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arXiv 1901.09650physics.flu-dyn
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持续加速度解缠结拉格朗日turbulence

Persistent accelerations disentangle Lagrangian turbulence

Lukas Bentkamp, Cristian C. Lalescu, Michael Wilczek

摘要:

湍流中的粒子在长时间的静止之间经常遇到极端的加速度。极端事件的发生与涡度丝等强流结构的间歇性空间分布密切相关。流动条件的这种混合历史导致非常复杂的颗粒统计,具有明显的尺度依赖性,这是湍流非平衡统计力学的主要挑战之一。在这里,我们介绍了持久的拉格朗日加速度的概念,量化的平方粒子加速度粗粒度的粘性时间尺度。调节拉格朗日粒子数据从模拟这个粗粒度的加速度,我们发现非常简单,接近高斯统计的雷诺数范围。这使得将复杂的粒子统计分解为更简单的子集合成为可能。基于这一观察,我们开发了一个全面的理论框架,拉格朗日单粒子统计,捕捉加速度,速度增量以及单粒子分散。

英文摘要:

Particles in turbulence frequently encounter extreme accelerations between extended periods of quiescence. The occurrence of extreme events is closely related to the intermittent spatial distribution of intense flow structures such as vorticity filaments. This mixed history of flow conditions leads to very complex particle statistics with a pronounced scale dependence, which presents one of the major challenges on the way to a non-equilibrium statistical mechanics of turbulence. Here, we introduce the notion of persistent Lagrangian acceleration, quantified by the squared particle acceleration coarse-grained over a viscous time scale. Conditioning Lagrangian particle data from simulations on this coarse-grained acceleration, we find remarkably simple, close-to-Gaussian statistics for a range of Reynolds numbers. This opens the possibility to decompose the complex particle statistics into much simpler sub-ensembles. Based on this observation, we develop a comprehensive theoretical framework for Lagrangian single-particle statistics that captures the acceleration, velocity increments as well as single-particle dispersion.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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