Impacts of Mineralogy on Aggregate Crushing

该项目旨在通过多尺度实验、数值和机器学习研究，揭示矿物学对骨料破碎的影响，并了解颗粒组合中的顺序破碎机制。颗粒破碎主要发生在铁路道砟、粒状断层泥、高尾矿库中，也与桩基安装和海上基础设计有关。破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，这些操作仍然非常低效。通过这项研究，pi将对矿物非均质性对颗粒破碎的影响有一个基本的了解。研究结果将有助于优化采矿作业中的颗粒材料处理、深地基设计以及制药和农业制造技术。多矿物骨料微观结构和力学行为的演化建模也将服务于用于原位土壤表征的穿透仪设备和用于地面和地外探测的轮式车辆的部署。除了他们的研究任务外，pi还将为学生创造多学期的本科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纳入他们的学术活动中。这两个项目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中的重要作用的认识。大多数天然岩土材料是多矿物的，然而，没有已知的实验方法可以解开形态和矿物学对骨料破碎的影响。该项目的目标是揭示干式多矿物颗粒破碎的未知机制，并通过优化干式骨料破碎来实现节能工业应用。为了实现这一目标，pi将(1)测量石英和花岗岩团聚体中的矿物内和矿物间的结合特性；(2)对单矿物和多矿物骨料进行单颗粒破碎试验；(3)用离散元法（DEM）模拟试验过程中的破碎化过程；(4)高分辨率x射线计算机断层扫描单调和循环加载过程中的图像聚合；(5)利用DEM模拟非均质骨料组合体的顺序断裂和织构演化；(6)通过机器学习预测自组织和最终结构。几个基本问题将被解决，主要是：异质颗粒组合能否在循环加载和颗粒破碎时自组织？在连续破碎过程中，碎片是否朝着渐进的大小和/或形状演化？本项目提出的实验、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。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值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.