CaltechAUTHORS
  A Caltech Library Service

Design of ultra-thin shell structures in the stochastic post-buckling range using Bayesian machine learning and optimization

Bessa, M. A. and Pellegrino, S. (2018) Design of ultra-thin shell structures in the stochastic post-buckling range using Bayesian machine learning and optimization. International Journal of Solids and Structures, 139-140 . pp. 174-188. ISSN 0020-7683. http://resolver.caltech.edu/CaltechAUTHORS:20180221-091817099

Full text is not posted in this repository. Consult Related URLs below.

Use this Persistent URL to link to this item: http://resolver.caltech.edu/CaltechAUTHORS:20180221-091817099

Abstract

A data-driven computational framework combining Bayesian regression for imperfection-sensitive quantities of interest, uncertainty quantification and multi-objective optimization is developed for the design of complex structures. The framework is used to design ultra-thin carbon fiber deployable shells subjected to two bending conditions. Significant increases in the ultimate buckling loads are shown to be possible, with potential gains on the order of 100% as compared to a previously proposed design. The key to this result is the existence of a large load reserve capability after the initial bifurcation point and well into the post-buckling range that can be effectively explored by the data-driven approach. The computational strategy here presented 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:Article
Related URLs:
URLURL TypeDescription
https://doi.org/10.1016/j.ijsolstr.2018.01.035DOIArticle
https://www.sciencedirect.com/science/article/pii/S0020768318300441PublisherArticle
ORCID:
AuthorORCID
Pellegrino, S.0000-0001-9373-3278
Additional Information:© 2018 Elsevier Ltd. Received 31 July 2017, Revised 31 December 2017, Accepted 25 January 2018, Available online 7 February 2018. Financial support from the Northrop Grumman Corporation is gratefully acknowledged.
Group:GALCIT
Funders:
Funding AgencyGrant Number
Northrop Grumman CorporationUNSPECIFIED
Subject Keywords:Ultra-thin composites; Buckling; Post-buckling; Design charts; Data mining; Heteroscedastic Gaussian process; Evolutionary optimization
Record Number:CaltechAUTHORS:20180221-091817099
Persistent URL:http://resolver.caltech.edu/CaltechAUTHORS:20180221-091817099
Official Citation:M.A. Bessa, S. Pellegrino, Design of ultra-thin shell structures in the stochastic post-buckling range using Bayesian machine learning and optimization, International Journal of Solids and Structures, Volumes 139–140, 2018, Pages 174-188, ISSN 0020-7683, https://doi.org/10.1016/j.ijsolstr.2018.01.035. (http://www.sciencedirect.com/science/article/pii/S0020768318300441)
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:84899
Collection:CaltechAUTHORS
Deposited By: Tony Diaz
Deposited On:21 Feb 2018 17:52
Last Modified:26 Apr 2018 16:11

Repository Staff Only: item control page