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

作为污染物、化学反应物和颗粒物有效扩散的介质，湍流流动的特性在许多环境和工程应用中是至关重要的。这项建议的目标是追求在湍流中污染物扩散研究的基本进展，重点是在对湍流物理的基本理解和改进的物理现实的预测工具的开发之间的几个关键方面。研究将包括通过使用先进的网络基础设施实现的大规模数值模拟的紧密耦合的组合，以及使用大规模模拟数据库开发拉格朗日随机模拟的新扩展。这项工作的学术价值包括在时间上反向跟踪拉格朗日流体元素轨迹的独特挑战，这将为将多粒子群上的轨迹数据与流动的局部空间结构联系起来提供新的见解，在局部空间结构中，强烈的局部变形可以发展，特别是在高雷诺数的条件下。当结合布朗运动中扩散分子相对于流体的轨迹时，该方法还将粒子路径直接连接到环境中典型局部污染源引起的被动标量波动的统计。有限的粒子惯性和重力的影响也将同样被考虑在内。在高性能计算、湍流理论和建模方面表现出高度协同作用和专业知识的研究人员将合作开发新的随机模型来解释这些影响。作为更广泛的影响，该项目将在环境科学中的一个主要流体动力学问题的建模方面提供亟需的改进。预计这项工作将改进的湍流扩散模型不仅适用于大气空气质量，而且还适用于有毒物质的意外或恐怖主义排放(高阶矩很重要)、农业中的昆虫行为(向后追踪模仿基本的生物体行为)以及种子和其他气溶胶物质的风力扩散(惯性很重要)。除了培养一名博士生接触资深和国际知名科学家的专业知识和观点外，该项目还将在佐治亚理工学院为从全国申请者库中挑选的本科生提供特殊的暑期实习机会。后一种安排将通过授予美国科学促进会的分奖来管理，该协会一直非常积极地从这一代表性不足的人口中招收和培养学生。

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.