Purpose

The purpose of this paper is to study the soot formation and evolution by using this newly developed Lagrangian particle tracking with weighted fraction Monte Carlo (LPT-WFMC) method.

Design/methodology/approach

The weighted soot particles are used in this MC framework and is tracked using Lagrangian approach. A detailed soot model based on the LPT-WFMC method is used to study the soot formation and evolution in ethylene laminar premixed flames.

Findings

The LPT-WFMC method is validated by both experimental and numerical results of the direct simulation Monte Carlo (DSMC) and Multi-Monte Carlo (MMC) methods. Compared with DSMC and MMC methods, the stochastic error analysis shows this new LPT-WFMC method could further extend the particle size distributions (PSDs) and improve the accuracy for predicting soot PSDs at larger particle size regime.

Originality/value

Compared with conventional weighted particle schemes, the weight distributions in LPT-WFMC method are adjustable by adopting different fraction functions. As a result, the number of numerical soot particles in each size interval could be also adjustable. The stochastic error of PSDs in larger particle size regime can also be minimized by increasing the number of numerical soot particles at larger size interval.

中文翻译：

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使用新的加权分数蒙特卡罗方法进行拉格朗日粒子跟踪，用于研究预混火焰中的烟尘粒度分布

目的

本文的目的是通过使用这种新开发的带加权分数蒙特卡罗 (LPT-WFMC) 方法的拉格朗日粒子跟踪来研究烟尘的形成和演化。

设计/方法/方法

在此 MC 框架中使用加权烟尘粒子，并使用拉格朗日方法进行跟踪。基于 LPT-WFMC 方法的详细烟灰模型用于研究乙烯层流预混火焰中烟灰的形成和演化。

发现

LPT-WFMC 方法通过直接模拟蒙特卡罗 (DSMC) 和多蒙特卡罗 (MMC) 方法的实验和数值结果得到验证。与 DSMC 和 MMC 方法相比，随机误差分析表明，这种新的 LPT-WFMC 方法可以进一步扩展粒度分布 (PSD)，并提高在较大粒度范围内预测烟尘 PSD 的准确性。

原创性/价值

与传统的加权粒子方案相比，LPT-WFMC方法中的权重分布可以通过采用不同的分数函数进行调整。因此，每个尺寸区间的烟灰颗粒数量也可以调整。通过增加较大尺寸区间的数值烟尘颗粒的数量，也可以最小化较大颗粒尺寸范围内 PSD 的随机误差。