A Generalised Multiple Mapping Conditioning Approach for Turbulent Combustion
A Generalised Multiple Mapping Conditioning Approach for Turbulent Combustion
复制标题
湍流燃烧的广义多重映射调节方法
DOI：
--
复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
A. Klimenko
中科院分区：
文献类型：
--
作者：
M. Cleary;A. Klimenko
本文跟踪了对湍流燃烧的多重映射条件(MMC)方法的理解的发展，并回顾了MMC模型的不同实现。正如MMC的名字所暗示的，原始版本表示CMC类型的条件方程(条件矩闭包)和广义映射闭包的一致组合。看来，MMC模型的优势，尤其是其随机版本的优势，在于更一般(也更透明)的解释。在这种新的泛化解释中，我们可以用物理推理取代复杂的推导，该模型似乎是近几十年来发展的建模方法的自然延伸。MMC可以被看作是一种在传统混合模型上强制某些已知湍流特征的方法。这是通过在参考空间中定位混合操作来实现的。选择参考空间变量来模拟对反应量有很大影响的湍流的特性。最好和最简单的例子是MMC模型，它只有一个参考变量来模拟混合分数。在扩散火焰中，反应量的湍流涨落与混合物分数的涨落密切相关。通过使混合在参考混合分数空间中成为局部的，实现了CMC类型的混合闭包。在MMC的原始解释中，参考变量被建模为马尔可夫过程。由于参考变量应尽可能真实地模拟湍流的特性，因此下一步和推广的MMC的基础是去除马尔可夫限制，并将参考变量设置为等于在DNS或LES流场中跟踪的拉格朗日量。事实上，没有哪个马尔可夫值能比混合分数本身更好地模拟混合分数。(使用维度高于条件变量数量的马尔可夫向量过程，代表了在广义MMC中产生参考变量的更经济的替代方案。)推广的MMC方法有效地将基于混合分数的模型、PDF方法和LES/DNS技术整合到单一方法中，并有可能混合以前为传统模型开发的有用特征。MMC的一般方法促进了对模拟的更灵活的理解，使用稀疏放置的拉格朗日粒子作为工具，可以以相对较低的计算成本提供反应标量的准确联合分布。对部分预混甲烷/空气扩散火焰(Sandia Flame，D)的实例计算支持了MMC新解释背后的物理推理。该格式对动态场采用大涡模拟，对标量场采用稀疏-拉格朗日滤波密度函数方法和MMC混合。测试了两种不同的粒子混合方案。在单个工作站上仅使用35,000个拉格朗日粒子(其中只有10,000个粒子具有化学活性)进行模拟。相对较低的计算成本允许使用包含34个活性物种和219个反应的真实化学动力学。
This paper follows the evolution in understanding of the multiple mapping conditioning (MMC) approach for turbulent combustion and reviews different implementations of MMC models. As the MMC name suggests, the original version represents a consistent combination of CMC-type conditional equations (conditional moment closure) and generalised mapping closure. It seems that the strength of the MMC model, and especially that of its stochastic version, lies in a more general (and much more transparent) interpretation. In this new generalised interpretation, we can replace complicated derivations by physical reasoning and the model appears to be a natural extension of modelling approaches developed in recent decades. MMC can be seen as a methodology for enforcing certain known characteristics of turbulence on a conventional mixing model. This is achieved by localising the mixing operation in a reference space. The reference space variables are selected to emulate the properties of a turbulent flow which have a strong effect on reactive quantities. The best and simplest example is an MMC model which has a single reference variable emulating the mixture fraction. In diffusion flames turbulent fluctuations of reacting quantities are strongly correlated with fluctuations of the mixture fraction. By making mixing local in the reference mixture fraction space a CMC-type mixing closure is enforced. In the original interpretation of MMC the reference variables are modelled as Markov processes. Since the reference variables should emulate properties of turbulent flows as realistically as possible the next step, and the basis of generalised MMC, is to remove the Markovian restriction and set reference variables equal to traced Lagrangian quantities within DNS or LES flow fields. Indeed, no Markov value can emulate the mixture fraction better than the mixture fraction itself. (Using a Markov vector process of dimension higher than the number of conditioning variables represents a more economical alternative for producing reference variables in generalised MMC.) The generalised MMC approach effectively incorporates the mixture fraction-based models, the PDF methods and LES/DNS techniques into a single methodology with possibility of blending useful features developed previously for conventional models. The generalised approach to MMC stimulates a more flexible understanding of simulations using sparsely placed Lagrangian particles as tools that may provide accurate joint distributions of reactive scalars at relatively low computational cost. The physical reasoning behind the new interpretation of MMC is supported by example computations for a partially premixed methane/air diffusion flame (Sandia Flame D). The scheme utilises LES for the dynamic field and a sparse-Lagrangian filtered density function method with MMC mixing for the scalar field. Two different particle mixing schemes are tested. Simulations are performed using only 35,000 Lagrangian particles (of these only 10,000 are chemically active) on a single workstation. The relatively low computational cost allows the use of realistic chemical kinetics containing 34 reactive species and 219 reactions.