Stochastic nonlinear analysis of unidirectional fiber composites using image-based microstructural uncertainty quantification

Huang, Tianyu; Gao, Jiaying; Sun, Qingping; Zeng, Danielle; Su, Xuming; Kam Liu, Wing; Chen, Wei

doi:10.1016/j.compstruct.2020.113470

Title: Stochastic nonlinear analysis of unidirectional fiber composites using image-based microstructural uncertainty quantification

Journal Article · Fri Dec 18 00:00:00 EST 2020 · Composite Structures

DOI:https://doi.org/10.1016/j.compstruct.2020.113470· OSTI ID:1848462

Huang, Tianyu ^[1]; Gao, Jiaying ^[1]; Sun, Qingping ^[2]; Zeng, Danielle ^[3]; Su, Xuming ^[3]; Kam Liu, Wing ^[1]; Chen, Wei ^[1]

Northwestern Univ., Evanston, IL (United States). Dept. of Mechanical Engineering
Nanjing Univ. of Aeronautics and Astronautics, Nanjing (China). College of Energy and Power Engineering. Jiangsu Province Key Lab. of Aerospace Power System
Ford Motor Company, Detroit, MI (United States). Research and Advanced Engineering

Here, we present a data-driven nonlinear uncertainty quantification and propagation framework to study the microstructure-induced stochastic performance of unidirectional (UD) carbon fiber reinforced polymer (CFRP) composites. The proposed approach integrates (1) microscopic image characterization, (2) stochastic microstructure reconstruction, and (3) efficient multiscale finite element simulations enabled by self-consistent clustering (SCA) analysis. To model the complex microstructural variability, the proposed UQ methods take the non-Gaussian uncertainty sources into account through a distribution-free sampling approach leveraging nonparametric and asymptotic statistical tools. A hierarchical conditional sampling strategy enables the simultaneous sampling of multiple sources of uncertainties. Our approach provides insights into the impact of microstructural variabilities, which are shown to have an increasing impact on the nonlinear responses of UD CFRP parts under progressive compression loading and ultimately on the failure rate over time. We discover that before CFRP parts start to fail, a characteristic time period emerges with distinctive uncertainty distributions specific to the microstructure variability and the probability of failure. Identifying the failure time period is crucial to the reliability prediction, which is an essential component of CFRP design.

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

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE); US Air Force Office of Scientific Research (AFOSR)

Grant/Contract Number:: EE0006867; FA9550-18-1-0381; NIST 70NANB19H005

OSTI ID:: 1848462

Alternate ID(s):: OSTI ID: 1777539

Journal Information:: Composite Structures, Vol. 260, Issue C; ISSN 0263-8223

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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