Granular Materials Design and Optimization

颗粒材料设计与优化

• 批准号: 1334426
• 负责人: Heinrich Jaeger
• 金额: $ 33万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1334426&HistoricalAwards=false
• 关键词: Granular; Materials; Design; Optimization

1334426PI: JaegerThe handling and processing of particulate matter is important to the economy and technology base of the nation. For applications where either the performance of the granular material is critical or the fabrication cost is substantial, optimization of the constituent particles becomes a key task. Yet the understanding and control of the complex behavior exhibited by granular material poses formidable challenges, in particular for non-spherical shapes. The state-of-the-art approach is to predict the aggregate properties for given particle type or shape. What is needed for proper design, but so far has been lacking, is a general approach to the inverse problem: a methodology that identifies those particle attributes that will optimize given aggregate properties. The objective of the project is to develop and implement such methodology. The project integrates evolutionary optimization strategies, numerical simulations, three-dimensional (3d) rapid prototyping, experiments testing the mechanical load response, and non-invasive x-ray imaging into a comprehensive, tightly coupled approach capable of providing solutions to this inverse problem.This project directly addresses questions that so far have been difficult to answer, including how to optimize particle shape for given performance goals or design granular materials with unique aggregate characteristics that fall outside the typical performance regions. A focus of the project will be on designing the mechanical load response of random granular aggregate systems, an aspect much less studied than the static packing properties. Going beyond simple convex particles, the project will explore a wide class of compound particles composed from smaller building blocks. Arbitrary particle shapes will be represented by granular molecules, whose configuration can be mutated and evolved to optimize performance. This evolution is performed by an optimization algorithm that calls up DEM simulations. The project will explore a range of different particle-level variables besides shape, such as size, bulk modulus, and bending rigidity (for more complex, granular-polymer-type particles). Among the specific goals will be to design granular materials not only with respect to characteristics like the effective modulus or the yield stress of the aggregate, but to design the whole stress strain curve. 3d-printing will make it possible to fabricate large numbers of optimized particles for direct experimental validation. X-rays will provide microstructural information in cases where particles cannot be simulated and to check whether design rules obtained from model particles, such as 3d-printed ones, remain valid when the particle material is changed.The availability of optimized designed particles would make it possible to overcome a number of bottlenecks currently limiting the use of granular materials and open up a wide range of new uses. This might include lightweight jammable and shape-conforming materials for soft robotics; high-toughness high-porosity materials for medical implants; or shock absorbing materials that have designed stress-strain characteristics and can be poured around sensitive equipment. The project will train graduate and undergraduate students in forefront research at the interface of science and engineering. The research will be integrated with a multi-faceted set of education and outreach activities, including activities with the nearby Chicago Museum of Science and Industry.

1334426 PI：Jaeger颗粒物的处理和加工对国家的经济和技术基础很重要。对于颗粒材料的性能至关重要或制造成本很高的应用，组成颗粒的优化成为关键任务。然而，理解和控制颗粒材料所表现出的复杂行为带来了巨大的挑战，特别是对于非球形形状。最先进的方法是预测给定颗粒类型或形状的聚集体性质。正确的设计所需要的，但到目前为止一直缺乏的是一个通用的方法来解决逆问题：一种方法，确定这些粒子属性，将优化给定的聚合物属性。该项目的目标是制定和实施这种方法。该项目将进化优化策略、数值模拟、三维（3d）快速原型、测试机械载荷响应的实验和非侵入性X射线成像集成为一个全面的、紧密耦合的方法，能够为这一逆问题提供解决方案。该项目直接解决了迄今为止难以回答的问题，包括如何针对给定的性能目标优化颗粒形状或设计具有落在典型性能区域之外的独特聚集体特性的粒状材料。该项目的重点将是设计随机颗粒骨料系统的机械载荷响应，这方面的研究比静态包装性能少得多。除了简单的凸粒子，该项目还将探索由更小的构建块组成的广泛的复合粒子。任意颗粒形状将由颗粒分子表示，其配置可以突变和进化以优化性能。这种演变是通过调用DEM模拟的优化算法来执行的。该项目将探索除了形状之外的一系列不同的颗粒水平变量，例如尺寸，体积模量和弯曲刚度（对于更复杂的颗粒聚合物类型的颗粒）。具体目标之一是设计颗粒材料，不仅要考虑骨料的有效模量或屈服应力等特性，还要设计整个应力应变曲线。3d打印将使制造大量优化颗粒用于直接实验验证成为可能。X射线将在无法模拟颗粒的情况下提供微观结构信息，并检查从模型颗粒（如3d打印颗粒）获得的设计规则在颗粒材料发生变化时是否仍然有效。优化设计颗粒的可用性将使克服目前限制颗粒材料使用的许多瓶颈成为可能，并开辟广泛的新用途。这可能包括用于软机器人的轻质可干扰和形状一致的材料;用于医疗植入物的高韧性高孔隙率材料;或具有设计应力-应变特性并可倾倒在敏感设备周围的减震材料。该项目将培养研究生和本科生从事科学与工程结合的前沿研究。这项研究将与多方面的教育和推广活动相结合，包括与附近的芝加哥科学与工业博物馆的活动。