Effect of multiscalar subfilter PDF models in LES of turbulent flames with inhomogeneous inlets
Effect of multiscalar subfilter PDF models in LES of turbulent flames with inhomogeneous inlets
复制标题
多标量子滤波器 PDF 模型在具有不均匀入口的湍流火焰 LES 中的影响
DOI：
10.1016/j.proci.2018.07.116
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发表时间：
2019
影响因子：
3.4
通讯作者：
M. Mueller
中科院分区：
文献类型：
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作者：
Bruce A. Perry;M. Mueller
为了将降阶流形燃烧模型应用于多进气系统的大涡模拟(LES)，需要包含多个混合分数。因此，必须对混合馏分的联合子过滤器概率密度函数(PDF)进行建模。为了确定预测的湍流火焰结构对这些模型的敏感性，采用Dirichlet、Connor-Mosimann(CM)和Beta-Delta(BD)分布对悉尼引航射流燃烧器进行了大涡模拟计算。应用了一种内存高效的动态卷积方法，以支持使用更复杂的子过滤器PDF模型，如CM分布。在LES预测中观察到对子过滤器PDF模型的选择具有很强的敏感性。Dirichlet分布不适用于这种部分预混的湍流火焰，因为作为子过滤器的PDF模型隐含地假定混合分数空间中的对称混合，这与部分预混是不一致的。CM分布和BD分布与实验数据吻合较好，与部分预混在数学上是一致的。CM分布是所考虑的分布中最一般的分布，并且给出了最好的预测，但这增加了复杂性和计算成本。
To apply reduced-order manifold combustion models in Large Eddy Simulations (LES) of systems with multiple inlets, it is necessary to incorporate more than one mixture fraction. As a result, the joint subfilter Probability Density Function (PDF) of the mixture fractions must be modeled. To determine the sensitivity of the predicted turbulent flame structure to these models, LES calculations of the Sydney piloted jet burner with inhomogeneous inlets have been performed using the Dirichlet, Connor-Mosimann (CM), and Beta-Delta (BD) distributions. A memory-efficient convolution-on-the-fly approach was applied to enable use of more complex subfilter PDF models, such as the CM distribution. Strong sensitivities in the LES predictions were observed to the choice of subfilter PDF model. The Dirichlet distribution was found to be ill-suited to this partially premixed turbulent flame because as a subfilter PDF model it implicitly assumes symmetric mixing in mixture fraction space, which is inconsistent with partial premixing. Good agreement with the experimental data was obtained using both the CM and BD distributions, which are mathematically consistent with partial premixing. The CM distribution is the most general of the considered distributions and gives the best predictions, but this comes with increased complexity and computational cost.