Adaptive Clustering-based Reduced-Order Modeling Framework: Fast and accurate modeling of localized history-dependent phenomena

by   Bernardo P. Ferreira, et al.

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.


page 5

page 14

page 20

page 25

page 30

page 40


Concurrent Multiscale Damage Analysis with Adaptive Spatiotemporal Dimension Reduction

Concurrent multiscale damage models are often used to quantify the impac...

Localized Reduced Basis Additive Schwarz Methods

Reduced basis methods build low-rank approximation spaces for the soluti...

Locally Adaptive Hierarchical Cluster Termination With Application To Individual Tree Delineation

A clustering termination procedure which is locally adaptive (with respe...

Adaptive Reduced Basis Methods for Multiscale Problems and Large-scale PDE-constrained Optimization

This thesis presents recent advances in model order reduction methods wi...

Physics-informed cluster analysis and a priori efficiency criterion for the construction of local reduced-order bases

Nonlinear model order reduction has opened the door to parameter optimiz...

Extended tensor decomposition model reduction methods: training, prediction, and design under uncertainty

This paper introduces an extended tensor decomposition (XTD) method for ...

Please sign up or login with your details

Forgot password? Click here to reset