How well do models of particle motion represent industrial reality?

粒状或颗粒状材料在自然界和工艺工程中很常见，特别是作为产品，如速食食品，药品和消费品，如洗衣粉。与简单流体相比，它们表现出有趣和有用的行为，从某些情况下的动力学自由流动到其他情况下的缓慢摩擦主导运动。近年来，在模拟它们的行为方面取得了很大进展，特别是使用离散元方法（DEM），其中每个粒子被单独考虑，并计算其与相邻粒子的相互作用，以预测其下一个位置。许多工业公司现在正在使用这种方法进行设计和过程预测，但测试这种模型对现实是困难的，因为感兴趣的系统通常是不透明的，往往在金属壁容器内举行。该项目将利用一种称为正电子发射粒子跟踪（PEPT）的跟踪粒子运动的尖端技术，在一系列实验中绘制粒子行为，这些实验将同时使用DEM进行建模。目标是设计能够区分好的和坏的建模方法的模型的校准测试。这项工作是通过国际细颗粒研究所（IFPRI）与许多工业合作伙伴合作，IFPRI是一个总部位于美国的国际工业成员组织，其中许多人将通过他们的内部建模团队积极参与这项工作。理想的候选人将有兴趣在编码开发协议，使大型数据集的处理和比较，以及在粒子系统的物理和工程的兴趣。

Granular or particulate materials are common in nature and in process engineering, particularly as products such as instant foods, pharmaceuticals and consumer goods such as washing powders. Compared with simple fluids, they show interesting and useful behaviour, from kinetic free-flow in some circumstances to slow friction-dominated motion in others. In recent years there has been much progress in modelling their behaviour, particularly using the Discrete Element Method (DEM), in which each particle is considered separately and its interactions with its neighbours computed in order to predict its next position. Many industrial companies are now using such methods for design and process prediction but testing of such models against reality is difficult because the systems of interest are usually opaque and often held within metal-walled containers. This project will make use of a cutting-edge technique for tracking particle motion called Positron Emission Particle Tracking (PEPT) to map particle behaviour in a set of experiments which will be simultaneously modelled using DEM. The goal is to devise calibration tests for models which are able to discriminate between good and bad modelling approaches. The work is collaborative with a number of industrial partners through the International Fine Particle Research Institute (IFPRI), a US-based organisation of international industrial members, many of whom will be actively participating in the work through their in-house modelling teams. The ideal candidate will have an interest in coding to develop protocols to enable processing and comparison of large data sets, as well as an interest in the physics and engineering of particulate systems.