Uncertainty quantification in multiscale simulation of woven fiber composites

Bostanabad, Ramin; Liang, Biao; Gao, Jiaying; Liu, Wing Kam; Cao, Jian; Zeng, Danielle; Su, Xuming; Xu, Hongyi; Li, Yang; Chen, Wei

doi:10.1016/j.cma.2018.04.024

Title: Uncertainty quantification in multiscale simulation of woven fiber composites

Journal Article · Sat May 05 00:00:00 EDT 2018 · Computer Methods in Applied Mechanics and Engineering

DOI:https://doi.org/10.1016/j.cma.2018.04.024· OSTI ID:1504731

^[1]; Liang, Biao ^[1]; Gao, Jiaying ^[1]; Liu, Wing Kam ^[1]; Cao, Jian ^[1]; Zeng, Danielle ^[2]; Su, Xuming ^[2]; Xu, Hongyi ^[2]; Li, Yang ^[2]; Chen, Wei ^[1]

Northwestern Univ., Evanston, IL (United States)
Ford Motor Company, Dearborn, MI (United States)

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.

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Research Organization:: Ford Motor Company, Detroit, MI (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Vehicle Technologies Office (EE-3V); USDOE

Grant/Contract Number:: EE0006867

OSTI ID:: 1504731

Alternate ID(s):: OSTI ID: 1601508

Journal Information:: Computer Methods in Applied Mechanics and Engineering, Vol. 338, Issue C; ISSN 0045-7825

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 65 works

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