Adaptive spatiotemporal dimension reduction in concurrent multiscale damage analysis

Concurrent multiscale damage models are often used to quantify the impacts of manufacturing-induced micro-porosity on response macroscopic metallic components. However, these challenged by major numerical issues including mesh dependency, convergence difficulty, and low accuracy in concentration regions. In this paper, we make two contributions address difficulties. Firstly, develop a novel adaptive assembly-free implicit-explicit (AAF-IE) temporal integration scheme for nonlinear constitutive models. This prevents that implicit algorithms face amid softening. Our AAF-IE autonomously adjusts step sizes capture intricate history-dependent deformations. It also dispenses with re-assembling stiffness matrices elasto-plasticity which, turn, dramatically reduces memory footprints. Secondly, propose an clustering-based domain decomposition strategy reduce spatial degrees freedom agglomerating close-by finite element nodes into limited number clusters. clustering has static dynamic stages carried out during offline online analyses, respectively. The updates cluster density based discontinuity plastic strain. As demonstrated experiments, proposed method strikes good balance between efficiency fracture simulations. particular, use our efficient concurrent model significance spatially varying microscopic porosity macrostructure’s softening behavior.