Impacts of Mineralogy on Aggregate Crushing

本项目旨在通过多尺度的实验、数值和机器学习研究，揭示矿物学对集料破碎的影响，并了解颗粒集合体中的顺序破碎机制。颗粒破碎存在于铁路道床、散体断层泥、高尾矿坝中，也与桩基安装和海上基础设计有关。粉碎和研磨在采矿作业以及制药、农业和食品部门的制造过程中是必不可少的。然而，这些操作仍然是高度低效的。通过这项研究，PI将从根本上了解矿物非均质性对颗粒破碎的影响。这些发现将有助于优化采矿作业中的颗粒材料处理、深基础设计以及制药和农业制造技术。对多矿物集合体的微观结构和力学行为的演化进行建模也将有助于部署用于现场土壤表征的渗透仪设备和用于陆地和地外探测的轮式车辆。除了他们的研究任务，PIS还将为学生创造多学期的本科生研究机会和国际研究经验，并与佐治亚理工学院的组织合作，将多样性、公平性和包容性培训融入他们的学术活动中。两家私人投资者都致力于提高公众对岩土工程在解决能源和可持续发展挑战方面的关键作用的认识。大多数天然岩土材料是多矿物的，但目前还没有已知的实验方法来区分形态和矿物学对集料破碎的影响。该项目的目标是揭示干法多矿物颗粒粉碎的未知机理，并通过优化干法骨料破碎来实现节能的工业应用。为实现这一目标，PI将：(1)测量石英岩和花岗岩骨料的矿物内和矿物间结合性质；(2)对单矿物和多矿物骨料进行单颗粒压碎测试；(3)用离散单元法(DEM)模拟测试过程中的碎裂过程；(4)用高分辨率X射线计算机断层扫描(DEM)模拟单调和循环加载过程中的骨料集合体图像；(5)用DEM模拟异质骨料集合体的顺序断裂和组构演变；(6)通过机器学习预测自组织和最终组构。几个基本问题将被解决，主要是：非均质颗粒集合体在循环加载和颗粒破碎时能自组织吗？碎片在连续断裂时是否朝着渐近大小和/或形状演化？该项目中提出的实验、数值和人工智能方法的协同部署有可能改变当前岩土材料表征和行为预测的实践。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.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.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.