Lagrangian Studies of Turbulent Mixing and Dispersion Using High-Resolution Computations

摘要建议编号：CTS-9705678首席研究员：杨这项计划的目标是研究湍流中强化混合和弥散的基本方面。人们普遍认为，分子混合的建模在概率密度函数方法和拉格朗日随机模型中是一个有问题的领域，概率密度函数方法在反应流中具有很大的优势，拉格朗日随机模型用于预测污染物在大气中的扩散。该方法是基于精确输运方程的直接数值模拟，提取单个和多个被动标量(如化学物种或污染物)和分子位移的拉格朗日统计量，并将模型预测与模拟结果直接进行比较。要检查的数据对于文献来说是新的，几乎超出了实验测量的范围。大规模并行计算的最新进展将被用来提供在一系列雷诺数和施密特数范围内的高分辨率模拟数据，这将使人们能够仔细研究相似尺度问题，这些问题对于湍流中标量输运的基本描述至关重要。

ABSTRACT Proposal Number: CTS-9705678 Principal Investigator: Yeung The objectives of this project are to study fundamental aspects of enhanced mixing and dispersion in turbulent flows. It is widely recognized that the modeling of molecular mixing is a problematic area in probability-density-function methods which otherwise hold great advantages for reacting flows, and in Lagrangian stochastic models that are used to predict pollutant dispersion in the atmosphere. The approach is to use direct numerical simulations based on exact transport equations to extract Lagrangian statistics of single and multiple passive scalars (such as chemical species or contaminants) and molecular displacements, and to compare model predictions directly with the simulation results. The data to be examined are novel to the literature, and virtually beyond experimental measurement. The latest advances in massively parallel computing will be exploited to provide high-resolution simulation data over a range of Reynolds and Schmidt numbers, which would allow a careful study of issues of similarity scaling important to the fundamental description of scalar transport in turbulence.