A sparse-Lagrangian multiple mapping conditioning model for turbulent diffusion flames
A sparse-Lagrangian multiple mapping conditioning model for turbulent diffusion flames
复制标题
湍流扩散火焰的稀疏拉格朗日多重映射调节模型
DOI：
10.1016/j.proci.2008.07.015
复制
发表时间：
2009
影响因子：
4.4
通讯作者：
M. Pfitzner
中科院分区：
文献类型：
--
作者：
M. Cleary;A. Klimenko;J. Janicka;M. Pfitzner
本文提出了一种适用于湍流扩散火焰的稀疏拉格朗日多重映射条件（MMC）模型，并以甲烷/空气射流扩散火焰（Sandia Flame D）为例进行了实验验证。该模型结合了大涡模拟的流场和反应标量的随机多重映射调节（MMC）的方法。随机MMC模型的标量组成场的过滤密度函数。数值实现涉及一个稀疏拉格朗日粒子计划，其中有更少的粒子比LES网格单元。预测类似的准确性，以前公布的火焰D模拟实现了只有35,000个粒子（其中只有10,000个是化学活性）。子过滤器条件耗散是由位于参考混合分数空间内插从底层的欧拉滤波场的粒子对之间的相互作用建模。一个模型的混合时间尺度是成比例的混合颗粒之间的距离。结果表明，时间尺度可以调整，以实现良好的预测时间平均的平均值和波动的统计被动和反应标量。
A sparse-Lagrangian multiple mapping conditioning (MMC) model for turbulent diffusion flames is presented and tested against experimental data for a piloted methane/air jet diffusion flame (Sandia Flame D). The model incorporates a large eddy simulation for the flow field and a stochastic multiple mapping conditioning (MMC) approach for the reactive scalars. The stochastic MMC models the filtered density function of the scalar composition field. The numerical implementation involves a sparse-Lagrangian particle scheme in which there are fewer particles than there are LES grid cells. Predictions of similar accuracy to previously published Flame D simulations are achieved using only 35,000 particles (of these only 10,000 are chemically active). Sub-filter conditional dissipation is modelled by interactions between pairs of particles which are closely located in a reference mixture fraction space interpolated from the underlying Eulerian filtered field. A model is developed for the mixing time-scale which is proportional to the distance between mixing particles. It is shown that the time-scale can be adjusted to achieve good predictions for time-averaged mean and fluctuating statistics of passive and reactive scalars.