This paper presents a new method for modeling rarefied gas flows based on hybridization of direct simulation Monte Carlo (DSMC) and discrete velocity method (DVM)-based quasi-particle representations of the velocity distribution function. It is aimed at improving the resolution of the tails of the distribution function (compared with DSMC) and computational efficiency (compared with DVM). Details of the method, such as the collision algorithm and the particle merging scheme, are discussed. The hybrid approach is applied to the study of noise in a Maxwellian distribution, computation of electron-impact ionization rate coefficient, as well as numerical simulation of a supersonic Couette flow. The hybrid-based solver is compared with pure DSMC and DVM approaches in terms of accuracy, computational speed, and memory use. It is shown that such a hybrid approach can provide a lower computational cost than a pure DVM approach, while being able to retain accuracy in modeling high-velocity tails of the distribution function. For problems where trace species have a significant impact on the flow physics, the proposed method is shown to be capable of providing better computational efficiency and accuracy compared with standard fixed-weight DSMC.

本文提出了一种基于直接模拟蒙特卡洛（DSMC）和基于离散速度法（DVM）的速度分布函数的准粒子表示的混合建模稀有气体流的新方法。它旨在提高分布函数尾部的分辨率（与DSMC相比）和计算效率（与DVM相比）。讨论了该方法的详细信息，例如碰撞算法和粒子合并方案。混合方法用于研究麦克斯韦分布中的噪声，电子碰撞电离率系数的计算以及超音速库埃特流的数值模拟。在准确性，计算速度和内存使用方面，将基于混合的求解器与纯DSMC和DVM方法进行了比较。结果表明，这种混合方法可以提供比纯DVM方法更低的计算成本，同时能够在对分布函数的高速尾部建模时保持精度。对于痕量物质对流动物理有重大影响的问题，与标准的固定重量DSMC相比，所提出的方法显示出能够提供更好的计算效率和准确性。