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.

Marshall，Jeffrey 1332472在湍流中存在的粒子是由于湍流涡流之间的流体和小规模的反排除过程而导致的大规模扩散过程。后者（我们将称为粒子分散体）与要从湍流的涡流中抛出的重颗粒的趋势有关，在湍流涡流之间形成了间隙区域的高浓缩粒子表。已知这种小规模的异质性在几个数量级的粒子浓度中引起波动，进而对涉及粒子碰撞和团聚的过程产生巨大影响。尽管已显示随机拉格朗日模型（SLM）可以准确模拟大规模的粒子扩散，但这些方法缺乏考虑小规模浓度异质性所需的空间相关性。这里提出的工作将用空间相关的随机强迫代替随机强迫，基于通过一组随机涡流结构近似湍流涡流的近似涡流的强迫：拟议的研究将开发出一种随机的涡旋结构（SVS），以模拟碰撞碰撞的碰撞碰撞结构（SVS），以模拟碰撞和激进的效果，使其具有粘合的粘合物的粘合物，粘合了粘合的效果。 SVS模型将在长度尺度间隔内生成一个湍流速度场，其空间相关性在含能量的湍流积分量表与与涡流数量等于统一的长度尺度相关的最小尺度之间。通过与均匀湍流和湍流中粒子团聚形成的直接数值模拟相比，将验证所提出的随机模型。将使用用于粘合剂颗粒的离散元素方法，一种用于涡度诱导的速度场解决方案的快速多极方法，以及用于湍流直接数值模拟（DNS）的快速多极方法。更广泛的影响：粘合性颗粒主导着关键问题，包括生物工程系统（血流，GI流动，微生物悬浮液），制造过程（电喷雾，电污染，减轻灰尘），能源产生（粉状煤炭燃烧，燃烧，生物质量，灰分），环境过程 - 固定过程，固定过程，固定过程，塞式工艺，塞式工艺，供应，层次，层次，层次，层次，层次，层次，层次，层次，层次，层次，层次，层面层，繁殖层，繁殖层，繁殖层，繁殖层，繁殖层，繁殖层，繁殖层，层面层面的层面综合层，繁殖。微观技术（纳米颗粒分散，纳米管比对，自组装）。拟议的研究将开发并验证一种基于结构的新型随机模型，该模型将为涉及粘合剂颗粒的各种问题提供全新的模拟方法。该研究将通过佛蒙特大学和纽约市城市大学之间的现有安排与少数派服务机构共享。

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.

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

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.