  Concurrent analysis of composite materials can provide the interaction among
scales for better composite design, analysis, and performance prediction. A
data-driven concurrent n-scale modeling theory ($\textrm{FExSCA}^\textrm{n-1}$)
is proposed in this paper utilizing a mechanistic reduced order model (ROM)
called self-consistent clustering analysis (SCA). We demonstrated this theory
with a $\textrm{FExSCA}^2$ approach to study the 3-scale woven carbon fiber
reinforced polymer (CFRP) laminate structure. $\textrm{FExSCA}^2$ significantly
reduced expensive 3D nested composite representative volume elements (RVEs)
computation for woven and unidirectional (UD) composite structures by
developing a material database. The modeling procedure is established by
integrating the material database into a woven CFRP structural numerical model,
formulating a concurrent 3-scale modeling framework. This framework provides an
accurate prediction for the structural performance (e.g., nonlinear structural
behavior under tensile load), as well as the woven and UD physics field
evolution. The concurrent modeling results are validated against physical tests
that link structural performance to the basic material microstructures. The
proposed methodology provides a comprehensive predictive modeling procedure
applicable to general composite materials aiming to reduce laborious
experiments needed.
