Efficient multiscale modeling for woven composites based on self-consistent clustering analysis

Abstract Multiscale simulation of woven composites structure remains a challenge due to extremely expensive computational cost for solving the nonlinear woven Representative Volume Element (RVE). Recently, an effective and efficient Reduced Order modeling method, namely Self-consistent Clustering Analysis (SCA), is proposed to solve the RVE problem. In this work, the curse of computational cost in woven RVE problem is countered using the SCA, which maintains a considerable accuracy compared with the standard Finite Element Method (FEM). The Hill anisotropic yield surface is predicted efficiently using the woven SCA, which can accelerate the microstructure optimization and design of woven composites. Moreover, a two-scale FEM × SCA modeling framework is proposed for woven composites structure. Based on this framework, the complex behavior of the composite structures in macroscale can be predicted using microscale properties. Additionally, macroscale and mesoscale physical fields are captured simultaneously, which are hard, if not impossible, to observe using experimental methods. This will expedite the deformation mechanism investigation of composites. A numerical study is carried out for T-shaped hooking structures under cycle loading to illustrate these advantages.