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APS -70th Annual Meeting of the APS Division of Fluid Dynamics- Event - Lagrangian stochastic modelling in Large-Eddy Simulation of turbulent particle-laden flows

机译:APS-流体动力学APS部门第70届年会-事件-湍流含颗粒流大涡模拟中的拉格朗日随机建模

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Large-Eddy Simulation (LES) in Eulerian-Lagrangian studies of particle-laden flows is one of the most promising and viable approaches when Direct Numerical Simulation (DNS) is not affordable. However applicability of LES to particle-laden flows is limited by the modeling of the Sub-Grid Scale (SGS) turbulence effects on particle dynamics. These effects may be taken into account through a stochastic SGS model for the Equations of Particle Motion (EPM) that extends the Velocity Filtered Density Function method originally developed for reactive flows, to two-phase flows. The underlying filtered density function is simulated through a Lagrangian Monte Carlo procedure, where a set of Stochastic Differential Equations (SDE) is solved along the trajectory of a particle. The resulting Lagrangian stochastic model has been tested for the reference case of turbulent channel flow. Tests with inertial particles have been performed focusing on particle preferential concentration and segregation in the near-wall region: upon comparison with DNS-based statistics, our results show improved accuracy with respect to LES with no SGS model in the EPM for different Stokes numbers. Furthermore, statistics of the particle velocity recover well DNS levels.
机译:当直接数值模拟(DNS)无法负担时,欧拉-拉格朗日研究大粒子模拟(LES)是最有前途和可行的方法之一。但是,LES对子粒子尺度的湍流效应对子动力学的建模受到了限制。可通过用于粒子运动方程(EPM)的随机SGS模型来考虑这些影响,该模型将最初为无功流开发的速度滤波密度函数方法扩展为两相流。通过Lagrangian蒙特卡洛过程模拟了基本的滤波密度函数,其中沿着粒子的轨迹求解了一组随机微分方程(SDE)。对于湍流通道流动的参考情况,已经测试了所得的拉格朗日随机模型。已经对惯性粒子进行了测试,重点关注近壁区域中的粒子优先浓度和偏析:与基于DNS的统计数据进行比较后,我们的结果表明,对于不同的斯托克斯数,EPM中没有SGS模型的LES准确性更高。此外,粒子速度的统计数据可以很好地恢复DNS级别。

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