Multiscale Modeling of Brittle Composites Using Reduced Order Computational Homogenization

Abstract

This dissertation presents a novel multiscale computational framework for simulating failure and damage accumulation in brittle composites. A reduced-order multiple spatial scale methodology is developed to efficiently model failure under monotonic loading conditions. This methodology is based on the computational homogenization approach. The proposed reduced order approach is computationally efficient when compared to standard computational homogenization. Modeling of individual failure modes, such as matrix cracking, matrix/fiber debonding, delamination, and fiber fracture are incorporated into this methodology. A multiple spatio-temporal scale technique is proposed for simulating failure in composites subjected to cyclic loading conditions. This technique is based on the generalization of the homogenization approach to temporal scales. An adaptive macrochronological time stepping algorithm is devised to predict damage accumulation by resolving a small subset of cycles throughout the life of the composite. The proposed multiscale framework was verified by numerical simulation and was validated using experimental testing. Experimental validation involved a series of monotonic and fatigue experiments conducted on the carbon fiber reinforced polymer composite, IM7/977-3. Non-destructive inspection techniques including acoustic emission, X-ray radiography, and X-ray computed tomography were utilized to characterize progressive damage accumulation in the composite material. The multiple spatio-temporal model was calibrated and employed to predict the failure response of IM7/977-3 specimens. The proposed model was demonstrated to accurately and efficiently predict strength, ductility, and damage growth characteristics under both monotonic and cyclic loadings. The combined computational and experimental investigation provided a thorough picture of the failure processes of this material.