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Uncontrolled curing-induced residual stress and strain are significant limitations to the efficient design of thermoset composites that compromise their structural durability and geometrical tolerance. Experimentally validated process modeling for the evaluation of processing parameter contributions to the residual stress build-up is crucial to identify residual stress mitigation strategies and enhance structural performance. This work presents an experimentally validated novel numerical approach based on higher-order finite elements for the process modeling of fiber-reinforced thermoset polymers across two composite characteristic length scales, the micro and macro-scale levels. The cure kinetics is described using an auto-catalytic phenomenological model. An instantaneous linear-elastic constitutive law, informed by time-dependent material characterization, is used to evaluate the stress state evolution as a function of the degree of cure and time. Micromechanical modeling is based on Representative Volume Elements (RVEs) that account for random fiber distribution verified against traditional 3D FE analysis. 0/90 laminate testing at the macroscale validates the proposed approach with an accuracy of 9%. 

The present work investigates progressive damage in steel-reinforced concrete structures. An elastic-perfectly plastic material response is considered for the reinforcing steel constituent, while the smeared-crack approach is applied to model the nonlinear behavior of concrete. The analysis employs one-dimensional numerical models based on higher-order finite elements derived using the Carrera unified formulation (CUF). A set of numerical assessments is presented to study the mechanical response of a steel-reinforced notched concrete beam loaded in tension. The predictions are found to be in very good agreement with reference experimental observations, thereby validating the numerical approach. It is shown that CUF allows for the explicit representation of the constituents within the composite beam, resulting in accurate solutions in a computationally efficient manner.