Finite-size particles in homogeneous turbulence: a numerical study

我们建议通过直接数值模拟（DNS）研究的基本机制，在稀分散相（由固体颗粒组成）和均匀的湍流（或自诱导）流受到重力场之间的相互作用。所考虑的两相流状态的特征在于颗粒雷诺数为O（100），颗粒直径相当于或大于最小流量尺度。因此，公共点粒子近似不直接适用。因此，我们完全解决了相界面以及所有相关的流动尺度。建议的流动配置是统计上均匀的，由初始各向同性的湍流场或重颗粒被添加到其中的环境流体。这种情况是例如在大气（云）或化学工程过程中遇到的流动条件的理想化。在这个项目中产生的数据（流场和颗粒运动）的分析将指导以下基本问题：如何在湍流背景流的沉降速度受有限尺寸，有限雷诺数和集体效应？颗粒诱导湍流增强/减弱的机制是什么？问题参数如何影响分散相的空间分布？重粒子如何改变现有的流动结构/产生新的结构？可以预期，所提出的研究结果将进一步促进我们对颗粒流动动力学的理解。此外，获得的见解将有利于未来的努力，以改善现有的工程目的的模型，这种流动。

We propose to study via direct numerical simulation (DNS) the basic mechanisms involved in the interaction between a dilute dispersed phase (consisting of solid particles) and a homogeneous turbulent (or self-induced) flow subjected to a gravitational field. The two-phase flow regime under consideration can be characterized by particle Reynolds numbers of O(100) and particle diameters comparable to or larger than the smallest flow scales. Therefore, the common point-particle approximation is not directly applicable. As a consequence, we fully resolve the phase interfaces as well as all relevant flow scales. The proposed flow configuration is statistically homogeneous, consisting either of an initially isotropic turbulent field or of ambient fluid to which heavy particles are added. This situation is an idealization of flow conditions encountered e.g. in the atmosphere (clouds) or in chemical engineering processes. The analysis of the data generated in this project (flow field and particle motion) will be guided by the following fundamental questions: How is the settling velocity in turbulent background flow affected by finite-size, finite-Reynolds-number and collective effects? What are the mechanisms of particle-induced turbulence enhancement/attenuation? How is the spatial distribution of the disperse phase influenced by the problem parameters? How do heavy particles modify existing flow structures/generate new structures? It can be expected that the results of the proposed research will further promote our understanding of particulate flow dynamics. Moreover, the insight gained will benefit future efforts to improve existing engineering-purpose models of such flows.