View article

A stochastic multi-scale modeling framework for uncertainty quantification of carbon fiber reinforced composites with a non-intrusive method called Polynomial Chaos Decomposition with Differentiation (PCDD) is presented. The performance behavior and reliability of the composites are dependent on its constituents properties (fiber and matrix properties) in addition to ply-orientation, ply-thickness, and loading conditions; hence, the uncertainties are considered using two stages: i) micro-scale modeling, and ii) macro-scale modeling. In the first stage, stochastic micro-scale modeling with PCDD is carried out to obtain the effective material properties of a lamina that are influenced by the uncertainties in its constituents. Then, these stochastic effective material properties are considered along with the uncertainties in geometrical properties of the laminate such as ply thicknesses and ply orientation to determine the …