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arXiv 1607.01709physics.flu-dyn
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拉格朗日滤波密度函数用于基于LES的湍流离散流随机模拟

Lagrangian filtered density function for LES-based stochastic modelling of turbulent dispersed flows

Alessio Innocenti, Cristian Marchioli, Sergio Chibbaro

摘要:

当直接数值模拟(DNS)的计算费用变得过于昂贵时,基于大涡模拟(LES)的欧拉-拉格朗日方法是研究湍流离散流动最有前途和可行的数值工具之一。然而,这种方法的适用性是有限的,如果子网格尺度(SGS)的流动粒子动力学的影响被忽略。在本文中,我们建议考虑到这些影响的拉格朗日随机SGS模型的粒子运动方程。该模型扩展到颗粒负载流的速度过滤密度函数方法最初开发的反应流。基本的过滤密度函数模拟通过拉格朗日蒙特卡罗程序,解决了一组随机微分方程(SDES)沿个别粒子的轨迹。由此产生的模型进行测试的参考情况下,湍流槽流,使用混合算法,其中的流体速度场是由大涡模拟提供的,然后用于提前的SDES的时间。模型的一致性进行评估,在零惯性粒子的限制,当“重复字段”可从欧拉LES和拉格朗日跟踪。惯性粒子的测试进行了检查的模型捕捉粒子的优先浓度和近壁偏析的能力。与DNS为基础的统计相比,我们的结果表明,提高了精度和显着减少的错误的情况下,没有SGS模型中使用的粒子运动方程。

英文摘要:

The Eulerian-Lagrangian approach based on Large-Eddy Simulation (LES) is one of the most promising and viable numerical tools to study turbulent dispersed flows when the computational cost of Direct Numerical Simulation (DNS) becomes too expensive. The applicability of this approach is however limited if the effects of the Sub-Grid Scales (SGS) of the flow on particle dynamics are neglected. In this paper, we propose to take these effects into account by means of a Lagrangian stochastic SGS model for the equations of particle motion. The model extends to particle-laden flows the velocity-filtered density function method originally developed for reactive flows. The underlying filtered density function is simulated through a Lagrangian Monte Carlo procedure that solves for a set of Stochastic Differential Equations (SDEs) along individual particle trajectories. The resulting model is tested for the reference case of turbulent channel flow, using a hybrid algorithm in which the fluid velocity field is provided by LES and then used to advance the SDEs in time. The model consistency is assessed in the limit of particles with zero inertia, when "duplicate fields" are available from both the Eulerian LES and the Lagrangian tracking. Tests with inertial particles were performed to examine the capability of the model to capture particle preferential concentration and near-wall segregation. Upon comparison with DNS-based statistics, our results show improved accuracy and considerably reduced errors with respect to the case in which no SGS model is used in the equations of particle motion.

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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