Computational Protocols for the Study of Damage Initiation in Unidirectional Fiber-Reinforced Polymer Matrix Composites

Abstract The increased adoption of polymer matrix composites (PMCs) in failure-critical applications is impeded by the challenges in developing reliable datasets for predictive models linking salient attributes of PMC microstructures to their damage resilience properties. We present a comprehensive set of computational protocols for producing high-value simulation datasets that can be used for building the desired machine-learnt models. These new protocols combine (i) a novel generative approach to produce ensembles of distinct statistical volume elements (SVEs) targeted to specified combinations of fiber volume fractions and the degree and directionality of fiber clustering, and (ii) consistent protocols for the construction of extreme value distributions describing microscale damage drivers from finite element-predicted stress fields. It is demonstrated that the proposed protocols can produce a large dataset comprised of distinct SVEs in a computationally efficient manner, and the produced dataset is openly shared with the broader research community to serve as a benchmark for future studies.