The 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.
