. This paradigm shift is prompting the development of non-traditional approaches to the design of space missions. This paper reports the results of the first year of a continuing collaboration of the authors to develop and demonstrate System-of-System engineering methodologies for the deep analysis of dependencies and synthesis of robust architectures in exploration mission contexts. We present the procedure that we followed to develop and apply our methodology, obstacles found, steps taken to improve the methods based on the needs of experts and decision makers, required data for the analysis, and results produced by our holistic analysis. In particular, we focus on the analysis of technological choices for space propulsion for a generic cislunar mission, including both complex interactions between subsystems in different type of propulsion and availability of different providers. We identify critical systems and sets of systems based on cascading effects of performance degradation, assessment of the robustness of different designs in the operational domain, and simultaneous analysis of schedule dependencies between the constituent systems

Autonomous Capture of Non-Cooperative Spacecraft with a Space Free-Flyer

This work deals with the problem of performing rendezvous and capture of a non-cooperative spacecraft by means of a space free-flyer, i.e. a satellite base equipped with a robotic manipulator. Though this kind of manoeuvres addresses the solving of relevant existing problems such as debris removal, satellite servicing, orbit changing, only few spaceborne experiments have been conducted, all keeping strong working hypothesis. In this work, a few techniques and strategies to obtain more realistic algorithms, taking into account relative motion and computational load, are presented. An orbit and attitude dynamics simulator has been developed to experiment the proposed strategies

System-of-Systems Acquisition Analytics Using Machine Learning Techniques

Panel #11: Analytics in Acquisition ManagementAcquisition Research: Creating Synergy for Informed Change. 17th Annual Acquisition Research Symposiu