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Concurrent multiscale damage models are often used to quantify the impacts ofmanufacturing-induced micro porosity on the damage response of macroscopicmetallic components. However, these models are challenged by major numericalissues including mesh dependency, convergence difficulty, and low accuracy inconcentration regions. We make two contributions that collectively aim toaddress these difficulties. Firstly, we develop a novel adaptive assembly freeimpl-exp (AAF-IE) temporal integration scheme for nonlinear constitutivemodels. This scheme prevents the convergence issues that arise when implicitalgorithms are employed to model softening. Our AAF-IE scheme autonomouslyadjusts step sizes to capture intricate history dependent deformations. It alsodispenses with reassembling the stiffness matrices amid runtime forelasto-plasticity and damage models which, in turn, dramatically reduces memoryfootprints. Secondly, we propose an adaptive clustering-based domaindecomposition strategy to dramatically reduce the spatial degrees of freedom byagglomerating close-by finite element nodes into a limited number of clusters.Our adaptive clustering scheme has static and dynamic stages that are carriedout during offline and online analyses, respectively. The adaptive strategydetermines the cluster density based on spatial discontinuity and controlsadaptive clusters via field estimations. As demonstrated by numericalexperiments the proposed adaptive method strikes a good balance betweenefficiency and accuracy for fracture simulations. In particular, we use ourefficient concurrent multiscale model to quantify the significance of spatiallyvarying microscopic porosity on a macrostructural softening behavior.

Adaptive Spatiotemporal Dimension Reduction in Concurrent Multiscale Damage Analysis