Adaptivity for clustering-based reduced-order modeling of localized history-dependent phenomena
This work addresses the problem of fast and accurate material modeling for engineers and researchers dealing with localized history-dependent phenomena, representing an incremental improvement over existing clustering-based reduced-order models.
The paper tackles the challenge of modeling history-dependent nonlinear problems with localized plasticity and damage by proposing an Adaptive Clustering-based Reduced-Order Modeling (ACROM) framework, which dynamically refines clustering in high-gradient regions and shows improved performance over static methods in capturing multi-scale elasto-plastic behavior and predicting fracture and toughness in a particle-matrix composite.
This paper proposes a novel Adaptive Clustering-based Reduced-Order Modeling (ACROM) framework to significantly improve and extend the recent family of clustering-based reduced-order models (CROMs). This adaptive framework enables the clustering-based domain decomposition to evolve dynamically throughout the problem solution, ensuring optimum refinement in regions where the relevant fields present steeper gradients. It offers a new route to fast and accurate material modeling of history-dependent nonlinear problems involving highly localized plasticity and damage phenomena. The overall approach is composed of three main building blocks: target clusters selection criterion, adaptive cluster analysis, and computation of cluster interaction tensors. In addition, an adaptive clustering solution rewinding procedure and a dynamic adaptivity split factor strategy are suggested to further enhance the adaptive process. The coined Adaptive Self-Consistent Clustering Analysis (ASCA) is shown to perform better than its static counterpart when capturing the multi-scale elasto-plastic behavior of a particle-matrix composite and predicting the associated fracture and toughness. Given the encouraging results shown in this paper, the ACROM framework sets the stage and opens new avenues to explore adaptivity in the context of CROMs.