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

合作研究：湍流中惯性粒子对动力学的综合研究

• 批准号: 0967349
• 负责人: Lance Collins
• 金额: $ 20万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2010
• 资助国家: 美国
• 起止时间: 2010-07-15 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0967349&HistoricalAwards=false
• 关键词: Collaborative; Research; Integrated; investigation; inertial

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.

我们提出了一个综合的实验和数值研究的惯性粒子在各向同性湍流的动力学。实验工作将在一个新的足球湍流设施中进行，该设施将作为该项目的一部分建造。新设施将能够产生雷诺数（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的空心玻璃球，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，我们将在单次和双次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，我们将进行直接数值模拟（DNS），将用于：（i）推进DHPIV技术;和（ii）补充实验测量。智力优点：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）惯性粒子的两粒子分散;和（ii）惯性粒子碰撞率，两者都是第一次。DHPIV将捕获位置和相对速度统计数据，通过运动学关系，这些数据将用于量化分散率和碰撞核，作为粒子参数（斯托克斯数）和雷诺数的函数。新的设备，加上明智的粒子选择，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。目前的算法对于不一定跟随流动或保持高度相关的惯性粒子不起作用。在DNS的帮助下，我们将开发一种新的匹配算法的基础上扫描图像之间的时间间隔。 DNS也将在这项研究中得到推进。我们目前的算法是能够执行10243模拟我们的32个节点的集群。然而，为了匹配所提出的实验的条件，我们必须提高分辨率。我们将修改我们的代码的数据结构，以便利用在三维快速傅立叶变换的最新发展。新的代码将能够在德克萨斯州高级计算中心的100甚至1000个处理器上运行，实现20483次模拟和500雷诺数。这样，我们将继续我们的传统，使定量比较的实验。此外，DNS产生更多的信息比实验的流场，以及允许我们研究拉格朗日统计。我们将通过这些研究来验证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。更广泛的影响：湍流中离散颗粒的运动对于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碳烟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，以使这些预测正确。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，无论是实验还是计算。本提案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理论理解。我们的方法是非常规的，因为我们混合DNS和实验完全。事实上，这项工作的优势是我们能够在DNS和实验之间进行定量比较。我们每周通过视频会议开会，全面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活动。PI在其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中招募和辅导妇女和代表性不足的少数群体，在社区内开展外联活动，以及在国家科学基金会组织一系列高知名度的讲习班，旨在鼓励代表性不足的少数群体进入学院。