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

职业：使用随机技术来理解和预测非球形颗粒的流动

• 批准号: 2145871
• 负责人: Aaron Morris
• 金额: $ 54.37万
• 依托单位: Purdue University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-05-01 至 2027-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2145871&HistoricalAwards=false
• 关键词: CAREER; Using; Stochastic; Techniques; Understand

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.

气体中涉及颗粒颗粒的密集悬浮液的流（称为颗粒流）在自然界和工业中很常见。 在许多实际重要性的情况下，悬浮粒子具有不规则的形状（即非球形），这使得预测悬架的流动行为特别具有挑战性。 结果，许多颗粒过程的设计通常依赖于昂贵的经验试验和错误测试。 该职业项目将开发基于物理的随机模型，该模型解释了不规则的粒子形状，以在大规模系统中更准确地预测粒子动态。 该项目的结果将有助于将理想化的球形粒子扩展到更现实的颗粒介质，并为粒子技术中发生的技术挑战提供新的解决方案。 该项目将涉及研究生和本科生的研究培训，并将为涉及粒子技术的可能职业做好准备。 研究团队将参加普渡大学工程外展俱乐部，为当地学校的K-12学生带来颗粒流的演示。该职业项目的目标是使用随机方法开发基于物理的模型，以预测包含数十亿个颗粒的系统中的粒子流。非球形颗粒的当前最新离散元素方法仅限于100万个颗粒。 相比之下，一杯沙子包含约1亿个颗粒。为了实现这一目标，该项目将开发高保真模拟，以捕获碰撞粒子的动态，以构建大型系统的随机模型。将进行离散的元素模拟，以确定在碰撞期间非球形颗粒以及重新分布旋转和翻译能的散射。然后，将使用机器学习工具来构建概率分布功能，以将前汇合与粒子的碰撞状态相关联。然后，将将概率分布函数纳入直接模拟蒙特卡洛求解器中，以模拟包含数十亿个颗粒的系统的动力学。为了验证随机模型，将首先进行比较，以确定相对较小的系统的确定性离散元素模拟。然后，将通过将结果与文献中的可用实验数据以及内部测试的数据进行比较，评估随机模型的准确性。除了捕获由于粒子形状效应而产生的复杂物理外，该项目还将创建一个框架，以改善对其他复杂现象（例如粒子流失和凝聚）的预测。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的审查标准来通过评估来获得支持的。