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