A particle-based ellipsoidal statistical Bhatnagar–Gross–Krook solver with variable weights for the simulation of large density gradients in micro- and nano-nozzles
A particle-based ellipsoidal statistical Bhatnagar–Gross–Krook solver with variable weights for the simulation of large density gradients in micro- and nano-nozzles
复制标题
DOI：
10.1063/5.0023905
复制
发表时间：
2020-11
影响因子：
4.6
通讯作者：
M. Pfeiffer
中科院分区：
文献类型：
--
作者：
M. Pfeiffer
本文证明了一个改进的粒子为基础的椭球统计Bhatnagar-Gross-Krook（ESBGK）求解器模拟微喷管的效率。为此，公共粒子ESBGK算法适于处理可变粒子权重，包括在具有低统计样本的区域中创建附加粒子以及在密集区域中合并粒子。在描述了这些方法及其实现之后，比较了直接模拟蒙特卡罗方法、普通粒子ESBGK方法和改进的ESBGK方法对微喷管几何形状的模拟结果，并从精度和效率两个方面进行了比较。所有三种方法都表现出良好的一致性;然而，修改后的ESBGK方法具有最高的效率，节省了约500倍的计算时间，以在稀疏扩展区域产生可比的统计样本量。
This paper demonstrates the efficiency of a modified particle based Ellipsoidal Statistical Bhatnagar–Gross–Krook (ESBGK) solver to simulate micro-nozzles. For this, the common particle ESBGK algorithm is adapted to handle variable particle weights including the creation of additional particles in regions with low statistical samples and merging of particles in dense regions. After the description of the methods and their implementation, the simulation results of a micro-nozzle geometry using the Direct Simulation Monte Carlo, the common particle ESBGK, and the proposed modified ESBGK method are compared concerning accuracy and efficiency. All three methods show good agreement; however, the modified ESBGK method has the highest efficiency, saving a factor of around 500 of computational time to produce a comparable statistical sample size in the rarefied expansion region.