arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1306.6388physics.flu-dyn
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湍流双颗粒弥散中的扩散近似

The Diffusion Approximation in Turbulent Two-Particle Dispersion

Gregory L. Eyink, Damien Benveniste

摘要:

我们解决了流体粒子对统计的逆问题:我们表明,时间序列的概率密度函数(PDF)的分离可以精确地再现通过求解扩散方程与适当的时间依赖性扩散。扩散率张量由条件拉格朗日速度结构函数的时间积分给出,由PDF的比率加权。流体动力学湍流的物理假设(扫描,短记忆,平均场)产生更简单的积分公式,包括一个Kraichnan和Lundgren。我们评估后者使用时空数据库从数值Navier-Stokes解驱动湍流。这种扩散理论再现PDF的以及在均方根分离,但增长率的均方色散是高估由于忽略了记忆效应。更一般的应用,我们的方法勾勒。

英文摘要:

We solve an inverse problem for fluid particle pair-statistics: we show that a time sequence of probability density functions (PDF's) of separations can be exactly reproduced by solving the diffusion equation with a suitable time-dependent diffusivity. The diffusivity tensor is given by a time-integral of a conditional Lagrangian velocity structure-function, weighted by a ratio of PDF's. Physical hypotheses for hydrodynamic turbulence (sweeping, short memory, mean-field) yield simpler integral formulas, including one of Kraichnan and Lundgren. We evaluate the latter using a spacetime database from a numerical Navier-Stokes solution for driven turbulence. This diffusion theory reproduces PDF's well at rms separations, but growth rate of mean-square dispersion is overpredicted due to neglect of memory effects. More general applications of our approach are sketched.

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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