Particle-based kinetic Monte Carlo simulations of neutral particles is one of the major computational bottlenecks in tokamak scrape-off layer simulations. This computational cost comes from the need to resolve individual collision events in high-collisional regimes. However, in such regimes, one can approximate the high-collisional kinetic dynamics with computationally cheaper diffusion. Asymptotic-preserving schemes make use of this limit to perform simulations in these regimes, without a blow-up in computational cost as incurred by standard kinetic approaches. One such scheme is Kinetic-diffusion Monte Carlo. In this paper, we present a first extension of this scheme to the two-dimensional setting and its implementation in the Eiron particle code. We then demonstrate that this implementation produces a significant speedup over kinetic simulations in high-collisional cases.

翻译：基于粒子的中性粒子动力学蒙特卡洛模拟是托卡马克刮削层模拟中的主要计算瓶颈之一。这种计算成本源于在高碰撞率条件下需要解析单个碰撞事件。然而，在此类条件下，可以用计算成本更低的扩散来近似高碰撞动力学。渐近保持方案利用这一极限进行模拟，避免了标准动力学方法带来的计算成本激增。动力学-扩散蒙特卡洛即是其中一种方案。本文首次将该方案推广至二维场景，并在Eiron粒子程序中实现。我们证明，该实现可在高碰撞率案例中实现显著优于动力学模拟的加速效果。