Multiscale simulation of fiber composites with spatially varying uncertainties

Computational analyses of emerging materials such as fibrous composites rely on multiscale simulations. To account for the inherent uncertainty in these 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 nonintrusive approach is proposed for multiscale UQ and UP to address these limitations. We introduce the top-down sampling method that allows to model nonstationary and continuous 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 multiresponse Gaussian RFs in top-down sampling and leverage statistical techniques to address the considerable computational costs of multiscale simulations. We apply our approach to quantify the uncertainty in a cured woven prepreg due to spatial variations of fiber volume fraction and misalignment, fiber and matrix modulus, and yarns' architecture (i.e., angle, height, and spacing). Our results indicate that, even in linear analysis, the effect of uncertainty sources on material's response could be significant.