Impacts of Mineralogy on Aggregate Crushing

矿物学对骨料破碎的影响

• 批准号: 2416332
• 负责人: Chloe Arson
• 金额: $ 52.82万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2025-12-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2416332&HistoricalAwards=false
• 关键词: Impacts; Mineralogy; Aggregate; Crushing

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.

该项目旨在揭示矿物学对骨料破碎的影响，并通过多尺度实验，数值和机器学习研究了解颗粒组件中的顺序破碎机制。颗粒破碎不仅发生在铁路道碴、粒状断层泥、高尾矿坝中，而且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、农业和食品行业的制造过程中至关重要。然而，这些操作仍然是高度节能的。通过这项研究，PI将获得矿物非均质性对颗粒破碎的影响的基本认识。研究结果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。对多矿物聚集体微观结构和力学行为的演变进行建模也将有助于部署用于现场土壤表征的磁流变仪设备和用于陆地和外星探索的轮式车辆。除了他们的研究任务，PI将为学生创造多学期的本科研究机会和国际研究经验，并将多样性，公平和包容性培训融入他们的学术活动与格鲁吉亚技术组织合作。这两个PI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关键作用的认识。大多数天然地质材料是多矿物的，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多矿物颗粒破碎的未知机制，并通过优化干骨料破碎实现节能的工业应用。为实现这一目标，专业人员将（1）测量石英和花岗岩骨料中矿物内部和矿物之间的结合特性;（2）对单矿物和多矿物骨料进行单颗粒破碎试验;（3）用离散单元法模拟这些试验期间的破碎过程;（4）通过高分辨率X射线计算机断层扫描对单调和循环加载期间的骨料组合体进行成像;（5）利用离散元模拟异质聚集体的有序破碎和结构演化;（6）利用机器学习预测自组织和最终结构。几个基本问题将得到解决，主要是：可以异质颗粒组装自组织循环加载和颗粒破碎？在连续断裂后，碎片是否会朝着渐进的尺寸和/或形状演变？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地质材料表征和行为预测的实践。该奖项反映了NSF的法定使命，并被认为值得通过使用该基金会的智力进行评估来支持优点和更广泛的影响审查标准。