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Intermittency in the relative separations of tracers and of heavy particles in turbulent flows

机译:示踪剂和重颗粒在湍流中相对分离的间歇性

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Results from direct numerical simulations (DNS) of particle relative dispersion in three-dimensional homogeneous and isotropic turbulence at Reynolds number R_eλ ~ 300 are presented. We study point-like passive tracers and heavy particles, at Stokes number St D 0.6, 1 and 5. Particles are emitted from localised sources, in bunches of thousands, periodically in time, allowing an unprecedented statistical accuracy to be reached, with a total number of events for two-point observables of the order of 10~(11). The right tail of the probability density function (PDF) for tracers develops a clear deviation from Richardson's self-similar prediction, pointing to the intermittent nature of the dispersion process. In our numerical experiment, such deviations are manifest once the probability to measure an event becomes of the order of - or rarer than - one part over one million, hence the crucial importance of a large dataset. The role of finite-Reynolds-number effects and the related fluctuations when pair separations cross the boundary between viscous and inertial range scales are discussed. An asymptotic prediction based on the multifractal theory for inertial range intermittency and valid for large Reynolds numbers is found to agree with the data better than the Richardson theory. The agreement is improved when considering heavy particles, whose inertia filters out viscous scale fluctuations. By using the exit-time statistics we also show that events associated with pairs experiencing unusually slow inertial range separations have a non-self-similar PDF.
机译:给出了在雷诺数R_eλ〜300的三维均质和各向同性湍流中颗粒相对分散的直接数值模拟(DNS)结果。我们研究斯托克斯数St D为0.6、1和5的点状无源示踪剂和重粒子。粒子是周期性地从局部源发出的,成千上万个周期性地发出,从而达到前所未有的统计精度,总的来说两点可观测事件的事件数量为10〜(11)。示踪剂的概率密度函数(PDF)的右尾与理查森的自相似预测有明显的偏差,指出了分散过程的间歇性。在我们的数值实验中,一旦测量事件的概率达到一百万分之一(或少于一百万分之一),则这种偏差就很明显,因此,一个大型数据集至关重要。讨论了有限对-雷诺数效应的作用以及成对的间隔越过粘性和惯性范围尺度之间的边界时的相关波动。发现基于多重分形理论的惯性范围间歇性渐近预测方法对大雷诺数有效,并且比理查森理论更符合数据。当考虑重粒子时,一致性得到改善,重粒子的惯性可以滤除粘性水垢波动。通过使用退出时间统计数据,我们还显示了与经历异常缓慢的惯性范围分离的对相关的事件具有非自相似PDF。

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