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Monte-Carlo simulation of colliding particles or coalescing droplets transported by a turbulent flow in the framework of a joint fluid-particle pdf approach

机译:Monte-Carlo在接合流体 - 粒子PDF方法的框架内通过湍流运输的碰撞颗粒或聚结液滴的模拟

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The aim of the paper is to introduce and validate a Monte-Carlo algorithm for the prediction of an ensemble of colliding solid particles, or coalescing liquid droplets, suspended in a turbulent gas flow predicted by Reynolds Averaged Navier Stokes approach (RANS). The new algorithm is based on the direct discretization of the collision/coalescence kernel derived in the framework of a joint fluid-particle pdf approach proposed by Simonin et al. (2002). This approach allows to take into account correlations between colliding inertial particle velocities induced by their interaction with the fluid turbulence. Validation is performed by comparing the Monte-Carlo predictions with deterministic simulations of discrete solid particles coupled with Direct Numerical Simulation (DPS/DNS), or Large Eddy Simulation (DPS/LES), where the collision/coalescence effects are treated in a deterministic way. Five cases are investigated: elastic monodisperse particles, non-elastic monodisperse particles, binary mixture of elastic particles and binary mixture of elastic settling particles in turbulent flow and finally coalescing droplets. The predictions using the new Monte-Carlo algorithm are in much better agreement with DPS/DNS results than the ones using the standard algorithm. (C) 2015 Elsevier Ltd. All rights reserved.
机译:本文的目的是引入和验证用于预测碰撞固体颗粒的整体的蒙特卡罗算法,或者聚结液滴,悬浮在Reynolds的湍流气体流量平均Navier Stokes方法(RANS)中。新算法基于Simonin等人提出的联合流体粒子PDF方法的碰撞/聚结核的直接离散化。 (2002)。该方法允许考虑由其与流体湍流相互作用引起的碰撞惯性粒子速度之间的相关性。通过将Monte-Carlo预测与耦合与直接数值模拟(DPS / DNS)的离散固体颗粒的确定性模拟进行比较来执行验证,或者以确定的方式处理碰撞/聚结效应的大涡仿真(DPS / LES) 。研究了五种病例:弹性单分散颗粒,非弹性单分散颗粒,弹性颗粒的二元混合物和弹性沉降颗粒的二元混合物在湍流中,最后聚结液滴。使用新的Monte-Carlo算法的预测与使用标准算法的DPS / DNS结果有更好的协议。 (c)2015 Elsevier Ltd.保留所有权利。

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