Materials formed from particles are often handled in the fluid state, such as cement and concrete. These particle-laden fluids have complex flow properties, and understanding the basis for this behavior is critical to design and control of the particle interactions that are responsible for the properties. The surface interactions are also the controlling factor in the behavior of liquid-saturated soils. In the geotechnical context, controlling the interactions is not feasible, but understanding and predicting behavior is especially important. This project seeks to explain the behavior of flowing materials that exhibit very strong property changes. The focus is on suspensions with a high concentration of solid particles in a liquid. These materials often shear thicken, meaning drastically increase their viscosity, such as in cornstarch suspensions in water. This project will use simulation and theoretical methods to examine how the particle forces and surface shape cause shear thickening. Computer simulation models the forces of interaction between the particles and solves for their motion. From the forces and the particle velocities, the flow properties such as the viscosity can be determined, and these are validated against experiments. A set of theoretical tools is applied to describe the forces in the material and how they are connected in a network, much like the connections in a fishnet. The overall goal of the project is to establish a theoretical framework allowing prediction of the flow properties from a knowledge of the basic forces, thus allowing better design in engineering contexts and prediction in geophysical contexts. The team will work with industrial partners in the cement and concrete sector to use the understanding toward design of additives that modify flow behavior while retaining desired properties in the flowing and solid material.
Using discrete-particle simulation and statistical mechanical theory, the goal of this project is to develop a statistical mechanical framework that describes the basis for shear thickening and jamming in highly-concentrated suspensions of solid particles in liquids. The contributions of this work will advance the statistical mechanical framework for nonequilibrium suspensions, allowing better ability to predict behavior at the macroscopic scale from a knowledge of the particle interactions. A simulation tool in its established and validated form will be used to explore the nonequilibrium steady states (NESS) ensemble of shear-thickening suspensions under constraints on stress, shear rate, and volume fraction. The ensemble data from simulation will be explored by several theoretical tools, including network theory to establish a structure-property framework appropriate for dense suspensions that approach the jamming condition. A machine learning approach will be applied to advance methods previously developed by the team for prediction of the probability of the stress state based on the microscopic force interactions. Methods from large deviation theory will be used as a framework for describing the fluctuations of properties in NESS ensemble. The simulation method will also be advanced by systematically considering the role of size distribution and particle surface complexity. In addition to simple shear, oscillatory shearing and more complex flow protocols, in which an orthogonal oscillatory component is superimposed on simple shear, will be developed and the results explored by the noted theoretical tools. The research will engage undergraduate, graduate and post-doctoral researchers. The team will prepare technical society short courses and engage in STEM outreach to expose materials physics to K-12 students.
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.
