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dc.contributor.author
Guariniello, Cesare
dc.contributor.author
Marsh, Thomas B.
dc.contributor.author
Diggelmann, Thomas
dc.contributor.author
DeLaurentis, Daniel A.
dc.date.accessioned
2022-01-26T12:25:26Z
dc.date.available
2021-09-10T08:28:23Z
dc.date.available
2022-01-26T12:25:26Z
dc.date.issued
2021
dc.identifier.isbn
978-1-7281-7436-5
en_US
dc.identifier.isbn
978-1-7281-7437-2
en_US
dc.identifier.isbn
978-1-7281-2734-7
en_US
dc.identifier.other
10.1109/AERO50100.2021.9438405
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/505118.1
dc.identifier.uri
http://hdl.handle.net/20.500.11850/505118
dc.description.abstract
Identifying the optimal technologies required for space architectures to fulfill needs of large and complex missions is difficult for three primary reasons. First, interactions between the involved systems and technologies cannot be easily identified. Second, the diversity of participating stakeholders and involved disciplines, and the multiple sources of information make the trade space large and difficult to navigate and analyze. Third, continuous progress in the field leads to the development of new technologies, for which data and models are likely to be scarce, thus contributing to increase the uncertainty. System-of-Systems methodologies can address these. Previous research focused on modeling and analysis of interactions between systems in the operational domain, to identify risks and criticalities. We apply similar methodologies to interactions between technologies in the developmental domain, with the goal of identifying potential bottlenecks and risks in the schedule of space missions. This approach supports decision-making for prioritization of technologies, accounting for technological needs of the mission, uncertainty in development time, potential delays, and changes in policies. A particular innovation is the design and use of artificial intelligent agents, that can recognize information coming from multiple sources, including documents in plain English, evaluate the relevance of documents for which they are trained, and recognize useful information in publications which are not strongly connected with the specific technology on which the agent has been trained. The results include development schedule of different architectures based on different technology prioritization and risk acceptance, analysis of the impact of delays and of changes in policies, analysis of flexibility of mission sets and a case study of Cryogenic Fluid Management technologies in an Artemis-based mission to the Moon is used to demonstrate the application of the methodology. ©2021 IEEE
en_US
dc.language.iso
en
en_US
dc.publisher
IEEE
en_US
dc.title
System-of-Systems Methods for Technology Assessment and Prioritization for Space Architectures
en_US
dc.type
Conference Paper
dc.date.published
2021-06-07
ethz.book.title
2021 IEEE Aerospace Conference
en_US
ethz.pages.start
9438405
en_US
ethz.size
13 p.
en_US
ethz.event
2021 IEEE Aerospace Conference
en_US
ethz.event.location
Online
en_US
ethz.event.date
March 6-13, 2021
en_US
ethz.identifier.scopus
ethz.publication.place
Piscataway, NJ
en_US
ethz.publication.status
published
en_US
ethz.date.deposited
2021-08-06T02:48:59Z
ethz.source
SCOPUS
ethz.source
WOS
ethz.eth
yes
en_US
ethz.availability
Metadata only
en_US
ethz.rosetta.installDate
2021-09-10T08:28:31Z
ethz.rosetta.lastUpdated
2022-03-29T18:00:07Z
ethz.rosetta.versionExported
true
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/499760
dc.identifier.olduri
http://hdl.handle.net/20.500.11850/504834
ethz.COinS
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