A new Monte Carlo model for predicting the mechanical properties of fiber yarns. (November 2015) - Wei, Xiaoding; Ford, Matthew; Soler-Crespo, Rafael A.; Espinosa, Horacio D.
Abstract: Understanding the complicated failure mechanisms of hierarchical composites such as fiber yarns is essential for advanced materials design. In this study, we developed a new Monte Carlo model for predicting the mechanical properties of fiber yarns that includes statistical variation in fiber strength. Furthermore, a statistical shear load transfer law based on the shear lag analysis was derived and implemented to simulate the interactions between adjacent fibers and provide a more accurate tensile stress distribution along the overlap distance. Simulations on two types of yarns, made from different raw materials and based on distinct processing approaches, predict yarn strength values that compare favorably with experimental measurements. Furthermore, the model identified very distinct dominant failure mechanisms for the two materials, providing important insights into design features that can improve yarn strength. Highlights: We develop a new Monte Carlo yarn model for fiber yarns' strength prediction. Element size dependence is resolved by a new random strength generation algorithm. Shear-lag-based load transfer law is employed to simulate inter-fiber interactions. Key factors limiting two CNT yarns׳ mechanical performance are identified.