A Bayesian Framework for Assessing the Strength Distribution of Composite Structures with Random Defects

ue to their very good strength to weight ratio, composite materials make up over 50% of recent aircraft constructions. The materials are manufactured from very thin fibrous layers (∼ 10 −4 m) and even thinner resin interfaces (∼ 10 −5 m). To achieve the required strength, a particular layup sequence of orientations of the anisotropic fibrous layers is used. During manufacturing, small localised defects in the form of misaligned fibrous layers can occur in composite materials. In this talk I will introduce a Markov Chain Monte Carlo algorithm, which derives the stochastic distri- bution of such wrinkle defects from image data. The approach significantly reduces uncertainty in the parameterization of stochastic numerical studies on the effects of defects. The defects are parameterized by stochastic random fields defined using Karhunen-Loéve modes inferred from misalignment data extracted from B-Scan data using a modified version of Multiple Field Image Analysis [2]. Further, I will discuss using the GENEO coarse space as a surrogate model for the fine-scale displacement and stress fields. For the coarse space construction, GENEO computes generalised eigenvectors of the local stiffness matrices on the overlapping subdomains and builds an approximate coarse space by combining the smallest energy eigenvectors on each subdomain via a partition of unity [1].     «

ue to their very good strength to weight ratio, composite materials make up over 50% of recent aircraft constructions. The materials are manufactured from very thin fibrous layers (∼ 10 −4 m) and even thinner resin interfaces (∼ 10 −5 m). To achieve the required strength, a particular layup sequence of orientations of the anisotropic fibrous layers is used. During manufacturing, small localised defects in the form of misaligned fibrous layers can occur in composite materials. In this talk...     »