  Morphology and dynamics at the meso-scale play crucial roles in the overall
macro- or system-scale flow of heterogeneous materials. In a multi-scale
framework, closure models upscale unresolved sub-grid (meso-scale) physics and
therefore encapsulate structure-property (S-P) linkages to predict performance
at the macro-scale. This work establishes a route to structure-property
linkage, proceeding all the way from imaged micro-structures to flow
computations in one unified level set-based framework. Level sets are used to:
1) Define embedded geometries via image segmentation; 2) Simulate the
interaction of sharp immersed boundaries with the flow field, and 3) Calculate
morphological metrics to quantify structure. Meso-scale dynamics are computed
to calculate sub-grid properties, i.e. closure models for momentum and energy
equations. The structure-property linkage is demonstrated for two types of
multi-material flows: interaction of shocks with a cloud of particles and
reactive meso-mechanics of pressed energetic materials. We also present an
approach to connect local morphological characteristics in a microstructure
containing topologically complex features with the shock response of imaged
samples of such materials. This paves the way for using geometric machine
learning techniques to associate imaged morphologies with their properties.
