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首页> 外文期刊>International Journal of Multiphase Flow >Prediction of a particle-laden turbulent channel flow: Examination of two classes of stochastic dispersion models
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Prediction of a particle-laden turbulent channel flow: Examination of two classes of stochastic dispersion models

机译:充满粒子的湍流通道流的预测:检验两类随机扩散模型

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摘要

Nowadays, two families of stochastic models are mainly used to predict the dispersion of inertial particles in inhomogeneous turbulent flows. This first one is named "normalized model" and the second one "Generalized Langevin Model (GLM)". Nevertheless, the main differences between the normalized and GLM models have not been thoroughly investigated. Is there a model which is more suitable to predict the particle dispersion in inhomogeneous turbulence? We propose in the present study to clarify this point by computing a particle-laden turbulent channel flow using a GLM-type model, and also a normalized- type model. Particle statistics (such as concentration, mean and rms particle velocity, fluid-particle velocity covariances) will be provided and compared to Direct Numerical Simulation (DNS) data in order to assess the performance of both dispersion models. It will be shown that the normalized dispersion model studied can predict correctly the effect of particle inertia on some dispersion statistics, but not on all. For instance, it was found that the prediction of the particle kinetic shear stress and some components of the fluid-particle covariance is not physically acceptable.
机译:如今,两类随机模型主要用于预测惯性粒子在非均匀湍流中的弥散。第一个被称为“规范化模型”,第二个被称为“广义Langevin模型(GLM)”。但是,归一化模型和GLM模型之间的主要差异尚未得到彻底研究。是否有一个模型更适合预测非均匀湍流中的颗粒扩散?我们在本研究中建议通过使用GLM型模型和归一化型模型计算载有颗粒的湍流通道流来澄清这一点。将提供粒子统计数据(例如浓度,均值和均方根粒子速度,流体-粒子速度协方差),并将其与直接数值模拟(DNS)数据进行比较,以评估两个色散模型的性能。结果表明,所研究的归一化色散模型可以正确预测粒子惯性对某些色散统计量的影响,而不能对所有的色散统计量正确。例如,发现对颗粒动力剪切应力和流体-颗粒协方差的某些成分的预测在物理上是不可接受的。

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