Concurrent n-scale modeling for non-orthogonal woven composite

TL;DR

This paper introduces a data-driven concurrent n-scale modeling framework for woven composites that reduces computational costs and accurately predicts structural performance by integrating a mechanistic reduced order model and material database.

Contribution

It proposes a novel n-scale modeling theory ($\textrm{FExSCA}^\textrm{n-1}$) and demonstrates its application to 3-scale woven CFRP, significantly reducing computational effort while maintaining accuracy.

Findings

Reduced 3D RVE computations using material database

Accurate prediction of nonlinear structural behavior

Validated results linking microstructure to performance

Abstract

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.