NSF-IFPRI Collaboratory in Dense Particulate Flow

NSF-IFPRI 稠密颗粒流合作实验室

• 批准号: 1010008
• 负责人: Paul Mort
• 金额: $ 9.75万
• 依托单位: International Fine Particle Research Institute, Inc.
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-05-01 至 2012-04-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1010008&HistoricalAwards=false
• 关键词: NSF; IFPRI; Collaboratory; Dense; Particulate

CBET-101008Mort, Paul R.Intellectual Merit: In recognition of advances in simulation and modeling of granular flows, the International Fine Particle Research Institute (IFPRI) is currently developing a collaborative program in which IFPRI will make available specific datasets from well controlled experiments wherein flow fields are well defined and stresses are measured over scales ranging from individual particles, chains and clusters of particles, and bulk particulate flows. The datasets include both model and industrially-relevant materials with distributed characteristics (e.g., size and shape) and a range of material properties (stiffness, restitution, etc). The challenge of the program is to develop theoretical approaches and models to describe the range of flows measured in the IFPRI experiments and validation thereof. Broader Impact - in the mutual interest of Industry, Academia and Sustainability: Industry requires useful methods to predict stress and flow behavior of granular and powder materials across industrially relevant flow regimes, as a function of operating conditions, material properties and particulate characteristics. In the trend toward sustainable processing and energy efficiency, industry strives toward the minimization of specific energy input in granular and powder processing, i.e., minimal power consumption at maximum production rate. The fundamental objectives of this project address this goal by providing a robust description of the physics of dense flows on particle and bulk scales, including energy dissipative interactions of realistic materials. As a foundation for predictive methods, the broader community of industrial and academic researchers, modelers and engineers need to define and understand the relevant regimes of dense particulate flow, the underlying physics therein, and the effect of boundary conditions, material properties and particle characteristics.

CBET-101008 Mort，Paul R.知识产权：认识到颗粒流模拟和建模方面的进展，国际细颗粒研究所（IFPRI）目前正在开发一个合作计划，其中IFPRI将提供来自良好控制实验的特定数据集，其中流场得到很好的定义，并在单个颗粒、颗粒链和颗粒群的尺度上测量应力，和大量颗粒流。数据集包括具有分布式特征的模型和工业相关材料（例如，尺寸和形状）和一系列材料特性（刚度、恢复性等）。该计划的挑战是开发理论方法和模型来描述IFPRI实验中测量的流量范围及其验证。更广泛的影响-符合工业、学术界和可持续发展的共同利益：工业需要有用的方法来预测颗粒和粉末材料在工业相关流动状态下的应力和流动行为，作为操作条件、材料特性和颗粒特性的函数。在可持续加工和能源效率的趋势中，工业努力使颗粒和粉末加工中的比能输入最小化，即，以最大的生产率实现最小的能耗。该项目的基本目标是通过提供粒子和体积尺度上的稠密流物理学的强大描述来实现这一目标，包括现实材料的能量耗散相互作用。作为预测方法的基础，更广泛的工业和学术研究人员，建模人员和工程师需要定义和理解稠密颗粒流的相关机制，其中的基本物理特性，以及边界条件，材料特性和颗粒特性的影响。