Stochastic nonlinear analysis of unidirectional fiber composites using image-based microstructural uncertainty quantification. (15th March 2021)

• Record Type: Journal Article
• Authors: Huang, Tianyu; Gao, Jiaying; Sun, Qingping; Zeng, Danielle; Su, Xuming; Kam Liu, Wing; Chen, Wei
• Is Part Of: Composite structures. Volume 260(2021)
• Publication Date: 2021-03-15
• Subjects: Uncertainty quantification and propagation -- Image analysis -- Fiber waviness and nonuniform distribution -- Efficient multiscale modeling; Composite construction -- Periodicals; Composites -- Périodiques; 624.18
• DOI: 10.1016/j.compstruct.2020.113470
• Languages: English
• ISSNs: 0263-8223

Abstract

Highlights: Image-based stochastic characterization of fiber waviness and volume distribution. Stochastic reconstructions to simulate correlated non-Gaussian uncertainties. Uncertainty propagation with nonlinear materials via multiscale modeling. Stochastic nonlinear analysis of UD composites under compressive loading. Abstract: 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.