Collaborative Research: Discontinuous Shear Thickening and Shear Jamming in Dense Suspensions: Statistical Mechanics and the Microscopic Basis for Extreme Transitions of Properties

由颗粒形成的材料通常在流体状态下处理，如水泥和混凝土。这些含有颗粒的流体具有复杂的流动性质，了解这种行为的基础对于设计和控制导致这些性质的颗粒相互作用至关重要。地表相互作用也是液体饱和土体性状的控制因素。在岩土工程背景下，控制相互作用是不可行的，但理解和预测行为尤其重要。这个项目试图解释流动的物质表现出非常强烈的性质变化的行为。重点是液体中固体颗粒浓度较高的悬浮液。这些物质通常会剪切增稠，这意味着它们的粘度会急剧增加，比如玉米淀粉在水中的悬浮液。本项目将使用模拟和理论方法来研究粒子力和表面形状如何导致剪切增厚。计算机模拟对粒子之间的相互作用力进行建模，并对其运动进行求解。根据作用力和颗粒速度，可以确定粘性等流动特性，并与实验进行了验证。一套理论工具被用来描述材料中的力以及它们如何在网络中连接，就像渔网中的连接一样。该项目的总体目标是建立一个理论框架，以便能够根据基本力的知识预测流动特性，从而能够在工程背景下进行更好的设计，并在地球物理背景下进行预测。该团队将与水泥和混凝土行业的工业合作伙伴合作，利用对添加剂设计的理解，在保持流动和固体材料所需性能的同时改变流动行为。利用离散粒子模拟和统计力学理论，本项目的目标是开发一个统计力学框架，描述高浓度固体颗粒在液体中悬浮时剪切增稠和堵塞的基础。这项工作的贡献将推进非平衡悬浮的统计力学框架，使其能够更好地根据粒子相互作用的知识在宏观尺度上预测行为。建立和验证的模拟工具将被用来探索剪切增稠悬浮液在应力、剪切速率和体积分数约束下的非平衡稳态(NESS)系综。来自仿真的总体数据将被包括网络理论在内的几种理论工具来探索，以建立适用于接近干扰条件的稠密悬架的结构-特性框架。机器学习方法将被应用于该团队先前开发的高级方法，用于基于微观力相互作用预测应力状态的概率。大偏差理论中的方法将被用作描述Ness系综中性质涨落的框架。通过系统地考虑粒度分布和颗粒表面复杂性的作用，提出了模拟方法。除了简单剪切外，还将开发振荡剪切和更复杂的流动方案，其中正交振荡分量叠加在简单剪切上，并通过著名的理论工具探索结果。这项研究将邀请本科生、研究生和博士后研究人员参与。该团队将准备技术协会短期课程，并参与STEM外联活动，向K-12学生展示材料物理。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.