Gravity-induced settling of many non-spherical particles at intermediate Galileo numbers: a DNS study

许多非球形粒子在中间伽利略数下的重力诱导沉降：一项 DNA 研究

• 批准号: 398061626
• 负责人: Professor Dr. Markus Uhlmann
• 金额: --
• 依托单位: Institut für Hydromechanik (IfH)
• 依托单位国家: 德国
• 项目类别: Research Grants
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/398061626?language=en
• 关键词: Gravity; induced; settling; many; non

The motion of ice particles in the atmosphere, of plankton in the oceans, and of suspended particles in chemical engineering apparatuses are some examples of sedimentation processes where the particle settling speed strongly depends upon their spatial distribution. Considerable progress in our fundamental understanding of suspension dynamics has already been gained through modern laboratory experiments and numerical analysis. However, most studies restrict their attention to spherically-shaped solids, although the majority of real-world applications such as the ones mentioned above involve non-spherical particles. In the present project we propose to study the settling behavior of ensembles of many spheroidal particles (both oblate and prolate) via interface-resolved direct numerical simulation (DNS) at low solid volume fraction and for particle Reynolds numbers of O(100. In this regime the modeling uncertainty of Euler-Lagrange (point-particle) models and of Euler-Euler (two-fluid) models is particularly high, and high-fidelity data for many non-spherical particles is practically non-existent. In the present project we plan to use the large data-set which will be generated in order to answer the following questions: How does particle shape influence the tendency to form wake-induced clusters? What are the consequences for the statistics of the particle motion? What is the impact upon the fluid flow induced by the settling particle ensemble? It can be expected that the thorough data analysis will allow us to make recommendations for reduced-order modelling of settling suspensions involving non-spherical particles.

冰粒在大气中的运动，浮游生物在海洋中的运动，以及化学工程设备中悬浮粒子的运动都是沉积过程的一些例子，其中粒子的沉降速度强烈地依赖于它们的空间分布。通过现代的实验室实验和数值分析，我们对悬架动力学的基本认识已经取得了相当大的进展。然而，大多数研究将他们的注意力限制在球形固体上，尽管大多数现实世界的应用，如上面提到的那些涉及非球形粒子。在本项目中，我们建议通过界面分辨直接数值模拟(DNS)来研究低固相体积分数和颗粒雷诺数为O(100)时许多球形颗粒(扁平和长球形)的系综的沉降行为。在这种情况下，Euler-Lagrange(点-粒子)模型和Euler-Euler(双流体)模型的建模不确定性特别高，许多非球形粒子的高保真数据几乎不存在。在本项目中，我们计划使用将产生的大数据集来回答以下问题：粒子形状如何影响形成尾迹诱导星团的倾向？粒子运动的统计结果是什么？沉降粒子团对流体流动有何影响？可以预期，彻底的数据分析将使我们能够为涉及非球形颗粒的沉降悬浮液的降阶建模提出建议。