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Title: Uncertainty quantification in multiscale simulation of woven fiber composites

We report woven fiber composites have been increasingly employed as light-weight materials in aerospace, construction, and transportation industries due to their superior properties. These materials possess a hierarchical structure that necessitates the use of multiscale simulations in their modeling. To account for the inherent uncertainty in materials, such simulations must be integrated with statistical uncertainty quantification (UQ) and propagation (UP) methods. However, limited advancement has been made in this regard due to the significant computational costs and complexities in modeling spatially correlated structural variations coupled at different scales. In this work, a non-intrusive approach is proposed for multiscale UQ and UP to address these limitations. We introduce the top-down sampling method that allows to model non-stationary and continuous (but not differentiable) spatial variations of uncertainty sources by creating nested random fields (RFs) where the hyperparameters of an ensemble of RFs is characterized by yet another RF. We employ multi-response Gaussian RFs in top-down sampling and leverage statistical techniques (such as metamodeling and dimensionality reduction) to address the considerable computational costs of multiscale simulations. We apply our approach to quantify the uncertainty in a cured woven composite due to spatial variations of yarn angle, fiber volume fraction, and fiber misalignment angle.more » Finally, our results indicate that, even in linear analysis, the effect of uncertainty sources on the material’s response could be significant.« less
Authors:
ORCiD logo [1] ;  [1] ;  [1] ;  [1] ;  [1] ;  [2] ;  [2] ;  [2] ;  [2] ;  [1]
  1. Northwestern Univ., Evanston, IL (United States)
  2. Ford Motor Company, Dearborn, MI (United States)
Publication Date:
Grant/Contract Number:
EE0006867
Type:
Accepted Manuscript
Journal Name:
Computer Methods in Applied Mechanics and Engineering
Additional Journal Information:
Journal Volume: 338; Journal Issue: C; Journal ID: ISSN 0045-7825
Publisher:
Elsevier
Research Org:
Ford Motor Company, Dearborn, MI (United States)
Sponsoring Org:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V)
Country of Publication:
United States
Language:
English
Subject:
42 ENGINEERING; 97 MATHEMATICS AND COMPUTING
OSTI Identifier:
1504731

Bostanabad, Ramin, Liang, Biao, Gao, Jiaying, Liu, Wing Kam, Cao, Jian, Zeng, Danielle, Su, Xuming, Xu, Hongyi, Li, Yang, and Chen, Wei. Uncertainty quantification in multiscale simulation of woven fiber composites. United States: N. p., Web. doi:10.1016/j.cma.2018.04.024.
Bostanabad, Ramin, Liang, Biao, Gao, Jiaying, Liu, Wing Kam, Cao, Jian, Zeng, Danielle, Su, Xuming, Xu, Hongyi, Li, Yang, & Chen, Wei. Uncertainty quantification in multiscale simulation of woven fiber composites. United States. doi:10.1016/j.cma.2018.04.024.
Bostanabad, Ramin, Liang, Biao, Gao, Jiaying, Liu, Wing Kam, Cao, Jian, Zeng, Danielle, Su, Xuming, Xu, Hongyi, Li, Yang, and Chen, Wei. 2018. "Uncertainty quantification in multiscale simulation of woven fiber composites". United States. doi:10.1016/j.cma.2018.04.024.
@article{osti_1504731,
title = {Uncertainty quantification in multiscale simulation of woven fiber composites},
author = {Bostanabad, Ramin and Liang, Biao and Gao, Jiaying and Liu, Wing Kam and Cao, Jian and Zeng, Danielle and Su, Xuming and Xu, Hongyi and Li, Yang and Chen, Wei},
abstractNote = {We report woven fiber composites have been increasingly employed as light-weight materials in aerospace, construction, and transportation industries due to their superior properties. These materials possess a hierarchical structure that necessitates the use of multiscale simulations in their modeling. To account for the inherent uncertainty in materials, such simulations must be integrated with statistical uncertainty quantification (UQ) and propagation (UP) methods. However, limited advancement has been made in this regard due to the significant computational costs and complexities in modeling spatially correlated structural variations coupled at different scales. In this work, a non-intrusive approach is proposed for multiscale UQ and UP to address these limitations. We introduce the top-down sampling method that allows to model non-stationary and continuous (but not differentiable) spatial variations of uncertainty sources by creating nested random fields (RFs) where the hyperparameters of an ensemble of RFs is characterized by yet another RF. We employ multi-response Gaussian RFs in top-down sampling and leverage statistical techniques (such as metamodeling and dimensionality reduction) to address the considerable computational costs of multiscale simulations. We apply our approach to quantify the uncertainty in a cured woven composite due to spatial variations of yarn angle, fiber volume fraction, and fiber misalignment angle. Finally, our results indicate that, even in linear analysis, the effect of uncertainty sources on the material’s response could be significant.},
doi = {10.1016/j.cma.2018.04.024},
journal = {Computer Methods in Applied Mechanics and Engineering},
number = C,
volume = 338,
place = {United States},
year = {2018},
month = {5}
}