MMC-LES modelling of droplet nucleation and growth in turbulent jets
MMC-LES modelling of droplet nucleation and growth in turbulent jets
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DOI：
10.1016/j.ces.2017.04.008
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发表时间：
2017-08
影响因子：
4.7
通讯作者：
G. Neuber;A. Kronenburg;O. Stein;M. Cleary
中科院分区：
文献类型：
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作者：
G. Neuber;A. Kronenburg;O. Stein;M. Cleary
采用欧拉大涡模拟（LES）和稀疏拉格朗日粒子法相结合的方法求解湍流气溶胶成核和生长的粒子数平衡方程。我们使用LES的过滤速度和混合场的解决方案，而粒子的方法提供了一个点的统计数据的气体物种和凝聚相，这样的非线性气溶胶成核和生长项自然出现在封闭的形式。稀疏粒子的实现需要在参考空间中进行额外的本地化，而这种本地化是通过采用广义多映射条件（MMC）混合模型来实现的。完整的模型，被称为MMC-LES，通过比较与实验数据的成核研究在湍流，热，氮气射流满载与邻苯二甲酸二丁酯（DBP），冷凝过程中与冷空气的coflow混合。可以接受的协议之间的MMC-LES的预测和实验数据。DBP液滴的平均尺寸被很好地预测，总液滴数和液滴统计对前体浓度的依赖性的预测是令人满意的。稀疏粒子方法与传统（密集）蒙特卡罗LES模拟结果的比较表明，MMC-LES预测气溶胶成核和增长的能力，在相对较低的计算成本。湍流和非线性成核源和生长项之间的相互作用的一个额外的定量分析表明，亚网格效应不能被忽略，湍流和成核之间的相互作用可以修改平均成核率超过250%。
An Eulerian large-eddy simulation (LES) is coupled with a sparse-Lagrangian particle method to solve the population balance equation for aersol nucleation and growth in turbulent flows. We use the LES for the solution of the filtered velocity and mixing fields while the particle method provides one-point statistics of the gaseous species and the condensed phase such that the non-linear aerosol nucleation and growth terms appear naturally in closed form. A sparse particle implementation requires additional localisation in a reference space, and this localisation is realised here by employing the generalised multiple mapping conditioning (MMC) mixing model. The complete model, called MMC-LES, is validated by comparison with experimental data from nucleation studies in a turbulent, hot, nitrogen jet laden with dibutyl-phthalate (DBP) that condenses during mixing with a coflow of cold air. Acceptable agreement is found between the MMC-LES predictions and the experimental data. The average DBP droplet sizes are well predicted, and predictions of the total droplet number and the dependencies of droplet statistics on precursor concentrations are satisfactory. A comparison of the sparse particle method with results from conventional (dense) Monte Carlo-LES simulations demonstrates the capabilities of MMC-LES to predict aerosol nucleation and growth at relatively low computational cost. An additional quantitative analysis of the interactions between the turbulence and the non-linear nucleation source and growth terms shows that sub-grid effects must not be neglected and interactions between turbulence and nucleation can modify averaged nucleation rates by more than 250%.