The effect of timescale variation in multiple mapping conditioning mixing of PDF calculations for Sandia Flame series D–F

The effect of timescale variation in multiple mapping conditioning mixing of PDF calculations for Sandia Flame series D–F
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Sandia Flame 系列 DâF 的 PDF 计算的多重映射条件混合中时间尺度变化的影响

DOI:
10.1080/13647830.2016.1191677
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发表时间:
2016
影响因子:
1.3
通讯作者:
K. Vogiatzaki
K. Vogiatzaki
中科院分区:
工程技术4区
文献类型:
--
作者:
C. Straub;A. Kronenburg;K. Vogiatzaki

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一个随机实施的多映射条件(MMC)模型已被用于一系列的湍流射流扩散火焰与不同程度的局部熄灭(桑迪亚火焰D-F)的燃烧化学相互作用的建模。映射函数近似混合分数的累积概率分布,并且可以通过标量混合时标的标准实现来控制相应的方差。条件波动由较小的耗散时间尺度τmin控制。结果表明,一个明确的依赖性的条件波动的选择的小时标,和适当的值湍流射流火焰是类似的值确定在相关的直接数值模拟(DNS)研究均匀湍流反应流。温度和物种质量分数的平均值和方差的预测是非常好的所有火焰,表明适当的建模的条件方差。粒子数密度的进一步敏感性研究表明,在数值求解过程中的粒子数的结果相对不敏感。良好的结果可以得到与少至10个粒子每个细胞,允许一个计算上便宜的实施的蒙特卡罗/概率密度函数(PDF)方法。
A stochastic implementation of the multiple mapping conditioning (MMC) model has been used for the modelling of turbulence–chemistry interactions in a series of turbulent jet diffusion flames with varying degrees of local extinction (Sandia Flames D–F). The mapping function approximates the cumulative probability distribution of mixture fraction and the corresponding variance can be controlled by a standard implementation of the scalar mixing timescale. The conditional fluctuations are controlled by a minor dissipation timescale, τmin. The results show a clear dependence of the conditional fluctuations on the choice of the minor timescale, and the appropriate value for turbulent jet flames is similar to values determined in related direct numerical simulation (DNS) studies of homogeneous turbulent reacting flows. The predictions of means and variances of temperature and species mass fractions are very good for all flames, indicating an appropriate modelling of the conditional variances. Further sensitivity studies with respect to particle number density demonstrate a relative insensitivity of the results to the particle number in the numerical solution procedure. Good results can be obtained with as few as 10 particles per cell, allowing for a computationally inexpensive implementation of a Monte Carlo/probability density function (PDF) method.
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