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Design of ultra-thin composite deployable shell structures through machine learning

Bessa, Miguel A. and Pellegrino, Sergio (2017) Design of ultra-thin composite deployable shell structures through machine learning. In: Proceedings of the IASS Annual Symposium 2017. HafenCity University , Hamburg, pp. 1-8. https://resolver.caltech.edu/CaltechAUTHORS:20191114-160021163

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Abstract

A data-driven computational framework is applied for the design of optimal ultra-thin Triangular Rollable and Collapsible (TRAC) carbon fiber booms. High-fidelity computational analyses of a large number of geometries are used to build a database. This database is then analyzed by machine learning to construct design charts that are shown to effectively guide the design of the ultra-thin deployable structure. The computational strategy discussed herein is general and can be applied to different problems in structural and materials design, with the potential of finding relevant designs within high-dimensional spaces.


Item Type:Book Section
Related URLs:
URLURL TypeDescription
https://structurae.net/en/literature/conference-paper/design-of-ultra-thin-composite-deployable-shell-structures-through-machine-learningPublisherConference Abstract
ORCID:
AuthorORCID
Pellegrino, Sergio0000-0001-9373-3278
Additional Information:© 2017 by Miguel A. Bessa and Sergio Pellegrino. Published by the International Association for Shell and Spatial Structures (IASS) with permission. The authors acknowledge financial support from the Northrop Grumman Corporation. Comments of an anonymous reviewer are gratefully acknowledged.
Group:GALCIT, Space Solar Power Project
Funders:
Funding AgencyGrant Number
Northrop Grumman Corporation UNSPECIFIED
Subject Keywords:Buckling, ultra-thin composite shells, machine learning, data mining, data-driven computational framework, design
Record Number:CaltechAUTHORS:20191114-160021163
Persistent URL:https://resolver.caltech.edu/CaltechAUTHORS:20191114-160021163
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:99856
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:15 Nov 2019 03:02
Last Modified:15 Nov 2019 03:02

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