A sparse-Lagrangian particle implementation of the multiple mapping conditioning / large-eddy simulation (MMC-LES) model for two-phase flows is developed with the aid of carrier-phase direct numerical simulation (CP-DNS) of a droplet-laden temporally evolving reacting double shear layer. In sparse-Lagrangian MMC for spray combustion, the liquid fuel droplets are represented by a first set of Lagrangian particles, while the reacting gas phase is described by a second set of (stochastic) particles. The two phases exchange heat and mass and a one-to-one coupling technique of liquid and gas particles with particle pair selection conditional on mixture fraction and/or temperature is introduced. The simulations show that unconditional mean and rms of mixture fraction are accurately reproduced by MMC and are largely independent of the particle coupling. Temperature, however, is significantly underpredicted when using the Eulerian mixture fraction for particle selection as is conventional for particle-particle selection in MMC. This is due to a lack of correlation between temperature and mixture fraction caused by excessive cooling of selected fluid elements due to evaporation and subsequent local flame extinction and reignition. Conditioning on temperature leads to mild improvements but does not prevent a decorrelation of the temperatures “seen” by the droplets. Alternatively, a minimization based on stochastic particle temperature is suggested. This leads to good agreement between our MMC-LES model and the CP-DNS. The comparison between the CP-DNS data and MMC-LES also confirms that the anisotropic mixing time scale derived for gaseous flows ensures an accurate degree of micro-mixing also for two-phase flows and provides very good predictions of conditional fluctuations of reacting scalars around the conditional means.

借助载流液滴的载流相直接数值模拟（CP-DNS），开发了两相流多重映射条件/大涡模拟（MMC-LES）模型的稀疏-拉格朗日粒子实现不断发展的反应性双剪切层。在用于喷雾燃烧的稀疏-拉格朗日MMC中，液体燃料滴由第一组拉格朗日粒子表示，而反应气相由第二组（随机）粒子表示。引入了两相进行热量和质量交换，并引入了液体和气体颗粒一对一的耦合技术，其中颗粒对的选择取决于混合物的分数和/或温度。仿真表明，MMC可以准确地再现无条件的均值和均方根均方根值，并且在很大程度上与粒子耦合无关。但是，当使用Eulerian混合级分进行颗粒选择时，温度会大大低估，就像在MMC中进行颗粒选择一样。这是由于由于蒸发以及随后的局部火焰熄灭和重燃导致所选流体元件过度冷却而导致的温度与混合物分数之间缺乏相关性。对温度的调节会导致温和的改善，但不会阻止液滴“看到”的温度去相关。或者，建议基于随机粒子温度的最小化。这使我们的MMC-LES模型与CP-DNS之间达成了良好的协议。