Stochastic Vortex Structure Model for Adhesive Particles in Turbulent Flows

湍流中粘性颗粒的随机涡结构模型

• 批准号: 1332472
• 负责人: Jeffrey Marshall
• 金额: $ 28.46万
• 依托单位: University of Vermont & State Agricultural College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1332472&HistoricalAwards=false
• 关键词: Stochastic; Vortex; Structure; Model; Adhesive

Marshall, Jeffrey 1332472Particles present in turbulent flows are transported because of a large-scale diffusive process caused by exchange of fluid between turbulent eddies and by a small-scale anti-diffusive process. The latter (which we will refer to as particle dispersion) is associated with the tendency of heavy particles to be thrown out of turbulent eddies, forming high-concentration particle sheets in the interstitial region between the turbulent eddies. This small-scale heterogeneity is known to cause fluctuations in the particle concentration of several orders of magnitude, which in turn have a dramatic effect on processes that involve particle collision and agglomeration. While stochastic Lagrangian models (SLMs) have been shown to accurately simulate large-scale particle diffusion, these methods lack the spatial correlation necessary to account for small-scale concentration heterogeneity. The work proposed here will replace random forcing used in stochastic Lagrangian models with a spatially-correlated stochastic forcing based on approximating the turbulent eddies by a set of random vortex structures.Intellectual Merit :The proposed research will develop a stochastic vortex structure (SVS) model for simulating collision and agglomeration of adhesive particles in turbulent flows. The SVS model will generate a turbulent velocity field with correct spatial correlation over a length scale interval ranging between the energy-containing turbulence integral scale and a minimum scale that is associated with the length scale at which the eddy Stokes number is equal to unity. The proposed stochastic model will be validated by comparison to direct numerical simulations of particle agglomerate formation in homogeneous turbulence and turbulent shear flows. Simulations will be performed using a discrete element method for adhesive particles, a fast multipole method for solution of the vorticity-induced velocity field, and a pseudospectral method for direct numerical simulation (DNS) of turbulence. Broader Impacts :Adhesive particles dominate critical problems ranging from bioengineering systems (blood flow, GI flows, microorganism suspensions), manufacturing processes (electrospray, electrocoating, dust mitigation), energy generation (pulverized coal combustion, biomass combustion, ash mitigation), environmental processes (sediment transport, volcanic processes, filtration processes), and nano- and microscale technology (nanoparticle dispersion, nanotube alignment, self-assembly). The proposed research will develop and validate a novel structurally-based stochastic model that will enable an entirely new simulation approach for a wide range of problems involving adhesive particles.The research will be shared with a minority-serving institution via an existing arrangement between University of Vermont and City University of New York.

湍流中存在的颗粒是由于湍流漩涡之间流体交换引起的大规模扩散过程和小规模的反扩散过程而被输送的。后者（我们称之为粒子弥散）与重粒子被抛出湍流漩涡的趋势有关，在湍流漩涡之间的间隙区域形成高浓度的粒子片。众所周知，这种小尺度的非均质性会引起粒子浓度几个数量级的波动，进而对涉及粒子碰撞和聚集的过程产生巨大影响。虽然随机拉格朗日模型（SLMs）已被证明可以准确地模拟大尺度颗粒扩散，但这些方法缺乏解释小尺度浓度异质性所需的空间相关性。本文提出的工作将取代随机拉格朗日模型中使用的随机强迫，取而代之的是基于一组随机涡结构近似湍流漩涡的空间相关随机强迫。智力优势：该研究将建立一个随机涡结构（SVS）模型，用于模拟湍流中粘附颗粒的碰撞和团聚。在含能湍流积分尺度与涡斯托克斯数等于1的长度尺度相关联的最小尺度之间的长度尺度区间内，SVS模型将生成具有正确空间相关性的湍流速度场。所提出的随机模型将通过与均匀湍流和湍流剪切流中颗粒凝聚形成的直接数值模拟进行比较来验证。模拟将使用粘接粒子的离散元方法、求解涡度诱导速度场的快速多极方法和湍流直接数值模拟（DNS）的伪谱方法。更广泛的影响：粘合剂颗粒主导着各种关键问题，包括生物工程系统（血液流动、胃肠道流动、微生物悬浮液）、制造过程（电喷雾、电涂层、粉尘缓解）、能源产生（煤粉燃烧、生物质燃烧、粉尘缓解）、环境过程（沉积物运输、火山过程、过滤过程）以及纳米和微尺度技术（纳米颗粒分散、纳米管排列、自组装）。拟议的研究将开发和验证一种新的基于结构的随机模型，该模型将为涉及粘合剂颗粒的广泛问题提供一种全新的模拟方法。这项研究将通过佛蒙特大学和纽约城市大学之间的现有安排与一个少数民族服务机构共享。

项目成果

Local Stochastic Vortex Structure Method for Synthetic Turbulence Computation in Flight Simulators

• DOI: 10.2514/1.j057329
• 发表时间: 2019-02
• 期刊: AIAA Journal
• 影响因子: 2.5
• 作者: J. Marshall

A stochastic vortex structure method for interacting particles in turbulent shear flows

• DOI: 10.1063/1.5007743
• 发表时间: 2018-01
• 期刊: Physics of Fluids
• 影响因子: 4.6
• 作者: F. F. Dizaji-F.;J. Marshall;J. R. Grant

Exponential scaling in early-stage agglomeration of adhesive particles in turbulence

• DOI: 10.1103/physrevfluids.4.024304
• 发表时间: 2019-02-26
• 期刊: PHYSICAL REVIEW FLUIDS
• 影响因子: 2.7
• 作者: Chen, Sheng;Li, Shuiqing;Marshall, Jeffrey S.
• 通讯作者: Marshall, Jeffrey S.

The role of fluid turbulence on contact electrification of suspended particles

流体湍流对悬浮颗粒接触带电的作用

• DOI: 10.1016/j.elstat.2017.04.011
• 发表时间: 2017
• 期刊: Journal of Electrostatics
• 影响因子: 1.8
• 作者: Jin, Xing;Marshall, Jeffrey S.
• 通讯作者: Marshall, Jeffrey S.

On the significance of two-way coupling in simulation of turbulent particle agglomeration

• DOI: 10.1016/j.powtec.2017.05.027
• 发表时间: 2017-08
• 期刊: Powder Technology
• 影响因子: 5.2
• 作者: F. F. Dizaji-F.;J. Marshall