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

本文对惯性粒子在各向同性湍流中的动力学进行了综合的实验和数值研究。实验工作将在一个新的足球湍流设施中进行，该设施将作为该项目的一部分建造。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体，并使用先进版本的数字全息粒子图像测速仪(DHPIV)对这些球体进行成像。使用独特的光学设置，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，我们还将进行直接数值模拟，用于：(I)推进DHPIV技术；(Ii)补充实验测量。智力优势：我们建议在实验室和硅胶中进行的测量将使我们能够量化两个重要的气溶胶过程：(I)惯性粒子的两个粒子弥散；以及(Ii)惯性粒子碰撞速率，这两个过程都是第一次。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弥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，再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幅图像中粒子的准确配对。目前的算法对不一定跟随流动或保持高度相关的惯性粒子不起作用。在域名系统的帮助下，我们将开发一种新的基于扫描图像之间的时间推移的匹配算法。在这项研究下，域名系统也将得到推进。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，为了与拟议实验的条件相匹配，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快速傅立叶变换的最新发展。新代码将能够在德克萨斯先进计算中心的100英寸S甚至1000英寸S处理器上运行，实现20483次模拟和500雷诺数。这样，我们将延续我们与实验进行定量比较的传统。此外，与关于流场的实验相比，数值模拟得到了更多的信息，并且使我们能够研究拉格朗日统计。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，并推进我们对颗粒扩散和碰撞的理论理解。波及影响：离散颗粒在湍流中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尘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，我们面临着描述颗粒的弥散和碰撞特性的挑战，以便正确地进行这些预测。从历史上看，我们对湍流的理解与我们测量关键变量的能力密切相关，无论是通过实验还是通过计算。这项提议的目标是衡量统计量，使我们能够量化这两个重要的气溶胶过程。这些结果将激发我们小组内部和其他地方令人兴奋的新的理论理解。我们的方法是非常规的，因为我们将域名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域名系统和实验之间进行定量比较。我们通过视频会议每周开会，彻底讨论工作的方方面面。这为学生提供了一个丰富的环境，他们在高水平上接触到了所有的活动。PI在他们的职业生涯中一直积极参与外展活动。最近的活动包括在各自的机构招聘和指导妇女和任职人数不足的少数群体，在社区内开展外联活动，并在国家科学基金会组织了一系列备受瞩目的讲习班，目的是鼓励任职人数不足的少数群体进入学院。

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.