NSF Award
2416332
This project aims to unravel the impacts of mineralogy on aggregate crushing and understand sequential fragmentation mechanisms in granular assemblies via multi-scale experimental, numerical, and machine learning investigations. Particle crushing occurs in railway ballast, granular fault gouge, high tailing dams, and is also relevant to pile installation and offshore foundation design. Crushing and grinding are essential in mining operations, as well as manufacturing processes in the pharmaceutical, agricultural and food sectors. However, these operations are still highly energy-inefficient. Through this research, the PIs will gain a fundamental understanding of the effect of mineral heterogeneity on particle breakage. Findings will contribute to optimizing particulate material handling in mining operations, deep foundation design and pharmaceutical and agricultural manufacturing techniques. Modeling the evolution of polymineralic aggregate microstructure and mechanical behavior will also serve the deployment of penetrometer devices for in-situ soil characterization and wheeled vehicles for terrestrial and extraterrestrial exploration. Besides their research tasks, the PIs will create multi-semester undergraduate research opportunities and international research experiences for students, and integrate diversity, equity and inclusion training into their scholarly activities in partnership with Georgia Tech organizations. Both PIs are committed to enhance public awareness on the critical role of geotechnical engineering in addressing energy and sustainability challenges.<br/><br/><br/>Most natural geomaterials are polymineralic, and yet, there is no known experimental method that can disentangle the effects of morphology and mineralogy on aggregate crushing. The goal of the project is to unveil the yet-unknown mechanisms of dry polymineralic grain crushing and to enable energy-efficient industrial applications by optimizing dry aggregate breakage. Towards this goal, the PIs will (1) Measure intra- and inter-mineral bonding properties in quartzitic and granitic aggregates; (2) Conduct single-particle crushing tests on monomineralic and polymineralic aggregates; (3) Model fragmentation processes during these tests with the Distinct Element Method (DEM); (4) Image aggregate assemblies during monotonic and cyclic loading by high-resolution X-ray computed tomography; (5) Simulate sequential breakage and fabric evolution in heterogeneous aggregate assemblies with the DEM; (6) Predict self-organization and ultimate fabric by Machine Learning. Several fundamental questions will be addressed, mainly: Can heterogeneous granular assemblies self-organize upon cyclic loading and particle breakage? Do fragments evolve towards an asymptotic size and/or shape upon sequential breakage? The synergistic deployment of experimental, numerical, and artificial intelligence methods proposed in this project has the potential to transform the current practice of geomaterial characterization and behavior prediction.<br/><br/>This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
