期刊
INTERNATIONAL JOURNAL OF SOLIDS AND STRUCTURES
卷 139, 期 -, 页码 174-188出版社
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.ijsolstr.2018.01.035
关键词
Ultra-thin composites; Buckling; Post-buckling; Design charts; Data mining; Heteroscedastic Gaussian process; Evolutionary optimization
类别
资金
- Northrop Grumman Corporation
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. (C) 2018 Elsevier Ltd. All rights reserved.
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