Value-Driven Analysis of New Paradigms in Space Architectures: An Ilities-Based Approach

Current commercial, civil, and military space architecture designs perform exquisitely and reliably. However, today’s architecture paradigms are also characterized by expensive launches, large and expensive high-performance spacecraft, long development cycles, and wide variations in ground architectures. While current assets provide high-quality services, and future assets are slated to improve performance within the same design frameworks, proposed future architectures may not be capitalizing on technology improvements, system innovations, or policy alternatives explored during the last two decades. This paper identifies five “trends” along which space architectures may develop, aimed at granting systems several “ilities,” such as resiliency, robustness, flexibility, scalability, and affordability. The trends examined include: commercialization of space, significant reductions in launch costs and the development of hybrid or reusable launch systems, development of on-orbit infrastructure and servicing, aggregation or disaggregation of orbital assets, and the automation and standardization of ground architectures. Further refinement of these key technological and system trends could result in major paradigm shifts in the development and fielding of space operations as well as lead to space architecture designs in the future that are radically different from those today. Within the framework of systems engineering ilities and risk management, this paper reviews current literature surrounding these new change trends and justifies their potential to cause significant paradigm shifts. By examining the work and research conducted so far through an ilities-based approach, systems engineers can more fully appreciate the value being offered by these trends

Open Innovation Generated Robotic Design and Solver Characteristics Dataset

This repository entry should be viewed in conjunction with the Data in Brief explainer "Linking solver characteristics, solving processes and solution attributes: A data explainer for an Open Innovation Generated Robotic Design Dataset" which documents the research design and implementation process and provides a detailed explanation of each data record, carefully characterizing potential limitations associated with research design choices.Between 2017 and 2020, a team of researchers from the George Washington University collaborated with NASA and Freelancer.com to design and launch the “Astrobee Challenge Series,” a large-scale field experiment that aimed to generate data to characterize the relationship between how a technical problem is formulated and who is able and willing to solve, and the quality of solutions they generate. The core experimental manipulation was of the architecture of the problem posed; the typical open innovation process was instrumented to collect unusually rich data but otherwise untouched. In all, 17 individual contests were run over a period of 12 months. Over the course of the challenge series, we tracked a population of 16,249 potential solvers, of which 6,219 initiated solving, and a subset of 147 unique solvers submitted 263 judgeable solutions. The resultant dataset is unique because it captures demographic and expertise data on the full population of potential solvers and links their activity to their solving processes and solution outcomes. Moreover, in addition to winning designs (the typical basis of analysis), it captures design outcomes for all submitted design artifacts allowing analysis of the variety of solutions to the same problem. This data should be useful for researchers interested in studying the design and innovation process, particularly those focused on novelty, variety, feasibility of solutions or expertise, diversity and capability of solvers.THIS DATASET IS ARCHIVED AT DANS/EASY, BUT NOT ACCESSIBLE HERE. TO VIEW A LIST OF FILES AND ACCESS THE FILES IN THIS DATASET CLICK ON THE DOI-LINK ABOV

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