LAUSR

The processing and mechanical properties of two-dimensional woven carbon fibre-reinforced composites depend directly on the internal geometrical architecture, which presents typical multiscale nature, while the multiscale structures possess inevitable geometric variabilities during the manufacturing process. This work presents a stochastic multiscale geometric modelling framework containing two developed algorithms to facilitate reconstructing statistically equivalent structures on microscale and mesoscale of two-dimensional woven carbon fibre-reinforced composite considering internal geometric variability. The sequential random perturbation algorithm is proposed to realize the random distribution nature of fibres inside yarns on microscale by sequential smart movements of initial regular distributed fibres. Then, an algorithm based on Gaussian random sequence is developed to characterize the internal variabilities of yarn path and shape on mesoscale via reconstructing correlated stochastic deviations along yarns. The proposed modelling framework effectively reconstructs the geometric models of random microstructure and mesostructure, which is convenient to be implemented into computational micromechanical analysis on both scales, serving as the foundation of the numerical calculation of the multiscale processing and mechanical properties of the studied composite.