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arXiv 1003.4331physics.flu-dynphysics.comp-ph
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槽道湍流集中源标量混合的概率密度函数模拟

Probability density function modeling of scalar mixing from concentrated sources in turbulent channel flow

J. Bakosi, P. Franzese, Z. Boybeyi

摘要:

本文用概率密度函数方法研究了槽道湍流中集中源被动标量的弥散问题。速度、湍流频率和标量浓度的联合概率密度函数由大量的拉格朗日粒子表示。随机近壁PDF模型结合了广义Langevin模型的Haworth和波普与德宾的椭圆松弛的方法,提供一个数学上精确的处理对流和粘性输运与非局部表示的近壁雷诺应力各向异性。壁的存在是通过在不使用阻尼或壁函数的情况下对颗粒施加无滑移和不渗透性条件来实现的。湍流时间尺度上的信息提供的伽马分布模型的van Slooten等人。两种不同的微观混合模型进行了比较,将小尺度混合的效果上的标量传输:广泛使用的相互作用与交换的平均值(IEM)和交换的条件平均值(IECM)模型的相互作用。在Re_τ=1080时,基于摩擦速度和通道半宽度,将单点速度和浓度统计与直接数值模拟和实验数据进行了比较。联合模型准确地再现了各种各样的条件和无条件的统计数据在物理和组成空间。

英文摘要:

Dispersion of a passive scalar from concentrated sources in fully developed turbulent channel flow is studied with the probability density function (PDF) method. The joint PDF of velocity, turbulent frequency and scalar concentration is represented by a large number of Lagrangian particles. A stochastic near-wall PDF model combines the generalized Langevin model of Haworth & Pope with Durbin's method of elliptic relaxation to provide a mathematically exact treatment of convective and viscous transport with a non-local representation of the near-wall Reynolds stress anisotropy. The presence of walls is incorporated through the imposition of no-slip and impermeability conditions on particles without the use of damping or wall-functions. Information on the turbulent timescale is supplied by the gamma-distribution model of van Slooten et al. Two different micromixing models are compared that incorporate the effect of small scale mixing on the transported scalar: the widely used interaction by exchange with the mean (IEM) and the interaction by exchange with the conditional mean (IECM) model. Single-point velocity and concentration statistics are compared to direct numerical simulation and experimental data at Re_τ=1080 based on the friction velocity and the channel half width. The joint model accurately reproduces a wide variety of conditional and unconditional statistics in both physical and composition space.

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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