Organizers:
- Jason Mulderrig, Air Force Research Laboratory
- Matthew Grasinger, Air Force Research Laboratory
- Michael Buche, Sandia National Laboratories
Description:
The microscale topology of many materials exhibits complex network structures, ranging from irregular arrangements in polymeric and biological systems to engineered inhomogeneities in composites and metamaterials. These networks are characterized by rich variations in their structural features — from the length and orientation of structural elements (e.g. chains/fibers) to the functionality of connection sites and spatial distribution of constituents. When external force is applied, network elements often respond non-uniformly: in fibrous networks, elements deform non-affinely, and at large deformations, load-bearing becomes highly localized. This heterogeneous microscale response fundamentally shapes bulk material properties, including elasticity, strength, and fracture behavior (among others). Understanding these structure-property relationships requires both multiscale modeling approaches and novel experimental techniques that can probe the distribution of loads, strains, and failure events across multiple length scales. Advancing our understanding of how inherent or designed inhomogeneities influence material response will not only enhance our fundamental physical insights, but also guide the development of next-generation materials with tailored mechanical properties.
Topics of interest:
In this mini-symposium, we are broadly interested in a wide range of modeling and experimental techniques spanning across — and bridging between — various time and length scales. Possible topics include, but are not limited to:
- Statistical homogenization theories
- Graph theory-based network analysis techniques that connect topological features of the microstructure to emergent properties
- Statistical mechanics and micromechanical modeling approaches
- Continuum-level approaches (FEM)
- Data-driven machine learning tools (both conventional and graph-based ML techniques are of interest)
- Experimental characterization of load share and fracture in irregular networks (e.g. mechanoresponsive polymers)
- Networks with dynamic topologies (e.g. vitrimers)
- Discrete fibrous network models and coarse-grained chain molecular dynamics models
- Topologically-informed polymer chain scission and network fracture models
- Experiments and theory surrounding disordered mechanical metamaterials