Simulation and Modeling of Turbulent Dispersion: Backward Tracking, Molecular Dispersion and Particle Inertia

湍流色散的模拟和建模：向后跟踪、分子色散和粒子惯性

• 批准号: 1235906
• 负责人: Pui-Kuen Yeung
• 金额: $ 29.37万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-15 至 2016-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1235906&HistoricalAwards=false
• 关键词: Simulation; Modeling; Turbulent; Dispersion; Backward

1235906YeungThe characteristic of turbulent fluid flow as an agent of efficient dispersion of contaminants, chemical reactants and particulate matter in general is of critical importance in many environmental and engineering applications.The objectives of this proposal are to pursue fundamental advances in the study of contaminant dispersion in turbulent flows, with emphasis on several aspects crucial in the linkage between basic understanding of turbulence physics and the development of the predictive tools of improved physical realism.The research will involve a tightly-coupled combination of large-scale numerical simulations enabled by the use of advanced cyberinfrastructure, and the development of novel extensions of Lagrangian stochastic modeling using a massive simulation database. The intellectual merits of this work include the unique challenges of tracking Lagrangian fluid element trajectories backwards in time, which will provide new insights in relating the trajectory data on multi-particle clusters to the local spatial structure of the flow, where intense local deformation can develop especially in conditions of high Reynolds number. When coupled with trajectories of diffusing molecules in Brownian motion relative to the fluid, this approach also connects particle paths directly to the statistics of passive scalar fluctuations arising from typically localized sources of pollutants in the environment.The effects of finite particle inertia and gravitational forces will likewise be addressed. New stochastic models accounting for these effects will be developed in collaboration between researchers with demonstrated high synergism and expertise in high-performance computing, turbulence theory and modeling.As broader impacts, this project will provide much-needed improvements in the modeling of a major fluid dynamics problem in environmental science. Improved models of turbulent dispersion expected from this work will be applicable not only to atmospheric air quality but also to accidental or terrorism-driven discharge of toxic material (where higher-order moments are important), insect behavior in agriculture (where backward tracing mimics essential organism behaviors) and windborne dispersion of seeds and other aerosol-like material (where inertia is important). Besides training a PhD student with exposure to the expertise and perspective of a senior and internationally-respected scientist, this project will provide special summer internships at Georgia Tech for undergraduate students with disabilities to be selected from a national pool of applicants. The latter arrangement will be administered via a subaward to the American Association for the Advancement of Science, which has been very active in recruiting and nurturing students from this under-represented population.

1235906 Yeung，湍流流动的特征是有效分散污染物，化学反应物和一般物质在许多环境和工程应用中至关重要的重要性。该提议的目标是追求基本进步，在构成湍流的污染物方面的污染物中的基本进步，并在湍流中的构成方面的污染物中，并且构成了这些方面的范围，并且在湍流方面的发展方面，该方面的发展方面是构成的，并且构成了范围的范围，并且构成了范围的范围，并且在湍流方面的发展方面的构成方面的构成方面的链接，并且是构成范围的范围。改进的物理现实主义的预测工具将涉及通过使用先进的网络基础结构来实现大规模数值模拟的紧密耦合组合，以及使用大量模拟数据库的Lagrangian随机建模的新型扩展发展。这项工作的智力优点包括跟踪拉格朗日流体元素轨迹的独特挑战，这将为将多粒子簇的轨迹数据与流量的局部空间结构相关联，这将提供新的见解，在此，强烈的局部变形可以在高雷诺数的条件下尤其可以发展。当与流体相对于流体中的布朗运动中扩散分子的轨迹结合时，这种方法还将粒子路径直接连接到有限粒子惯性效果和引力力在环境中典型的局部污染物来源引起的被动标量波动的统计。在研究人员之间的合作中，将开发出新的随机模型，这些模型将在高性能计算，湍流理论和建模方面表现出很高的协同作用和专业知识。随着更广泛的影响，该项目将在环境科学领域的主要流体动态问题的建模方面提供急需的改进。这项工作预期的动荡分散模型的改进模型不仅适用于大气空气质量，还适用于偶然或以恐怖主义驱动的有毒材料的排放（高阶时刻很重要），农业中的昆虫行为（落后追踪模仿基本的有机体行为）和种子和其他类似Airosol的材料（在某些情况下含量）。除了培训一名博士生以接触高级和国际尊敬的科学家的专业知识和观点外，该项目还将为佐治亚理工学院的特殊暑期实习，以为残疾的本科生提供从国家申请人群中选出的残疾人。后一种安排将通过美国科学发展协会的子宣告来管理，该协会非常积极地招募和培养这个代表性不足的人群的学生。