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

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

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

11 Scopus citations


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.

Original languageEnglish (US)
Article number113470
JournalComposite Structures
StatePublished - Mar 15 2021


  • Efficient multiscale modeling
  • Fiber waviness and nonuniform distribution
  • Image analysis
  • Uncertainty quantification and propagation

ASJC Scopus subject areas

  • Ceramics and Composites
  • Civil and Structural Engineering


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