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. doi:10.1016/j.ijsolstr.2018.01.035. https://resolver.caltech.edu/CaltechAUTHORS:20180221-091817099
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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 | |||||||||
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| 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, Space Solar Power Project | |||||||||
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| Subject Keywords: | Ultra-thin composites; Buckling; Post-buckling; Design charts; Data mining; Heteroscedastic Gaussian process; Evolutionary optimization | |||||||||
| DOI: | 10.1016/j.ijsolstr.2018.01.035 | |||||||||
| Record Number: | CaltechAUTHORS:20180221-091817099 | |||||||||
| Persistent URL: | https://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: | 15 Nov 2021 20:23 |
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