Particle-in-cell and Monte Carlo collision (PIC-MCC) has been widely adopted as a simulation method for electric propulsion. However, neutral atoms move much more slowly than other species, which can cause a serious reduction in simulation speed. In this work, we investigate the view factor model in combination with the PIC-MCC method and propose a method for simulating three-dimensional neutral atoms. The accuracy of the PIC-MCC method can be significantly improved by updating the neutral distribution periodically. We compare the computational results with the fixed-neutral PIC-MCC model of the miniature ring-cusp discharge experiment at the University of California, Los Angeles (UCLA). The plasma distribution and potential distribution of the simulation match well with the UCLA experimental data. Compared with the fixed-neutral model, the view factor model increases the simulation time by only 33% while it improves the distribution accuracy of neutrals, plasma density, and electric potential, and reduces the simulation errors of discharge current and discharge power from 19.8% to 9.8%. The accuracy of PIC-MCC simulation has been improved at the expense of slightly increasing the computational time.

中文翻译：

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视角因子模型在粒子与蒙特卡洛碰撞代码中的应用

粒子内碰撞和蒙特卡洛碰撞（PIC-MCC）已被广泛用作电推进的模拟方法。但是，中性原子的移动速度比其他物种慢得多，这可能导致模拟速度严重降低。在这项工作中，我们结合PIC-MCC方法研究了视图因子模型，并提出了一种模拟三维中性原子的方法。通过定期更新中性分布，可以显着提高PIC-MCC方法的准确性。我们将计算结果与加利福尼亚大学洛杉矶分校（UCLA）的微型环形尖头放电实验的固定中性PIC-MCC模型进行比较。模拟的等离子体分布和电势分布与UCLA实验数据非常吻合。与固定中立模型相比，视图因子模型仅使仿真时间增加了33％，同时提高了中性点的分布精度，等离子体密度和电势，并将放电电流和放电功率的仿真误差从19.8％降低到9.8％。PIC-MCC仿真的准确性已得到提高，但代价是稍微增加了计算时间。