Collaborative Research: Integrated investigation of inertial particle pair dynamics in turbulence

我们提出了一种综合实验和数值研究惯性粒子在各向同性湍流中的动力学。实验工作将在一个新的足球湍流设施中进行，该设施将作为项目的一部分而建设。新设备将能够产生雷诺数（基于泰勒微尺度）为500的各向同性湍流。在这个流程中，我们将引入金属涂层中空玻璃球，并使用先进版本的数字全息粒子图像测速（DHPIV）对这些球体进行成像。利用独特的光学装置，我们将在单曝光和双曝光模式下对粒子进行成像，以获得位置和速度统计数据。此外，我们将执行直接数值模拟（DNS），将用于：(i)推进DHPIV技术；（ii）补充实验测量。智力价值：我们建议在实验室和计算机上进行的测量将使我们能够量化两个重要的气溶胶过程：(i)惯性粒子的双粒子弥散；（ii）惯性粒子碰撞率，均为首次。DHPIV将捕获位置和相对速度统计数据，并通过运动学关系将这些数据用于量化色散率和碰撞核作为粒子参数（斯托克斯数）和雷诺数的函数。新的设备，加上对粒子的明智选择，将使我们能够隔离每个参数的影响。速度测量的一个关键方面是两个图像中粒子的精确配对。目前的算法不能很好地适用于不一定跟随流动或保持高度相关的惯性粒子。在DNS的帮助下，我们将开发一种新的基于扫描图像间时间间隔的匹配算法。DNS也将在这项研究中得到改进。我们目前的算法能够在我们的32节点集群上执行10243次模拟。然而，为了符合所提出的实验条件，我们必须提高分辨率。我们将修改代码的数据结构，以便利用3D快速傅里叶变换的最新发展。新代码将能够在德克萨斯高级计算中心的100甚至1000个处理器上运行，支持20483个模拟和500个雷诺数。通过这种方式，我们将继续我们与实验进行定量比较的传统。此外，DNS比流场实验提供了更多的信息，并允许我们研究拉格朗日统计。我们将进行这些研究，以检验我们在实验分析中所做的假设，并推进我们对粒子分散和碰撞的理论理解。更广泛的影响：在湍流中离散粒子的运动对广泛的工程流动和自然流动都具有重要意义。从了解柴油发动机中烟灰颗粒的生长和氧化之间的竞争，到量化这些颗粒对全球气候的影响，我们面临的挑战是描述颗粒的分散和碰撞特性，以获得正确的预测。从历史上看，我们对湍流的理解一直与我们测量关键变量的能力密切相关，无论是实验还是计算。这项提议的目标是测量统计量，使我们能够量化这两个重要的气溶胶过程。这些结果将在我们小组和其他地方激发令人兴奋的新的理论理解。我们的方法是非常规的，因为我们将DNS和实验完全混合在一起。事实上，这项工作的一个优势是我们能够在DNS和实验之间进行定量比较。我们每周通过视频会议开会，深入讨论工作的各个方面。这为学生提供了一个丰富的环境，他们在高水平上接触到所有的活动。在他们的职业生涯中，pi一直在大力参与外联活动。最近的活动包括在各自的机构征聘和指导妇女和代表性不足的少数民族，在社区内进行外联，以及在国家科学基金会组织一系列引人注目的讲习班，旨在鼓励代表性不足的少数民族进入学院。

We propose an integrated experimental and numerical investigation of the dynamics of inertial particles in isotropic turbulence. The experimental work will be performed in a new soccer ball turbulence facility that will be built as part of the project. The new facility will be capable of producing isotropic turbulence with a Reynolds number (based on the Taylor microscale) of 500. Into this flow, we will introduce metal coated hollow glass spheres and image those spheres using an advanced version of digital holographic particle image velocimetry (DHPIV). Using a unique optical setup, we will image the particles in single and double exposure modes to obtain position and velocity statistics. Additionally, we will perform direct numerical simulations (DNS) that will be used to: (i) advance the DHPIV technique; and (ii) complement the experimental measurements.Intellectual Merit:The measurements we propose to make in the lab and in silico will allow us to quantify two important aerosol processes: (i) two-particle dispersion of inertial particles; and (ii) inertial particle collision rates, both for the first time. DHPIV will capture position and relative velocity statistics, and through kinematic relationships these data will be used to quantify the dispersion rate and the collision kernel as a function of the particle parameters (Stokes numbers) and Reynolds number. The new facility, combined with a judicious choice of particles, will allow us to isolate the effects of each parameter. A crucial aspect of the velocity measurement is the accurate pairing of particles in the two images. Current algorithms do not work well for inertial particles that don't necessarily follow the flow or remain highly correlated. With the aid of DNS, we will develop a new matching algorithm based on sweeping the time lapse between images. The DNS too will be advanced under this study. Our current algorithm is capable of performing 10243 simulations on our 32-node cluster. However, in order to match the conditions of the proposed experiments, we must increase the resolution. We will modify the data structure of our code so as to take advantage of recent developments in the 3D fast Fourier transform. The new code will be able to run on 100's and even 1000's of processors on the Texas Advanced Computing Center, enabling 20483 simulations and Reynolds numbers of 500. In this way, we will continue our tradition of making quantitative comparisons with the experiments. Additionally, DNS yields more information than the experiments about the flow field, as well as allows us to study Lagrangian statistics. We will perform these studies to test assumptions we have made in the analysis of the experiments, as well as to advance our theoretical understanding of particle dispersion and collision.Broader Impacts:The motion of discrete particles in a turbulent fluid is of great significance to a broad range of engineering flows as well as natural flows. From understanding the competition between growth and oxidation of soot particles in a diesel engine, to quantifying the impact these particles have on the global climate, we are challenged to describe the dispersive and collisional properties of particles in order to get these predictions right. Historically our understanding of turbulence has gone hand-in-hand with our ability to measure the key variables, either experimentally or computationally. The goal of this proposal is to measure the statistical quantities that will allow us to quantify these two important aerosol processes. These results will stimulate an exciting new theoretical understanding, both within our group and elsewhere. Our approach is unconventional in that we intermingle DNS and experiment completely. Indeed, a strength of this work has been our ability to make quantitative comparisons between DNS and experiments. We meet weekly via videoconference to thoroughly discuss all aspects of the work. This provides a rich environment for students, who are exposed, at a high level, to all of the activities. The PIs have been heavily involved with outreach throughout their careers. Recent activities include recruitment and mentoring of women and underrepresented minorities at their respective institutions, outreach within the community, and organization of a series of high profile workshops at the NSF directed towards encouraging underrepresented minorities into the academy.