A damage-related adaptive self-consistent clustering analysis method with localized refinement capability for the damage problem of 3D woven composites

In this work, a Damage-related Adaptive Self-Consistent Clustering Analysis (DASCA) method is developed to achieve higher solving accuracy for 3D damage problems than Self-Consistent Clustering Analysis (SCA) method, which is applied to investigate the mesoscale damage behavior of 3D woven composites (3DWC). The developed clustering adaptivity framework is based on the characteristics of the damage problem, which consists of five fundamental stages: evaluation of the adaptivity conditions, selection criterion of potential damage clusters, online adaptive clustering analysis, update of the cluster database and adaptive rewind of the analysis process. In the second stage, the clusters with the highest potential to enter the damage state are identified and marked as adaptivity target clusters. In the solving process, the distribution of clusters can achieve problem-dependent dynamic adjustments, ensuring that clusters are concentrated on the regions of interest. Though the numerical examples of 3DWC, the DASCA solutions, using fewer clusters and less computational cost, exhibit higher prediction accuracy for macroscopic mechanical performance, first damage initiation moments and damage evolution process than the SCA solutions.