The objective of the proposed research is to establish a framework to predict the continuum behavior of particulate systems by understanding and engineering a set of grain-scale features, termed here dynamic network attributes (DNA). The proposed work will develop the hypothesis that continuum behavior is encoded at the scale where neighboring grains interact. These interactions establish a stress path network, or fabric, that adapts dynamically to changing load conditions; giving rise to mesoscale features like force chains, which are unique to granular matter. In other words, the evolving DNA induces continuous change in the meso scale and the continuum response of the material. The opposite is also true, constituting a closed loop between grain-meso-continuum response. What is currently lacking are general rules that would allow one to connect, in a predictive manner, continuum behavior to the required grain-scale DNA. Part of the bottleneck is our current lack of understanding of how the DNA induces mesoscale features – and the associated lack of strong scale separability – and manifests as continuum behavior. As shown in Figure 1, to understand and predict particulate systems it is necessary to embrace the interconnectedness between DNA, meso scale and continuum behavior, what we call the granular mechanics Ouroboros.

To achieve the proposed objective, several fundamental science questions will need to be answered: What is the set of grain-scale parameters (e.g., grain DNA) that controls macroscopic behavior in particulate systems? How does grain DNA evolve? And, given such evolution, how can one then predict macroscopic behavior? If successful, this multidisciplinary university research initiative (MURI) will provide answers to these fundamental questions.