Mixing Modelling Framework Based on Multiple Mapping Conditioning for the Prediction of Turbulent Flame Extinction
Mixing Modelling Framework Based on Multiple Mapping Conditioning for the Prediction of Turbulent Flame Extinction
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基于多重映射条件的混合建模框架用于预测湍流火焰熄灭
DOI：
10.1007/s10494-015-9626-0
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发表时间：
2015
期刊：
影响因子：
--
通讯作者：
A. Kronenburg
中科院分区：
文献类型：
--
作者：
K. Vogiatzaki;S. Navarro-Martinez;A. Kronenburg
将多重映射条件(MMC)方法的随机实现应用于接近熄灭的湍流引导喷流扩散火焰(Sandia火焰F)。在MMC背景下引入了两个经典的混合模型(Curl‘s和IEM)来模拟湍流混合。所建议的模型涉及使用参考空间(映射到混合分数空间)来定义粒子的接近程度。本文建议在IEM和Curl模型中加入MMC思想，以期在不违反线性和独立性原则的情况下，将这两个模型的简单性与强制成分局部性结合起来。详细讨论了该方法的公式，给出了混合场和反应物种的计算结果。使用相同的混合模型和实验数据，将预测结果与联合标量PDF模拟结果进行了比较。此外，还考察了模型对粒子数的敏感性。结果表明，MMC对粒子数不那么敏感，通常可以用较少的粒子数改进对主要和次要化学反应物种的预测。
A stochastic implementation of the Multiple Mapping Conditioning (MMC) approach has been applied to a turbulent piloted jet diffusion flame (Sandia flame F) that is close to extinction. Two classic mixing models (Curl’s and IEM) are introduced in the MMC context to model the turbulent mixing. The suggested model involves the use of a reference space (that is mapped to mixture fraction space) in order to define particle proximity. The addition of the MMC ideas to the IEM and Curl’s models, that is suggested in the current work, aspires to combine the simplicity of these two models with the enforced compositional locality without violating the linearity and independence principles. The formulation of the approach is discussed in detail and results are presented for the mixing field and reactive species. The predictions are compared with joint-scalar PDF simulations using the same mixing models and experimental data. Moreover, the sensitivity of the model to the particle number is examined. It is shown that MMC is less sensitive to the number of particles and can generally produce improved predictions of major and minor chemically reacting species with a lower number of particles.
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影响因子：
4.4
作者：
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通讯作者：
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影响因子：
3.4
作者：
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DOI：
10.2514/8.1982
发表时间：
1951
期刊：
Journal of the Aeronautical Sciences
影响因子：
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作者：
S. Corrsin
通讯作者：
S. Corrsin
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影响因子：
4.4
作者：
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影响因子：
4.4
作者：
M. Cleary;A. Kronenburg
通讯作者：
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