CAREER: Using Stochastic Techniques to Understand and Predict the Flow of Non-spherical Particles

涉及气体中的颗粒的稠密悬浮物的流动，被称为颗粒流，在自然界和工业中是常见的。 在许多具有实际重要性的情况下，悬浮颗粒具有不规则形状（即，非球形），这使得预测悬浮液的流动行为对于当前可用的方法尤其具有挑战性。 因此，许多颗粒工艺的设计往往依赖于昂贵的经验试错测试。 这个CAREER项目将开发一个基于物理的随机模型，该模型考虑了不规则的颗粒形状，以更准确地预测大规模系统中的颗粒动力学。 该项目的结果将是有益的扩展颗粒流理论的理想化球形颗粒更现实的颗粒介质，并在提供新的解决方案，发生在颗粒技术的技术挑战。 该项目将涉及研究生和本科生的研究培训，并将为他们可能从事的涉及粒子技术的职业做好准备。 该研究团队将与普渡大学工程外展俱乐部合作，为当地学校的K-12学生带来颗粒流演示。该CAREER项目的目标是使用随机方法开发一个基于物理的模型，用于预测包含数十亿颗粒的系统中的颗粒流。当前用于非球形颗粒的最先进的离散单元方法限于少于一百万个颗粒。 相比之下，一杯沙子含有大约1亿个颗粒。为了实现这一目标，该项目将开发高保真模拟，捕捉碰撞粒子的动力学，以构建大规模系统的随机模型。将进行离散元模拟，以确定非球形粒子在碰撞过程中如何散射，并重新分配旋转和平移能量。然后将使用机器学习工具来构建概率分布函数，将粒子的碰撞前状态与碰撞后状态联系起来。然后，概率分布函数将被纳入直接模拟蒙特卡罗求解器，该求解器可以模拟包含数十亿粒子的系统的动力学。为了验证随机模型，将首先进行比较，以确定性的离散元模拟相对较小的规模的系统。随机模型的准确性，然后将评估较大规模的系统，通过比较结果与现有的实验数据在文献中，并从内部测试的数据。除了捕捉由于粒子形状效应而产生的复杂物理现象外，该项目还将创建一个框架，用于改进对粒子磨损和团聚等其他复杂现象的预测。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Flows involving dense suspensions of particles in gas, which are known as granular flows, are common in nature and industry. In many cases of practical importance, the suspended particles have an irregular shape (i.e., non-spherical), which makes predicting the flow behavior of the suspension especially challenging with currently available methods. As a result, the design of many particulate processes often relies on costly empirical trial-and-error testing. This CAREER project will develop a physics-based stochastic model that accounts for irregular particle shapes to predict particle dynamics more accurately in large-scale systems. Results of the project will be useful in extending granular flow theory for idealized spherical particles to more realistic granular media and in providing new solutions to technical challenges that occur in particle technology. The project will involve research training for graduate and undergraduate students and will prepare them for possible careers involving particle technology. The research team will participate with the Purdue Engineering Outreach club to bring demonstrations of particulate flows for K-12 students in local schools.The goal of this CAREER project is to use stochastic methods to develop a physics-based model for predicting particle flows in systems containing billions of particles. Current state-of-the-art discrete element methods for non-spherical particles are limited to fewer than one million particles. By comparison, a single cup of sand contains approximately 100 million particles. To achieve this goal, the project will develop high-fidelity simulations that capture the dynamics of colliding particles to construct a stochastic model for large-scale systems. Discrete element simulations will be performed to determine how non-spherical particles scatter during collisions and redistribute rotational and translational energies. Machine learning tools will then be employed to build probability distribution functions that relate the pre-collision to the post-collision states of particles. The probability distribution functions will then be incorporated into a direct simulation Monte Carlo solver that can simulate the dynamics of systems containing billions of particles. To validate the stochastic model, comparisons will first be made to deterministic discrete element simulations for relatively small-scale systems. The accuracy of the stochastic model will then be assessed for larger scale systems by comparing results with available experimental data in the literature and with data from in-house tests. In addition to capturing the complex physics that arise due to particle shape effects, the project will create a framework for improving predictions of other complex phenomena such as particle attrition and agglomeration.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.