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Overview of existing Langevin models formalism for heavy particle dispersion in a turbulent channel flow

机译:现有Langevin模型概述湍流通道中重粒子分散的形式主义

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The purpose of the paper is to compare two successful families of stochastic model for the prediction of inertial particles dispersion in a turbulent channel flow. Both models are based on the Langevin equation; nevertheless, they were developed following different paths. The first model considered is named "Drift Correction model (DCM)", and the second one is the "Generalized Langevin Model (GLM)". To examine the capabilities of both models, a comparison of the results predicted by the DCM- and GLM-type dispersion models with those extracted from a Direct Numerical Simulation (DNS) is conducted. In the limit of vanishing particle inertia, both models can accurately predict second-order statistics. It is also noticed, as not expected, that they are very similar when they are written in the same functional form. The comparison has also been conducted with DNS data of a particle-laden channel flow. The comparison of particle statistics (such as concentration, mean and rms particle velocity, third-order particle velocity correlations) shows that both stochastic models give very satisfactory results up to second-order statistics. The DCM- and GLM-type dispersion models studied can capture the main physical mechanisms that govern particle-laden turbulent channel flows. (C) 2016 Elsevier Ltd. All rights reserved.
机译:本文的目的是比较两个成功的随机模型族,以预测湍流通道中的惯性粒子弥散。两种模型均基于Langevin方程。但是,它们是按照不同的方式开发的。所考虑的第一个模型被称为“漂移校正模型(DCM)”,第二个模型被称为“广义Langevin模型(GLM)”。为了检查这两种模型的功能,将DCM和GLM型色散模型预测的结果与从直接数值模拟(DNS)中提取的结果进行了比较。在消失的粒子惯性的极限内,两个模型都可以准确地预测二阶统计量。还应注意,以相同的功能形式编写它们时,它们非常相似。还使用载有粒子的通道流的DNS数据进行了比较。粒子统计数据(例如浓度,均值和均方根粒子速度,三阶粒子速度相关性)的比较表明,两种随机模型给出的结果都非常令人满意,直到二阶统计量为止。所研究的DCM和GLM型色散模型可以捕获控制载有颗粒的湍流通道的主要物理机理。 (C)2016 Elsevier Ltd.保留所有权利。

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