Microstructural material database for self-consistent clustering analysis of elastoplastic strain softening materials

The self-consistent clustering analysis (SCA) recently proposed by Liu et al. [1] provides an effective way of developing a microstructural database based on a clustering algorithm and the Lippmann-Schwinger integral equation, which enables an efficient and accurate prediction of nonlinear material response. The self-consistent clustering analysis is further generalized to consider complex loading paths through the projection of the effective stiffness tensor. In the concurrent simulation, the predicted macroscale strain localization is observed to be sensitive to the combination of microscale constituents, showing the unique capability of the SCA microstructural database for complex material simulations.